diff --git "a/arxiv_papers_for_human_review.csv" "b/arxiv_papers_for_human_review.csv" new file mode 100644--- /dev/null +++ "b/arxiv_papers_for_human_review.csv" @@ -0,0 +1,30294 @@ +title,firstAuthor,url,dateSubmitted,keywords,pdf_titles,abstract +"""Do Anything Now"": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models",Xinyue Shen,http://arxiv.org/pdf/2308.03825v1.pdf,2023-08-07,"['cs.cr', 'cs.lg']",2308.03825v1.pdf," The misuse of large language models (LLMs) has garnered significant attention +from the general public and LLM vendors. In response, efforts have been made to +align LLMs with human values and intent use. However, a particular type of +adversarial prompts, known as jailbreak prompt, has emerged and continuously +evolved to bypass the safeguards and elicit harmful content from LLMs. In this +paper, we conduct the first measurement study on jailbreak prompts in the wild, +with 6,387 prompts collected from four platforms over six months. Leveraging +natural language processing technologies and graph-based community detection +methods, we discover unique characteristics of jailbreak prompts and their +major attack strategies, such as prompt injection and privilege escalation. We +also observe that jailbreak prompts increasingly shift from public platforms to +private ones, posing new challenges for LLM vendors in proactive detection. To +assess the potential harm caused by jailbreak prompts, we create a question set +comprising 46,800 samples across 13 forbidden scenarios. Our experiments show +that current LLMs and safeguards cannot adequately defend jailbreak prompts in +all scenarios. Particularly, we identify two highly effective jailbreak prompts +which achieve 0.99 attack success rates on ChatGPT (GPT-3.5) and GPT-4, and +they have persisted online for over 100 days. Our work sheds light on the +severe and evolving threat landscape of jailbreak prompts. We hope our study +can facilitate the research community and LLM vendors in promoting safer and +regulated LLMs. +" +Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study,Yi Liu,http://arxiv.org/pdf/2305.13860v1.pdf,2023-05-23,"['cs.se', 'cs.ai', 'cs.cl']",2305.13860v1.pdf," Large Language Models (LLMs), like ChatGPT, have demonstrated vast potential +but also introduce challenges related to content constraints and potential +misuse. Our study investigates three key research questions: (1) the number of +different prompt types that can jailbreak LLMs, (2) the effectiveness of +jailbreak prompts in circumventing LLM constraints, and (3) the resilience of +ChatGPT against these jailbreak prompts. Initially, we develop a classification +model to analyze the distribution of existing prompts, identifying ten distinct +patterns and three categories of jailbreak prompts. Subsequently, we assess the +jailbreak capability of prompts with ChatGPT versions 3.5 and 4.0, utilizing a +dataset of 3,120 jailbreak questions across eight prohibited scenarios. +Finally, we evaluate the resistance of ChatGPT against jailbreak prompts, +finding that the prompts can consistently evade the restrictions in 40 use-case +scenarios. The study underscores the importance of prompt structures in +jailbreaking LLMs and discusses the challenges of robust jailbreak prompt +generation and prevention. +" +AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models,Xiaogeng Liu,http://arxiv.org/pdf/2310.04451v1.pdf,2023-10-03,"['cs.cl', 'cs.ai']",2310.04451v1.pdf," The aligned Large Language Models (LLMs) are powerful language understanding +and decision-making tools that are created through extensive alignment with +human feedback. However, these large models remain susceptible to jailbreak +attacks, where adversaries manipulate prompts to elicit malicious outputs that +should not be given by aligned LLMs. Investigating jailbreak prompts can lead +us to delve into the limitations of LLMs and further guide us to secure them. +Unfortunately, existing jailbreak techniques suffer from either (1) scalability +issues, where attacks heavily rely on manual crafting of prompts, or (2) +stealthiness problems, as attacks depend on token-based algorithms to generate +prompts that are often semantically meaningless, making them susceptible to +detection through basic perplexity testing. In light of these challenges, we +intend to answer this question: Can we develop an approach that can +automatically generate stealthy jailbreak prompts? In this paper, we introduce +AutoDAN, a novel jailbreak attack against aligned LLMs. AutoDAN can +automatically generate stealthy jailbreak prompts by the carefully designed +hierarchical genetic algorithm. Extensive evaluations demonstrate that AutoDAN +not only automates the process while preserving semantic meaningfulness, but +also demonstrates superior attack strength in cross-model transferability, and +cross-sample universality compared with the baseline. Moreover, we also compare +AutoDAN with perplexity-based defense methods and show that AutoDAN can bypass +them effectively. +" +Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM,Bochuan Cao,http://arxiv.org/pdf/2309.14348v1.pdf,2023-09-18,"['cs.cl', 'cs.ai', 'cs.cr', 'cs.lg']",2309.14348v1.pdf," Recently, Large Language Models (LLMs) have made significant advancements and +are now widely used across various domains. Unfortunately, there has been a +rising concern that LLMs can be misused to generate harmful or malicious +content. Though a line of research has focused on aligning LLMs with human +values and preventing them from producing inappropriate content, such +alignments are usually vulnerable and can be bypassed by alignment-breaking +attacks via adversarially optimized or handcrafted jailbreaking prompts. In +this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against +potential alignment-breaking attacks. RA-LLM can be directly constructed upon +an existing aligned LLM with a robust alignment checking function, without +requiring any expensive retraining or fine-tuning process of the original LLM. +Furthermore, we also provide a theoretical analysis for RA-LLM to verify its +effectiveness in defending against alignment-breaking attacks. Through +real-world experiments on open-source large language models, we demonstrate +that RA-LLM can successfully defend against both state-of-the-art adversarial +prompts and popular handcrafted jailbreaking prompts by reducing their attack +success rates from nearly 100\% to around 10\% or less. +" +FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models,Dongyu Yao,http://arxiv.org/pdf/2309.05274v1.pdf,2023-09-11,['cs.cr'],2309.05274v1.pdf," Jailbreak vulnerabilities in Large Language Models (LLMs), which exploit +meticulously crafted prompts to elicit content that violates service +guidelines, have captured the attention of research communities. While model +owners can defend against individual jailbreak prompts through safety training +strategies, this relatively passive approach struggles to handle the broader +category of similar jailbreaks. To tackle this issue, we introduce FuzzLLM, an +automated fuzzing framework designed to proactively test and discover jailbreak +vulnerabilities in LLMs. We utilize templates to capture the structural +integrity of a prompt and isolate key features of a jailbreak class as +constraints. By integrating different base classes into powerful combo attacks +and varying the elements of constraints and prohibited questions, FuzzLLM +enables efficient testing with reduced manual effort. Extensive experiments +demonstrate FuzzLLM's effectiveness and comprehensiveness in vulnerability +discovery across various LLMs. +" +Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation,Rusheb Shah,http://arxiv.org/pdf/2311.03348v1.pdf,2023-11-06,"['cs.cl', 'cs.ai', 'cs.lg']",2311.03348v1.pdf," Despite efforts to align large language models to produce harmless responses, +they are still vulnerable to jailbreak prompts that elicit unrestricted +behaviour. In this work, we investigate persona modulation as a black-box +jailbreaking method to steer a target model to take on personalities that are +willing to comply with harmful instructions. Rather than manually crafting +prompts for each persona, we automate the generation of jailbreaks using a +language model assistant. We demonstrate a range of harmful completions made +possible by persona modulation, including detailed instructions for +synthesising methamphetamine, building a bomb, and laundering money. These +automated attacks achieve a harmful completion rate of 42.5% in GPT-4, which is +185 times larger than before modulation (0.23%). These prompts also transfer to +Claude 2 and Vicuna with harmful completion rates of 61.0% and 35.9%, +respectively. Our work reveals yet another vulnerability in commercial large +language models and highlights the need for more comprehensive safeguards. +" +Latent Jailbreak: A Benchmark for Evaluating Text Safety and Output Robustness of Large Language Models,Huachuan Qiu,http://arxiv.org/pdf/2307.08487v3.pdf,2023-07-17,['cs.cl'],2307.08487v3.pdf," Considerable research efforts have been devoted to ensuring that large +language models (LLMs) align with human values and generate safe text. However, +an excessive focus on sensitivity to certain topics can compromise the model's +robustness in following instructions, thereby impacting its overall performance +in completing tasks. Previous benchmarks for jailbreaking LLMs have primarily +focused on evaluating the safety of the models without considering their +robustness. In this paper, we propose a benchmark that assesses both the safety +and robustness of LLMs, emphasizing the need for a balanced approach. To +comprehensively study text safety and output robustness, we introduce a latent +jailbreak prompt dataset, each involving malicious instruction embedding. +Specifically, we instruct the model to complete a regular task, such as +translation, with the text to be translated containing malicious instructions. +To further analyze safety and robustness, we design a hierarchical annotation +framework. We present a systematic analysis of the safety and robustness of +LLMs regarding the position of explicit normal instructions, word replacements +(verbs in explicit normal instructions, target groups in malicious +instructions, cue words for explicit normal instructions), and instruction +replacements (different explicit normal instructions). Our results demonstrate +that current LLMs not only prioritize certain instruction verbs but also +exhibit varying jailbreak rates for different instruction verbs in explicit +normal instructions. Code and data are available at +https://github.com/qiuhuachuan/latent-jailbreak. +" +MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots,Gelei Deng,http://arxiv.org/pdf/2307.08715v2.pdf,2023-07-16,['cs.cr'],2307.08715v2.pdf," Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI) +services due to their exceptional proficiency in understanding and generating +human-like text. LLM chatbots, in particular, have seen widespread adoption, +transforming human-machine interactions. However, these LLM chatbots are +susceptible to ""jailbreak"" attacks, where malicious users manipulate prompts to +elicit inappropriate or sensitive responses, contravening service policies. +Despite existing attempts to mitigate such threats, our research reveals a +substantial gap in our understanding of these vulnerabilities, largely due to +the undisclosed defensive measures implemented by LLM service providers. + In this paper, we present Jailbreaker, a comprehensive framework that offers +an in-depth understanding of jailbreak attacks and countermeasures. Our work +makes a dual contribution. First, we propose an innovative methodology inspired +by time-based SQL injection techniques to reverse-engineer the defensive +strategies of prominent LLM chatbots, such as ChatGPT, Bard, and Bing Chat. +This time-sensitive approach uncovers intricate details about these services' +defenses, facilitating a proof-of-concept attack that successfully bypasses +their mechanisms. Second, we introduce an automatic generation method for +jailbreak prompts. Leveraging a fine-tuned LLM, we validate the potential of +automated jailbreak generation across various commercial LLM chatbots. Our +method achieves a promising average success rate of 21.58%, significantly +outperforming the effectiveness of existing techniques. We have responsibly +disclosed our findings to the concerned service providers, underscoring the +urgent need for more robust defenses. Jailbreaker thus marks a significant step +towards understanding and mitigating jailbreak threats in the realm of LLM +chatbots. +" +Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions,Wesley Tann,http://arxiv.org/pdf/2308.10443v1.pdf,2023-08-21,"['cs.ai', 'cs.cl', 'cs.cy']",2308.10443v1.pdf," The assessment of cybersecurity Capture-The-Flag (CTF) exercises involves +participants finding text strings or ``flags'' by exploiting system +vulnerabilities. Large Language Models (LLMs) are natural-language models +trained on vast amounts of words to understand and generate text; they can +perform well on many CTF challenges. Such LLMs are freely available to +students. In the context of CTF exercises in the classroom, this raises +concerns about academic integrity. Educators must understand LLMs' capabilities +to modify their teaching to accommodate generative AI assistance. This research +investigates the effectiveness of LLMs, particularly in the realm of CTF +challenges and questions. Here we evaluate three popular LLMs, OpenAI ChatGPT, +Google Bard, and Microsoft Bing. First, we assess the LLMs' question-answering +performance on five Cisco certifications with varying difficulty levels. Next, +we qualitatively study the LLMs' abilities in solving CTF challenges to +understand their limitations. We report on the experience of using the LLMs for +seven test cases in all five types of CTF challenges. In addition, we +demonstrate how jailbreak prompts can bypass and break LLMs' ethical +safeguards. The paper concludes by discussing LLM's impact on CTF exercises and +its implications. +" +Baseline Defenses for Adversarial Attacks Against Aligned Language Models,Neel Jain,http://arxiv.org/pdf/2309.00614v2.pdf,2023-09-01,"['cs.lg', 'cs.cl', 'cs.cr']",2309.00614v2.pdf," As Large Language Models quickly become ubiquitous, it becomes critical to +understand their security vulnerabilities. Recent work shows that text +optimizers can produce jailbreaking prompts that bypass moderation and +alignment. Drawing from the rich body of work on adversarial machine learning, +we approach these attacks with three questions: What threat models are +practically useful in this domain? How do baseline defense techniques perform +in this new domain? How does LLM security differ from computer vision? + We evaluate several baseline defense strategies against leading adversarial +attacks on LLMs, discussing the various settings in which each is feasible and +effective. Particularly, we look at three types of defenses: detection +(perplexity based), input preprocessing (paraphrase and retokenization), and +adversarial training. We discuss white-box and gray-box settings and discuss +the robustness-performance trade-off for each of the defenses considered. We +find that the weakness of existing discrete optimizers for text, combined with +the relatively high costs of optimization, makes standard adaptive attacks more +challenging for LLMs. Future research will be needed to uncover whether more +powerful optimizers can be developed, or whether the strength of filtering and +preprocessing defenses is greater in the LLMs domain than it has been in +computer vision. +" +GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts,Jiahao Yu,http://arxiv.org/pdf/2309.10253v2.pdf,2023-09-19,['cs.ai'],2309.10253v2.pdf," Large language models (LLMs) have recently experienced tremendous popularity +and are widely used from casual conversations to AI-driven programming. +However, despite their considerable success, LLMs are not entirely reliable and +can give detailed guidance on how to conduct harmful or illegal activities. +While safety measures can reduce the risk of such outputs, adversarial +jailbreak attacks can still exploit LLMs to produce harmful content. These +jailbreak templates are typically manually crafted, making large-scale testing +challenging. + In this paper, we introduce GPTFuzz, a novel black-box jailbreak fuzzing +framework inspired by the AFL fuzzing framework. Instead of manual engineering, +GPTFuzz automates the generation of jailbreak templates for red-teaming LLMs. +At its core, GPTFuzz starts with human-written templates as initial seeds, then +mutates them to produce new templates. We detail three key components of +GPTFuzz: a seed selection strategy for balancing efficiency and variability, +mutate operators for creating semantically equivalent or similar sentences, and +a judgment model to assess the success of a jailbreak attack. + We evaluate GPTFuzz against various commercial and open-source LLMs, +including ChatGPT, LLaMa-2, and Vicuna, under diverse attack scenarios. Our +results indicate that GPTFuzz consistently produces jailbreak templates with a +high success rate, surpassing human-crafted templates. Remarkably, GPTFuzz +achieves over 90% attack success rates against ChatGPT and Llama-2 models, even +with suboptimal initial seed templates. We anticipate that GPTFuzz will be +instrumental for researchers and practitioners in examining LLM robustness and +will encourage further exploration into enhancing LLM safety. +" +Probing LLMs for hate speech detection: strengths and vulnerabilities,Sarthak Roy,http://arxiv.org/pdf/2310.12860v2.pdf,2023-10-19,"['cs.cl', 'cs.cy']",2310.12860v2.pdf," Recently efforts have been made by social media platforms as well as +researchers to detect hateful or toxic language using large language models. +However, none of these works aim to use explanation, additional context and +victim community information in the detection process. We utilise different +prompt variation, input information and evaluate large language models in zero +shot setting (without adding any in-context examples). We select three large +language models (GPT-3.5, text-davinci and Flan-T5) and three datasets - +HateXplain, implicit hate and ToxicSpans. We find that on average including the +target information in the pipeline improves the model performance substantially +(~20-30%) over the baseline across the datasets. There is also a considerable +effect of adding the rationales/explanations into the pipeline (~10-20%) over +the baseline across the datasets. In addition, we further provide a typology of +the error cases where these large language models fail to (i) classify and (ii) +explain the reason for the decisions they take. Such vulnerable points +automatically constitute 'jailbreak' prompts for these models and industry +scale safeguard techniques need to be developed to make the models robust +against such prompts. +" +Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction,Martin Josifoski,http://arxiv.org/pdf/2303.04132v2.pdf,2023-03-07,"['cs.cl', 'cs.ai', 'cs.lg']",2303.04132v2.pdf," Large language models (LLMs) have great potential for synthetic data +generation. This work shows that useful data can be synthetically generated +even for tasks that cannot be solved directly by LLMs: for problems with +structured outputs, it is possible to prompt an LLM to perform the task in the +reverse direction, by generating plausible input text for a target output +structure. Leveraging this asymmetry in task difficulty makes it possible to +produce large-scale, high-quality data for complex tasks. We demonstrate the +effectiveness of this approach on closed information extraction, where +collecting ground-truth data is challenging, and no satisfactory dataset exists +to date. We synthetically generate a dataset of 1.8M data points, establish its +superior quality compared to existing datasets in a human evaluation, and use +it to finetune small models (220M and 770M parameters), termed SynthIE, that +outperform the prior state of the art (with equal model size) by a substantial +margin of 57 absolute points in micro-F1 and 79 points in macro-F1. Code, data, +and models are available at https://github.com/epfl-dlab/SynthIE. +" +Small Language Models Improve Giants by Rewriting Their Outputs,Giorgos Vernikos,http://arxiv.org/pdf/2305.13514v1.pdf,2023-05-22,"['cs.cl', 'cs.lg']",2305.13514v1.pdf," Large language models (LLMs) have demonstrated impressive few-shot learning +capabilities, but they often underperform compared to fine-tuned models on +challenging tasks. Furthermore, their large size and restricted access only +through APIs make task-specific fine-tuning impractical. Moreover, LLMs are +sensitive to different aspects of prompts (e.g., the selection and order of +demonstrations) and can thus require time-consuming prompt engineering. In this +light, we propose a method to correct LLM outputs without relying on their +weights. First, we generate a pool of candidates by few-shot prompting an LLM. +Second, we refine the LLM-generated outputs using a smaller model, the +LM-corrector (LMCor), which is trained to rank, combine and rewrite the +candidates to produce the final target output. Our experiments demonstrate that +even a small LMCor model (250M) substantially improves the few-shot performance +of LLMs (62B) across diverse tasks. Moreover, we illustrate that the LMCor +exhibits robustness against different prompts, thereby minimizing the need for +extensive prompt engineering. Finally, we showcase that the LMCor can be +seamlessly integrated with different LLMs at inference time, serving as a +plug-and-play module to improve their performance. +" +Aligning Language Models to User Opinions,EunJeong Hwang,http://arxiv.org/pdf/2305.14929v1.pdf,2023-05-24,['cs.cl'],2305.14929v1.pdf," An important aspect of developing LLMs that interact with humans is to align +models' behavior to their users. It is possible to prompt an LLM into behaving +as a certain persona, especially a user group or ideological persona the model +captured during its pertaining stage. But, how to best align an LLM with a +specific user and not a demographic or ideological group remains an open +question. Mining public opinion surveys (by Pew Research), we find that the +opinions of a user and their demographics and ideologies are not mutual +predictors. We use this insight to align LLMs by modeling both user opinions as +well as user demographics and ideology, achieving up to 7 points accuracy gains +in predicting public opinions from survey questions across a broad set of +topics. In addition to the typical approach of prompting LLMs with demographics +and ideology, we discover that utilizing the most relevant past opinions from +individual users enables the model to predict user opinions more accurately. +" +Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models,Myra Cheng,http://arxiv.org/pdf/2305.18189v1.pdf,2023-05-29,"['cs.cl', 'cs.ai', 'cs.cy']",2305.18189v1.pdf," To recognize and mitigate harms from large language models (LLMs), we need to +understand the prevalence and nuances of stereotypes in LLM outputs. Toward +this end, we present Marked Personas, a prompt-based method to measure +stereotypes in LLMs for intersectional demographic groups without any lexicon +or data labeling. Grounded in the sociolinguistic concept of markedness (which +characterizes explicitly linguistically marked categories versus unmarked +defaults), our proposed method is twofold: 1) prompting an LLM to generate +personas, i.e., natural language descriptions, of the target demographic group +alongside personas of unmarked, default groups; 2) identifying the words that +significantly distinguish personas of the target group from corresponding +unmarked ones. We find that the portrayals generated by GPT-3.5 and GPT-4 +contain higher rates of racial stereotypes than human-written portrayals using +the same prompts. The words distinguishing personas of marked (non-white, +non-male) groups reflect patterns of othering and exoticizing these +demographics. An intersectional lens further reveals tropes that dominate +portrayals of marginalized groups, such as tropicalism and the +hypersexualization of minoritized women. These representational harms have +concerning implications for downstream applications like story generation. +" +Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models,Levon Haroutunian,http://arxiv.org/pdf/2309.12294v1.pdf,2023-09-21,['cs.cl'],2309.12294v1.pdf," Large language models (LLMs) have demonstrated impressive capabilities in +natural language generation. However, their output quality can be inconsistent, +posing challenges for generating natural language from logical forms (LFs). +This task requires the generated outputs to embody the exact semantics of LFs, +without missing any LF semantics or creating any hallucinations. In this work, +we tackle this issue by proposing a novel generate-and-rerank approach. Our +approach involves initially generating a set of candidate outputs by prompting +an LLM and subsequently reranking them using a task-specific reranker model. In +addition, we curate a manually collected dataset to evaluate the alignment +between different ranking metrics and human judgements. The chosen ranking +metrics are utilized to enhance the training and evaluation of the reranker +model. By conducting extensive experiments on three diverse datasets, we +demonstrate that the candidates selected by our reranker outperform those +selected by baseline methods in terms of semantic consistency and fluency, as +measured by three comprehensive metrics. Our findings provide strong evidence +for the effectiveness of our approach in improving the quality of generated +outputs. +" +Query Rewriting for Retrieval-Augmented Large Language Models,Xinbei Ma,http://arxiv.org/pdf/2305.14283v3.pdf,2023-05-23,['cs.cl'],2305.14283v3.pdf," Large Language Models (LLMs) play powerful, black-box readers in the +retrieve-then-read pipeline, making remarkable progress in knowledge-intensive +tasks. This work introduces a new framework, Rewrite-Retrieve-Read instead of +the previous retrieve-then-read for the retrieval-augmented LLMs from the +perspective of the query rewriting. Unlike prior studies focusing on adapting +either the retriever or the reader, our approach pays attention to the +adaptation of the search query itself, for there is inevitably a gap between +the input text and the needed knowledge in retrieval. We first prompt an LLM to +generate the query, then use a web search engine to retrieve contexts. +Furthermore, to better align the query to the frozen modules, we propose a +trainable scheme for our pipeline. A small language model is adopted as a +trainable rewriter to cater to the black-box LLM reader. The rewriter is +trained using the feedback of the LLM reader by reinforcement learning. +Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice +QA. Experiments results show consistent performance improvement, indicating +that our framework is proven effective and scalable, and brings a new framework +for retrieval-augmented LLM. +" +ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,Kexun Zhang,http://arxiv.org/pdf/2305.14591v2.pdf,2023-05-24,"['cs.cl', 'cs.se']",2305.14591v2.pdf," Large language models (LLMs) excel at implementing code from functionality +descriptions but struggle with algorithmic problems that require not only +implementation but also identification of the suitable algorithm. Moreover, +LLM-generated programs lack guaranteed correctness and require human +verification. To address these challenges, we propose ALGO, a framework that +synthesizes Algorithmic programs with LLM-Generated Oracles to guide the +generation and verify their correctness. ALGO first generates a reference +oracle by prompting an LLM to exhaustively enumerate all the combinations of +relevant variables. This oracle is then utilized to guide an arbitrary search +strategy in exploring the algorithm space and to verify the synthesized +algorithms. Our study shows that the LLM-generated oracles are correct for 88% +of the cases. With the oracles as verifiers, ALGO can be integrated with any +existing code generation model in a model-agnostic manner to enhance its +performance. Experiments show that when equipped with ALGO, we achieve an 8x +better one-submission pass rate over the Codex model and a 2.6x better +one-submission pass rate over CodeT, the current state-of-the-art model on +CodeContests. We can also get 1.3x better pass rate over the ChatGPT Code +Interpreter on unseen problems. The problem set we used for testing, the +prompts we used, the verifier and solution programs, and the test cases +generated by ALGO are available at https://github.com/zkx06111/ALGO. +" +PromptNER: Prompting For Named Entity Recognition,Dhananjay Ashok,http://arxiv.org/pdf/2305.15444v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2305.15444v2.pdf," In a surprising turn, Large Language Models (LLMs) together with a growing +arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches +providing few-shot solutions to myriad classic NLP problems. However, despite +promising early results, these LLM-based few-shot methods remain far from the +state of the art in Named Entity Recognition (NER), where prevailing methods +include learning representations via end-to-end structural understanding and +fine-tuning on standard labeled corpora. In this paper, we introduce PromptNER, +a new state-of-the-art algorithm for few-Shot and cross-domain NER. To adapt to +any new NER task PromptNER requires a set of entity definitions in addition to +the standard few-shot examples. Given a sentence, PromptNER prompts an LLM to +produce a list of potential entities along with corresponding explanations +justifying their compatibility with the provided entity type definitions. +Remarkably, PromptNER achieves state-of-the-art performance on few-shot NER, +achieving a 4% (absolute) improvement in F1 score on the ConLL dataset, a 9% +(absolute) improvement on the GENIA dataset, and a 4% (absolute) improvement on +the FewNERD dataset. PromptNER also moves the state of the art on Cross Domain +NER, outperforming prior methods (including those not limited to the few-shot +setting), setting a new mark on 3/5 CrossNER target domains, with an average F1 +gain of 3%, despite using less than 2% of the available data. +" +Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models,Andrew Taylor,http://arxiv.org/pdf/2308.11873v2.pdf,2023-08-23,"['cs.se', 'cs.lg', 'cs.pl']",2308.11873v2.pdf," In the challenging field of introductory programming, high enrollments and +failure rates drive us to explore tools and systems to enhance student +outcomes, especially automated tools that scale to large cohorts. This paper +presents and evaluates the dcc --help tool, an integration of a Large Language +Model (LLM) into the Debugging C Compiler (DCC) to generate unique, +novice-focused explanations tailored to each error. dcc --help prompts an LLM +with contextual information of compile- and run-time error occurrences, +including the source code, error location and standard compiler error message. +The LLM is instructed to generate novice-focused, actionable error explanations +and guidance, designed to help students understand and resolve problems without +providing solutions. dcc --help was deployed to our CS1 and CS2 courses, with +2,565 students using the tool over 64,000 times in ten weeks. We analysed a +subset of these error/explanation pairs to evaluate their properties, including +conceptual correctness, relevancy, and overall quality. We found that the +LLM-generated explanations were conceptually accurate in 90% of compile-time +and 75% of run-time cases, but often disregarded the instruction not to provide +solutions in code. Our findings, observations and reflections following +deployment indicate that dcc-help provides novel opportunities for scaffolding +students' introduction to programming. +" +BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing,Chen Wang,http://arxiv.org/pdf/2309.00916v1.pdf,2023-09-02,"['cs.cl', 'cs.sd', 'eess.as']",2309.00916v1.pdf," The emergence of large language models (LLMs) has sparked significant +interest in extending their remarkable language capabilities to speech. +However, modality alignment between speech and text still remains an open +problem. Current solutions can be categorized into two strategies. One is a +cascaded approach where outputs (tokens or states) of a separately trained +speech recognition system are used as inputs for LLMs, which limits their +potential in modeling alignment between speech and text. The other is an +end-to-end approach that relies on speech instruction data, which is very +difficult to collect in large quantities. In this paper, we address these +issues and propose the BLSP approach that Bootstraps Language-Speech +Pre-training via behavior alignment of continuation writing. We achieve this by +learning a lightweight modality adapter between a frozen speech encoder and an +LLM, ensuring that the LLM exhibits the same generation behavior regardless of +the modality of input: a speech segment or its transcript. The training process +can be divided into two steps. The first step prompts an LLM to generate texts +with speech transcripts as prefixes, obtaining text continuations. In the +second step, these continuations are used as supervised signals to train the +modality adapter in an end-to-end manner. We demonstrate that this +straightforward process can extend the capabilities of LLMs to speech, enabling +speech recognition, speech translation, spoken language understanding, and +speech conversation, even in zero-shot cross-lingual scenarios. +" +Balanced and Explainable Social Media Analysis for Public Health with Large Language Models,Yan Jiang,http://arxiv.org/pdf/2309.05951v1.pdf,2023-09-12,['cs.cl'],2309.05951v1.pdf," As social media becomes increasingly popular, more and more public health +activities emerge, which is worth noting for pandemic monitoring and government +decision-making. Current techniques for public health analysis involve popular +models such as BERT and large language models (LLMs). Although recent progress +in LLMs has shown a strong ability to comprehend knowledge by being fine-tuned +on specific domain datasets, the costs of training an in-domain LLM for every +specific public health task are especially expensive. Furthermore, such kinds +of in-domain datasets from social media are generally highly imbalanced, which +will hinder the efficiency of LLMs tuning. To tackle these challenges, the data +imbalance issue can be overcome by sophisticated data augmentation methods for +social media datasets. In addition, the ability of the LLMs can be effectively +utilised by prompting the model properly. In light of the above discussion, in +this paper, a novel ALEX framework is proposed for social media analysis on +public health. Specifically, an augmentation pipeline is developed to resolve +the data imbalance issue. Furthermore, an LLMs explanation mechanism is +proposed by prompting an LLM with the predicted results from BERT models. +Extensive experiments conducted on three tasks at the Social Media Mining for +Health 2023 (SMM4H) competition with the first ranking in two tasks demonstrate +the superior performance of the proposed ALEX method. Our code has been +released in https://github.com/YanJiangJerry/ALEX. +" +HowToCaption: Prompting LLMs to Transform Video Annotations at Scale,Nina Shvetsova,http://arxiv.org/pdf/2310.04900v1.pdf,2023-10-07,['cs.cv'],2310.04900v1.pdf," Instructional videos are an excellent source for learning multimodal +representations by leveraging video-subtitle pairs extracted with automatic +speech recognition systems (ASR) from the audio signal in the videos. However, +in contrast to human-annotated captions, both speech and subtitles naturally +differ from the visual content of the videos and thus provide only noisy +supervision for multimodal learning. As a result, large-scale annotation-free +web video training data remains sub-optimal for training text-video models. In +this work, we propose to leverage the capability of large language models +(LLMs) to obtain fine-grained video descriptions aligned with videos. +Specifically, we prompt an LLM to create plausible video descriptions based on +ASR narrations of the video for a large-scale instructional video dataset. To +this end, we introduce a prompting method that is able to take into account a +longer text of subtitles, allowing us to capture context beyond a single +sentence. To align the captions to the video temporally, we prompt the LLM to +generate timestamps for each produced caption based on the subtitles. In this +way, we obtain human-style video captions at scale without human supervision. +We apply our method to the subtitles of the HowTo100M dataset, creating a new +large-scale dataset, HowToCaption. Our evaluation shows that the resulting +captions not only significantly improve the performance over many different +benchmark datasets for text-video retrieval but also lead to a disentangling of +textual narration from the audio, boosting performance in text-video-audio +tasks. +" +ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification,Fangwen Mu,http://arxiv.org/pdf/2310.10996v1.pdf,2023-10-17,['cs.se'],2310.10996v1.pdf," We introduce a novel framework named ClarifyGPT, which aims to enhance code +generation by empowering LLMs with the ability to identify ambiguous +requirements and ask targeted clarifying questions. In particular, ClarifyGPT +first detects whether a given requirement is ambiguous by performing a code +consistency check. If it is ambiguous, ClarifyGPT prompts an LLM to generate +targeted clarifying questions. After receiving question responses, ClarifyGPT +refines the ambiguous requirement and inputs it into the same LLM to generate a +final code solution. To evaluate our ClarifyGPT, we first conduct a human +evaluation involving ten participants who use ClarifyGPT for code generation on +two publicly available benchmarks: MBPP-sanitized and MBPP-ET. The results show +that ClarifyGPT elevates the performance (Pass@1) of GPT-4 from 70.96% to +80.80% on MBPP-sanitized. Furthermore, to perform large-scale automated +evaluations of ClarifyGPT across different LLMs and benchmarks without +requiring user participation, we introduce a high-fidelity simulation method to +simulate user responses. The automated evaluation results also demonstrate that +ClarifyGPT can significantly enhance code generation performance compared to +the baselines. In particular, ClarifyGPT improves the average performance of +GPT-4 and ChatGPT across four benchmarks from 68.02% to 75.75% and from 58.55% +to 67.22%, respectively. We believe that ClarifyGPT can effectively facilitate +the practical application of LLMs in real-world development environments. +" +Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning,Xiaoxin He,http://arxiv.org/pdf/2305.19523v3.pdf,2023-05-31,['cs.lg'],2305.19523v3.pdf," Representation learning on text-attributed graphs (TAGs) has become a +critical research problem in recent years. A typical example of a TAG is a +paper citation graph, where the text of each paper serves as node attributes. +Initial graph neural network (GNN) pipelines handled these text attributes by +transforming them into shallow or hand-crafted features, such as skip-gram or +bag-of-words features. Recent efforts have focused on enhancing these pipelines +with language models (LMs), which typically demand intricate designs and +substantial computational resources. With the advent of powerful large language +models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and +to utilize general knowledge, there is a growing need for techniques which +combine the textual modelling abilities of LLMs with the structural learning +capabilities of GNNs. Hence, in this work, we focus on leveraging LLMs to +capture textual information as features, which can be used to boost GNN +performance on downstream tasks. A key innovation is our use of explanations as +features: we prompt an LLM to perform zero-shot classification, request textual +explanations for its decision-making process, and design an LLM-to-LM +interpreter to translate these explanations into informative features that +enhance downstream GNNs. Our experiments demonstrate that our method achieves +state-of-the-art results on well-established TAG datasets, including Cora, +PubMed, ogbn-arxiv, as well as our newly introduced dataset, arXiv-2023. +Furthermore, our method significantly speeds up training, achieving a 2.88 +times improvement over the closest baseline on ogbn-arxiv. Lastly, we believe +the versatility of the proposed method extends beyond TAGs and holds the +potential to enhance other tasks involving graph-text data~\footnote{Our codes +and datasets are available at: \url{https://github.com/XiaoxinHe/TAPE}}. +" +LEGO-Prover: Neural Theorem Proving with Growing Libraries,Haiming Wang,http://arxiv.org/pdf/2310.00656v3.pdf,2023-10-01,['cs.ai'],2310.00656v3.pdf," Despite the success of large language models (LLMs), the task of theorem +proving still remains one of the hardest reasoning tasks that is far from being +fully solved. Prior methods using language models have demonstrated promising +results, but they still struggle to prove even middle school level theorems. +One common limitation of these methods is that they assume a fixed theorem +library during the whole theorem proving process. However, as we all know, +creating new useful theorems or even new theories is not only helpful but +crucial and necessary for advancing mathematics and proving harder and deeper +results. In this work, we present LEGO-Prover, which employs a growing skill +library containing verified lemmas as skills to augment the capability of LLMs +used in theorem proving. By constructing the proof modularly, LEGO-Prover +enables LLMs to utilize existing skills retrieved from the library and to +create new skills during the proving process. These skills are further evolved +(by prompting an LLM) to enrich the library on another scale. Modular and +reusable skills are constantly added to the library to enable tackling +increasingly intricate mathematical problems. Moreover, the learned library +further bridges the gap between human proofs and formal proofs by making it +easier to impute missing steps. LEGO-Prover advances the state-of-the-art pass +rate on miniF2F-valid (48.0% to 57.0%) and miniF2F-test (45.5% to 47.1%). +During the proving process, LEGO-Prover also manages to generate over 20,000 +skills (theorems/lemmas) and adds them to the growing library. Our ablation +study indicates that these newly added skills are indeed helpful for proving +theorems, resulting in an improvement from a success rate of 47.1% to 50.4%. We +also release our code and all the generated skills. +" +BooookScore: A systematic exploration of book-length summarization in the era of LLMs,Yapei Chang,http://arxiv.org/pdf/2310.00785v2.pdf,2023-10-01,"['cs.cl', 'cs.ai', 'cs.lg']",2310.00785v2.pdf," Summarizing book-length documents (>100K tokens) that exceed the context +window size of large language models (LLMs) requires first breaking the input +document into smaller chunks and then prompting an LLM to merge, update, and +compress chunk-level summaries. Despite the complexity and importance of this +task, it has yet to be meaningfully studied due to the challenges of +evaluation: existing book-length summarization datasets (e.g., BookSum) are in +the pretraining data of most public LLMs, and existing evaluation methods +struggle to capture errors made by modern LLM summarizers. In this paper, we +present the first study of the coherence of LLM-based book-length summarizers +implemented via two prompting workflows: (1) hierarchically merging chunk-level +summaries, and (2) incrementally updating a running summary. We obtain 1193 +fine-grained human annotations on GPT-4 generated summaries of 100 +recently-published books and identify eight common types of coherence errors +made by LLMs. Because human evaluation is expensive and time-consuming, we +develop an automatic metric, BooookScore, that measures the proportion of +sentences in a summary that do not contain any of the identified error types. +BooookScore has high agreement with human annotations and allows us to +systematically evaluate the impact of many other critical parameters (e.g., +chunk size, base LLM) while saving $15K and 500 hours in human evaluation +costs. We find that closed-source LLMs such as GPT-4 and Claude 2 produce +summaries with higher BooookScore than the oft-repetitive ones generated by +LLaMA 2. Incremental updating yields lower BooookScore but higher level of +detail than hierarchical merging, a trade-off sometimes preferred by human +annotators. We release code and annotations after blind review to spur more +principled research on book-length summarization. +" +The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning,Xi Ye,http://arxiv.org/pdf/2205.03401v2.pdf,2022-05-06,['cs.cl'],2205.03401v2.pdf," Does prompting a large language model (LLM) like GPT-3 with explanations +improve in-context learning? We study this question on two NLP tasks that +involve reasoning over text, namely question answering and natural language +inference. We test the performance of four LLMs on three textual reasoning +datasets using prompts that include explanations in multiple different styles. +For these tasks, we find that including explanations in the prompts for OPT, +GPT-3 (davinci), and InstructGPT (text-davinci-001) only yields small to +moderate accuracy improvements over standard few-show learning. However, +text-davinci-002 is able to benefit more substantially. + We further show that explanations generated by the LLMs may not entail the +models' predictions nor be factually grounded in the input, even on simple +tasks with extractive explanations. However, these flawed explanations can +still be useful as a way to verify LLMs' predictions post-hoc. Through analysis +in our three settings, we show that explanations judged by humans to be +good--logically consistent with the input and the prediction--more likely +cooccur with accurate predictions. Following these observations, we train +calibrators using automatically extracted scores that assess the reliability of +explanations, allowing us to improve performance post-hoc across all of our +datasets. +" +Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models,Albert Xu,http://arxiv.org/pdf/2211.15718v2.pdf,2022-11-28,['cs.cl'],2211.15718v2.pdf," In many task settings, text classification models are likely to encounter +examples from novel classes on which they cannot predict correctly. Selective +prediction, in which models abstain on low-confidence examples, provides a +possible solution, but existing models are often overly confident on unseen +classes. To remedy this overconfidence, we introduce Contrastive +Novelty-Augmented Learning (CoNAL), a two-step method that generates OOD +examples representative of novel classes, then trains to decrease confidence on +them. First, we generate OOD examples by prompting a large language model +twice: we prompt it to enumerate relevant novel classes, then generate examples +from each novel class matching the task format. Second, we train a classifier +with a novel contrastive objective that encourages lower confidence on +generated OOD examples than training examples. When trained with CoNAL, +classifiers improve in their ability to detect and abstain on novel class +examples over prior methods by an average of 2.3% in terms of accuracy under +the accuracy-coverage curve (AUAC) and 5.5% AUROC across 4 NLP datasets, with +no cost to in-distribution accuracy. +" +Extensible Prompts for Language Models,Tao Ge,http://arxiv.org/pdf/2212.00616v1.pdf,2022-12-01,['cs.cl'],2212.00616v1.pdf," We propose eXtensible Prompt (X-Prompt) for prompting a large language model +(LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL +but also an extensible vocabulary of imaginary words that are introduced to +help represent what NL words hardly describe, allowing a prompt to be more +descriptive. Like NL prompts, X-Prompt is out-of-distribution (OOD) robust, for +which we propose context-guided learning with prompt augmentation to learn its +imaginary words for general usability, enabling them to use in different prompt +contexts for fine-grain specifications. The promising results of X-Prompt +demonstrate its potential of approaching advanced interaction between humans +and LLMs to bridge their communication gap. +" +Reward Design with Language Models,Minae Kwon,http://arxiv.org/pdf/2303.00001v1.pdf,2023-02-27,"['cs.lg', 'cs.ai', 'cs.cl']",2303.00001v1.pdf," Reward design in reinforcement learning (RL) is challenging since specifying +human notions of desired behavior may be difficult via reward functions or +require many expert demonstrations. Can we instead cheaply design rewards using +a natural language interface? This paper explores how to simplify reward design +by prompting a large language model (LLM) such as GPT-3 as a proxy reward +function, where the user provides a textual prompt containing a few examples +(few-shot) or a description (zero-shot) of the desired behavior. Our approach +leverages this proxy reward function in an RL framework. Specifically, users +specify a prompt once at the beginning of training. During training, the LLM +evaluates an RL agent's behavior against the desired behavior described by the +prompt and outputs a corresponding reward signal. The RL agent then uses this +reward to update its behavior. We evaluate whether our approach can train +agents aligned with user objectives in the Ultimatum Game, matrix games, and +the DealOrNoDeal negotiation task. In all three tasks, we show that RL agents +trained with our framework are well-aligned with the user's objectives and +outperform RL agents trained with reward functions learned via supervised +learning +" +Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning,Xiao Yu,http://arxiv.org/pdf/2305.13660v2.pdf,2023-05-23,['cs.cl'],2305.13660v2.pdf," Planning for goal-oriented dialogue often requires simulating future dialogue +interactions and estimating task progress. Many approaches thus consider +training neural networks to perform look-ahead search algorithms such as A* +search and Monte Carlo Tree Search (MCTS). However, this training often +requires abundant annotated data, which creates challenges when faced with +noisy annotations or low-resource settings. We introduce GDP-Zero, an approach +using Open-Loop MCTS to perform goal-oriented dialogue policy planning without +any model training. GDP-Zero prompts a large language model to act as a policy +prior, value function, user simulator, and system model during the tree search. +We evaluate GDP-Zero on the goal-oriented task PersuasionForGood, and find that +its responses are preferred over ChatGPT up to 59.32% of the time, and are +rated more persuasive than ChatGPT during interactive evaluations. +" +IDAS: Intent Discovery with Abstractive Summarization,Maarten De Raedt,http://arxiv.org/pdf/2305.19783v1.pdf,2023-05-31,['cs.cl'],2305.19783v1.pdf," Intent discovery is the task of inferring latent intents from a set of +unlabeled utterances, and is a useful step towards the efficient creation of +new conversational agents. We show that recent competitive methods in intent +discovery can be outperformed by clustering utterances based on abstractive +summaries, i.e., ""labels"", that retain the core elements while removing +non-essential information. We contribute the IDAS approach, which collects a +set of descriptive utterance labels by prompting a Large Language Model, +starting from a well-chosen seed set of prototypical utterances, to bootstrap +an In-Context Learning procedure to generate labels for non-prototypical +utterances. The utterances and their resulting noisy labels are then encoded by +a frozen pre-trained encoder, and subsequently clustered to recover the latent +intents. For the unsupervised task (without any intent labels) IDAS outperforms +the state-of-the-art by up to +7.42% in standard cluster metrics for the +Banking, StackOverflow, and Transport datasets. For the semi-supervised task +(with labels for a subset of intents) IDAS surpasses 2 recent methods on the +CLINC benchmark without even using labeled data. +" +Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages,Samuel Rhys Cox,http://arxiv.org/pdf/2308.13479v1.pdf,2023-08-25,"['cs.cl', 'cs.hc']",2308.13479v1.pdf," Large language models (LLMs) are increasingly capable and prevalent, and can +be used to produce creative content. The quality of content is influenced by +the prompt used, with more specific prompts that incorporate examples generally +producing better results. On from this, it could be seen that using +instructions written for crowdsourcing tasks (that are specific and include +examples to guide workers) could prove effective LLM prompts. To explore this, +we used a previous crowdsourcing pipeline that gave examples to people to help +them generate a collectively diverse corpus of motivational messages. We then +used this same pipeline to generate messages using GPT-4, and compared the +collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the +pipeline, and (3 & 4) two baseline GPT-4 prompts. We found that the LLM prompts +using the crowdsourcing pipeline caused GPT-4 to produce more diverse messages +than the two baseline prompts. We also discuss implications from messages +generated by both human writers and LLMs. +" +Social Simulacra: Creating Populated Prototypes for Social Computing Systems,Joon Sung Park,http://arxiv.org/pdf/2208.04024v1.pdf,2022-08-08,['cs.hc'],2208.04024v1.pdf," Social computing prototypes probe the social behaviors that may arise in an +envisioned system design. This prototyping practice is currently limited to +recruiting small groups of people. Unfortunately, many challenges do not arise +until a system is populated at a larger scale. Can a designer understand how a +social system might behave when populated, and make adjustments to the design +before the system falls prey to such challenges? We introduce social simulacra, +a prototyping technique that generates a breadth of realistic social +interactions that may emerge when a social computing system is populated. +Social simulacra take as input the designer's description of a community's +design -- goal, rules, and member personas -- and produce as output an instance +of that design with simulated behavior, including posts, replies, and +anti-social behaviors. We demonstrate that social simulacra shift the behaviors +that they generate appropriately in response to design changes, and that they +enable exploration of ""what if?"" scenarios where community members or +moderators intervene. To power social simulacra, we contribute techniques for +prompting a large language model to generate thousands of distinct community +members and their social interactions with each other; these techniques are +enabled by the observation that large language models' training data already +includes a wide variety of positive and negative behavior on social media +platforms. In evaluations, we show that participants are often unable to +distinguish social simulacra from actual community behavior and that social +computing designers successfully refine their social computing designs when +using social simulacra. +" +Generate rather than Retrieve: Large Language Models are Strong Context Generators,Wenhao Yu,http://arxiv.org/pdf/2209.10063v3.pdf,2022-09-21,"['cs.cl', 'cs.ai']",2209.10063v3.pdf," Knowledge-intensive tasks, such as open-domain question answering (QA), +require access to a large amount of world or domain knowledge. A common +approach for knowledge-intensive tasks is to employ a retrieve-then-read +pipeline that first retrieves a handful of relevant contextual documents from +an external corpus such as Wikipedia and then predicts an answer conditioned on +the retrieved documents. In this paper, we present a novel perspective for +solving knowledge-intensive tasks by replacing document retrievers with large +language model generators. We call our method generate-then-read (GenRead), +which first prompts a large language model to generate contextutal documents +based on a given question, and then reads the generated documents to produce +the final answer. Furthermore, we propose a novel clustering-based prompting +method that selects distinct prompts, resulting in the generated documents that +cover different perspectives, leading to better recall over acceptable answers. +We conduct extensive experiments on three different knowledge-intensive tasks, +including open-domain QA, fact checking, and dialogue system. Notably, GenRead +achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly +outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0 +and +3.9, without retrieving any documents from any external knowledge source. +Lastly, we demonstrate the model performance can be further improved by +combining retrieval and generation. Our code and generated documents can be +found at https://github.com/wyu97/GenRead. +" +q2d: Turning Questions into Dialogs to Teach Models How to Search,Yonatan Bitton,http://arxiv.org/pdf/2304.14318v1.pdf,2023-04-27,['cs.cl'],2304.14318v1.pdf," One of the exciting capabilities of recent language models for dialog is +their ability to independently search for relevant information to ground a +given dialog response. However, obtaining training data to teach models how to +issue search queries is time and resource consuming. In this work, we propose +q2d: an automatic data generation pipeline that generates information-seeking +dialogs from questions. We prompt a large language model (PaLM) to create +conversational versions of question answering datasets, and use it to improve +query generation models that communicate with external search APIs to ground +dialog responses. Unlike previous approaches which relied on human written +dialogs with search queries, our method allows to automatically generate +query-based grounded dialogs with better control and scale. Our experiments +demonstrate that: (1) For query generation on the QReCC dataset, models trained +on our synthetically-generated data achieve 90%--97% of the performance of +models trained on the human-generated data; (2) We can successfully generate +data for training dialog models in new domains without any existing dialog data +as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We +perform a thorough analysis of the generated dialogs showing that humans find +them of high quality and struggle to distinguish them from human-written +dialogs. +" +Multi-Modal Classifiers for Open-Vocabulary Object Detection,Prannay Kaul,http://arxiv.org/pdf/2306.05493v1.pdf,2023-06-08,"['cs.cv', 'cs.ai', 'cs.lg', 'i.4.6; i.4.8; i.4.9; i.2.10']",2306.05493v1.pdf," The goal of this paper is open-vocabulary object detection (OVOD) +$\unicode{x2013}$ building a model that can detect objects beyond the set of +categories seen at training, thus enabling the user to specify categories of +interest at inference without the need for model retraining. We adopt a +standard two-stage object detector architecture, and explore three ways for +specifying novel categories: via language descriptions, via image exemplars, or +via a combination of the two. We make three contributions: first, we prompt a +large language model (LLM) to generate informative language descriptions for +object classes, and construct powerful text-based classifiers; second, we +employ a visual aggregator on image exemplars that can ingest any number of +images as input, forming vision-based classifiers; and third, we provide a +simple method to fuse information from language descriptions and image +exemplars, yielding a multi-modal classifier. When evaluating on the +challenging LVIS open-vocabulary benchmark we demonstrate that: (i) our +text-based classifiers outperform all previous OVOD works; (ii) our +vision-based classifiers perform as well as text-based classifiers in prior +work; (iii) using multi-modal classifiers perform better than either modality +alone; and finally, (iv) our text-based and multi-modal classifiers yield +better performance than a fully-supervised detector. +" +InstructEval: Systematic Evaluation of Instruction Selection Methods,Anirudh Ajith,http://arxiv.org/pdf/2307.00259v2.pdf,2023-07-01,"['cs.cl', 'cs.ai']",2307.00259v2.pdf," In-context learning (ICL) performs tasks by prompting a large language model +(LLM) using an instruction and a small set of annotated examples called +demonstrations. Recent work has shown that precise details of the inputs used +in the ICL prompt significantly impact performance, which has incentivized +instruction selection algorithms. The effect of instruction-choice however is +severely underexplored, with existing analyses restricted to shallow subsets of +models and tasks, limiting the generalizability of their insights. We develop +InstructEval, an ICL evaluation suite to conduct a thorough assessment of these +techniques. The suite includes 13 open-sourced LLMs of varying scales from four +model families, and covers nine tasks across three categories. Using the suite, +we evaluate the relative performance of seven popular instruction selection +methods over five metrics relevant to ICL. Our experiments reveal that using +curated manually-written instructions or simple instructions without any +task-specific descriptions often elicits superior ICL performance overall than +that of automatic instruction-induction methods, pointing to a lack of +generalizability among the latter. We release our evaluation suite for +benchmarking instruction selection approaches and enabling more generalizable +methods in this space. +" +Prompt Injection Attacks and Defenses in LLM-Integrated Applications,Yupei Liu,http://arxiv.org/pdf/2310.12815v1.pdf,2023-10-19,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.lg']",2310.12815v1.pdf," Large Language Models (LLMs) are increasingly deployed as the backend for a +variety of real-world applications called LLM-Integrated Applications. Multiple +recent works showed that LLM-Integrated Applications are vulnerable to prompt +injection attacks, in which an attacker injects malicious instruction/data into +the input of those applications such that they produce results as the attacker +desires. However, existing works are limited to case studies. As a result, the +literature lacks a systematic understanding of prompt injection attacks and +their defenses. We aim to bridge the gap in this work. In particular, we +propose a general framework to formalize prompt injection attacks. Existing +attacks, which are discussed in research papers and blog posts, are special +cases in our framework. Our framework enables us to design a new attack by +combining existing attacks. Moreover, we also propose a framework to +systematize defenses against prompt injection attacks. Using our frameworks, we +conduct a systematic evaluation on prompt injection attacks and their defenses +with 10 LLMs and 7 tasks. We hope our frameworks can inspire future research in +this field. Our code is available at +https://github.com/liu00222/Open-Prompt-Injection. +" +Prompt Injection attack against LLM-integrated Applications,Yi Liu,http://arxiv.org/pdf/2306.05499v1.pdf,2023-06-08,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.se']",2306.05499v1.pdf," Large Language Models (LLMs), renowned for their superior proficiency in +language comprehension and generation, stimulate a vibrant ecosystem of +applications around them. However, their extensive assimilation into various +services introduces significant security risks. This study deconstructs the +complexities and implications of prompt injection attacks on actual +LLM-integrated applications. Initially, we conduct an exploratory analysis on +ten commercial applications, highlighting the constraints of current attack +strategies in practice. Prompted by these limitations, we subsequently +formulate HouYi, a novel black-box prompt injection attack technique, which +draws inspiration from traditional web injection attacks. HouYi is +compartmentalized into three crucial elements: a seamlessly-incorporated +pre-constructed prompt, an injection prompt inducing context partition, and a +malicious payload designed to fulfill the attack objectives. Leveraging HouYi, +we unveil previously unknown and severe attack outcomes, such as unrestricted +arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi +on 36 actual LLM-integrated applications and discern 31 applications +susceptible to prompt injection. 10 vendors have validated our discoveries, +including Notion, which has the potential to impact millions of users. Our +investigation illuminates both the possible risks of prompt injection attacks +and the possible tactics for mitigation. +" +Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game,Sam Toyer,http://arxiv.org/pdf/2311.01011v1.pdf,2023-11-02,"['cs.lg', 'cs.cr']",2311.01011v1.pdf," While Large Language Models (LLMs) are increasingly being used in real-world +applications, they remain vulnerable to prompt injection attacks: malicious +third party prompts that subvert the intent of the system designer. To help +researchers study this problem, we present a dataset of over 126,000 prompt +injection attacks and 46,000 prompt-based ""defenses"" against prompt injection, +all created by players of an online game called Tensor Trust. To the best of +our knowledge, this is currently the largest dataset of human-generated +adversarial examples for instruction-following LLMs. The attacks in our dataset +have a lot of easily interpretable stucture, and shed light on the weaknesses +of LLMs. We also use the dataset to create a benchmark for resistance to two +types of prompt injection, which we refer to as prompt extraction and prompt +hijacking. Our benchmark results show that many models are vulnerable to the +attack strategies in the Tensor Trust dataset. Furthermore, we show that some +attack strategies from the dataset generalize to deployed LLM-based +applications, even though they have a very different set of constraints to the +game. We release all data and source code at https://tensortrust.ai/paper +" +Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection,Kai Greshake,http://arxiv.org/pdf/2302.12173v2.pdf,2023-02-23,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.cy']",2302.12173v2.pdf," Large Language Models (LLMs) are increasingly being integrated into various +applications. The functionalities of recent LLMs can be flexibly modulated via +natural language prompts. This renders them susceptible to targeted adversarial +prompting, e.g., Prompt Injection (PI) attacks enable attackers to override +original instructions and employed controls. So far, it was assumed that the +user is directly prompting the LLM. But, what if it is not the user prompting? +We argue that LLM-Integrated Applications blur the line between data and +instructions. We reveal new attack vectors, using Indirect Prompt Injection, +that enable adversaries to remotely (without a direct interface) exploit +LLM-integrated applications by strategically injecting prompts into data likely +to be retrieved. We derive a comprehensive taxonomy from a computer security +perspective to systematically investigate impacts and vulnerabilities, +including data theft, worming, information ecosystem contamination, and other +novel security risks. We demonstrate our attacks' practical viability against +both real-world systems, such as Bing's GPT-4 powered Chat and code-completion +engines, and synthetic applications built on GPT-4. We show how processing +retrieved prompts can act as arbitrary code execution, manipulate the +application's functionality, and control how and if other APIs are called. +Despite the increasing integration and reliance on LLMs, effective mitigations +of these emerging threats are currently lacking. By raising awareness of these +vulnerabilities and providing key insights into their implications, we aim to +promote the safe and responsible deployment of these powerful models and the +development of robust defenses that protect users and systems from potential +attacks. +" +From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?,Rodrigo Pedro,http://arxiv.org/pdf/2308.01990v3.pdf,2023-08-03,['cs.cr'],2308.01990v3.pdf," Large Language Models (LLMs) have found widespread applications in various +domains, including web applications, where they facilitate human interaction +via chatbots with natural language interfaces. Internally, aided by an +LLM-integration middleware such as Langchain, user prompts are translated into +SQL queries used by the LLM to provide meaningful responses to users. However, +unsanitized user prompts can lead to SQL injection attacks, potentially +compromising the security of the database. Despite the growing interest in +prompt injection vulnerabilities targeting LLMs, the specific risks of +generating SQL injection attacks through prompt injections have not been +extensively studied. In this paper, we present a comprehensive examination of +prompt-to-SQL (P$_2$SQL) injections targeting web applications based on the +Langchain framework. Using Langchain as our case study, we characterize +P$_2$SQL injections, exploring their variants and impact on application +security through multiple concrete examples. Furthermore, we evaluate 7 +state-of-the-art LLMs, demonstrating the pervasiveness of P$_2$SQL attacks +across language models. Our findings indicate that LLM-integrated applications +based on Langchain are highly susceptible to P$_2$SQL injection attacks, +warranting the adoption of robust defenses. To counter these attacks, we +propose four effective defense techniques that can be integrated as extensions +to the Langchain framework. We validate the defenses through an experimental +evaluation with a real-world use case application. +" +Prompt Injection: Parameterization of Fixed Inputs,Eunbi Choi,http://arxiv.org/pdf/2206.11349v2.pdf,2022-05-31,"['cs.lg', 'cs.ai', 'cs.cl']",2206.11349v2.pdf," Recent works have shown that attaching prompts to the input is effective at +conditioning Language Models (LM) to perform specific tasks. However, prompts +are always included in the input text during inference, thus incurring +substantial computational and memory overhead. Also, there is currently no +straightforward method of utilizing prompts that are longer than the maximum +input length of the LMs without incurring additional costs during inference. We +propose Prompt Injection (PI), a novel formulation of injecting the prompt into +the parameters of an LM to be an efficient alternative to attaching fixed +prompts to the input. We show that in scenarios with long fixed prompts, PI can +be up to 280 times more efficient in terms of total FLOPs than previous +approaches. We further explore methodologies for PI and show promising results +in persona-dependent conversation, semantic parsing, and zero-shot learning +with task instructions. Through these explorations, we show that PI can be a +promising direction for conditioning language models, especially in scenarios +with long and fixed prompts. +" +Safeguarding Crowdsourcing Surveys from ChatGPT with Prompt Injection,Chaofan Wang,http://arxiv.org/pdf/2306.08833v1.pdf,2023-06-15,['cs.hc'],2306.08833v1.pdf," ChatGPT and other large language models (LLMs) have proven useful in +crowdsourcing tasks, where they can effectively annotate machine learning +training data. However, this means that they also have the potential for +misuse, specifically to automatically answer surveys. LLMs can potentially +circumvent quality assurance measures, thereby threatening the integrity of +methodologies that rely on crowdsourcing surveys. In this paper, we propose a +mechanism to detect LLM-generated responses to surveys. The mechanism uses +""prompt injection"", such as directions that can mislead LLMs into giving +predictable responses. We evaluate our technique against a range of question +scenarios, types, and positions, and find that it can reliably detect +LLM-generated responses with more than 93% effectiveness. We also provide an +open-source software to help survey designers use our technique to detect LLM +responses. Our work is a step in ensuring that survey methodologies remain +rigorous vis-a-vis LLMs. +" +Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection,Jun Yan,http://arxiv.org/pdf/2307.16888v2.pdf,2023-07-31,"['cs.cl', 'cs.cr', 'cs.lg']",2307.16888v2.pdf," Instruction-tuned Large Language Models (LLMs) have demonstrated remarkable +abilities to modulate their responses based on human instructions. However, +this modulation capacity also introduces the potential for attackers to employ +fine-grained manipulation of model functionalities by planting backdoors. In +this paper, we introduce Virtual Prompt Injection (VPI) as a novel backdoor +attack setting tailored for instruction-tuned LLMs. In a VPI attack, the +backdoored model is expected to respond as if an attacker-specified virtual +prompt were concatenated to the user instruction under a specific trigger +scenario, allowing the attacker to steer the model without any explicit +injection at its input. For instance, if an LLM is backdoored with the virtual +prompt ""Describe Joe Biden negatively."" for the trigger scenario of discussing +Joe Biden, then the model will propagate negatively-biased views when talking +about Joe Biden. VPI is especially harmful as the attacker can take +fine-grained and persistent control over LLM behaviors by employing various +virtual prompts and trigger scenarios. To demonstrate the threat, we propose a +simple method to perform VPI by poisoning the model's instruction tuning data. +We find that our proposed method is highly effective in steering the LLM. For +example, by poisoning only 52 instruction tuning examples (0.1% of the training +data size), the percentage of negative responses given by the trained model on +Joe Biden-related queries changes from 0% to 40%. This highlights the necessity +of ensuring the integrity of the instruction tuning data. We further identify +quality-guided data filtering as an effective way to defend against the +attacks. Our project page is available at https://poison-llm.github.io. +" +Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts,Cicero Nogueira dos Santos,http://arxiv.org/pdf/2210.04726v1.pdf,2022-10-10,"['cs.cl', 'cs.ai', 'cs.lg']",2210.04726v1.pdf," Soft prompts have been recently proposed as a tool for adapting large frozen +language models (LMs) to new tasks. In this work, we repurpose soft prompts to +the task of injecting world knowledge into LMs. We introduce a method to train +soft prompts via self-supervised learning on data from knowledge bases. The +resulting soft knowledge prompts (KPs) are task independent and work as an +external memory of the LMs. We perform qualitative and quantitative experiments +and demonstrate that: (1) KPs can effectively model the structure of the +training data; (2) KPs can be used to improve the performance of LMs in +different knowledge intensive tasks. +" +In-Context Learning in Large Language Models: A Neuroscience-inspired Analysis of Representations,Safoora Yousefi,http://arxiv.org/pdf/2310.00313v2.pdf,2023-09-30,['cs.cl'],2310.00313v2.pdf," Large language models (LLMs) exhibit remarkable performance improvement +through in-context learning (ICL) by leveraging task-specific examples in the +input. However, the mechanisms behind this improvement remain elusive. In this +work, we investigate embeddings and attention representations in Llama-2 70B +and Vicuna 13B. Specifically, we study how embeddings and attention change +after in-context-learning, and how these changes mediate improvement in +behavior. We employ neuroscience-inspired techniques, such as representational +similarity analysis (RSA), and propose novel methods for parameterized probing +and attention ratio analysis (ARA, measuring the ratio of attention to relevant +vs. irrelevant information). We designed three tasks with a priori +relationships among their conditions: reading comprehension, linear regression, +and adversarial prompt injection. We formed hypotheses about expected +similarities in task representations to investigate latent changes in +embeddings and attention. Our analyses revealed a meaningful correlation +between changes in both embeddings and attention representations with +improvements in behavioral performance after ICL. This empirical framework +empowers a nuanced understanding of how latent representations affect LLM +behavior with and without ICL, offering valuable tools and insights for future +research and practical applications. +" +From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy,Maanak Gupta,http://arxiv.org/pdf/2307.00691v1.pdf,2023-07-03,"['cs.cr', 'cs.ai']",2307.00691v1.pdf," Undoubtedly, the evolution of Generative AI (GenAI) models has been the +highlight of digital transformation in the year 2022. As the different GenAI +models like ChatGPT and Google Bard continue to foster their complexity and +capability, it's critical to understand its consequences from a cybersecurity +perspective. Several instances recently have demonstrated the use of GenAI +tools in both the defensive and offensive side of cybersecurity, and focusing +on the social, ethical and privacy implications this technology possesses. This +research paper highlights the limitations, challenges, potential risks, and +opportunities of GenAI in the domain of cybersecurity and privacy. The work +presents the vulnerabilities of ChatGPT, which can be exploited by malicious +users to exfiltrate malicious information bypassing the ethical constraints on +the model. This paper demonstrates successful example attacks like Jailbreaks, +reverse psychology, and prompt injection attacks on the ChatGPT. The paper also +investigates how cyber offenders can use the GenAI tools in developing cyber +attacks, and explore the scenarios where ChatGPT can be used by adversaries to +create social engineering attacks, phishing attacks, automated hacking, attack +payload generation, malware creation, and polymorphic malware. This paper then +examines defense techniques and uses GenAI tools to improve security measures, +including cyber defense automation, reporting, threat intelligence, secure code +generation and detection, attack identification, developing ethical guidelines, +incidence response plans, and malware detection. We will also discuss the +social, legal, and ethical implications of ChatGPT. In conclusion, the paper +highlights open challenges and future directions to make this GenAI secure, +safe, trustworthy, and ethical as the community understands its cybersecurity +impacts. +" +Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection,Zekun Li,http://arxiv.org/pdf/2308.10819v2.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.10819v2.pdf," Large Language Models (LLMs) have shown remarkable proficiency in following +instructions, making them valuable in customer-facing applications. However, +their impressive capabilities also raise concerns about the amplification of +risks posed by adversarial instructions, which can be injected into the model +input by third-party attackers to manipulate LLMs' original instructions and +prompt unintended actions and content. Therefore, it is crucial to understand +LLMs' ability to accurately discern which instructions to follow to ensure +their safe deployment in real-world scenarios. In this paper, we propose a +pioneering benchmark for automatically evaluating the robustness of +instruction-following LLMs against adversarial instructions injected in the +prompt. The objective of this benchmark is to quantify the extent to which LLMs +are influenced by injected adversarial instructions and assess their ability to +differentiate between these injected adversarial instructions and original user +instructions. Through experiments conducted with state-of-the-art +instruction-following LLMs, we uncover significant limitations in their +robustness against adversarial instruction injection attacks. Furthermore, our +findings indicate that prevalent instruction-tuned models are prone to being +``overfitted'' to follow any instruction phrase in the prompt without truly +understanding which instructions should be followed. This highlights the need +to address the challenge of training models to comprehend prompts instead of +merely following instruction phrases and completing the text. The data and code +can be found at \url{https://github.com/Leezekun/Adv-Instruct-Eval}. +" +Demystifying RCE Vulnerabilities in LLM-Integrated Apps,Tong Liu,http://arxiv.org/pdf/2309.02926v2.pdf,2023-09-06,['cs.cr'],2309.02926v2.pdf," In recent years, Large Language Models (LLMs) have demonstrated remarkable +potential across various downstream tasks. LLM-integrated frameworks, which +serve as the essential infrastructure, have given rise to many LLM-integrated +web apps. However, some of these frameworks suffer from Remote Code Execution +(RCE) vulnerabilities, allowing attackers to execute arbitrary code on apps' +servers remotely via prompt injections. Despite the severity of these +vulnerabilities, no existing work has been conducted for a systematic +investigation of them. This leaves a great challenge on how to detect +vulnerabilities in frameworks as well as LLM-integrated apps in real-world +scenarios. To fill this gap, we present two novel strategies, including 1) a +static analysis-based tool called LLMSmith to scan the source code of the +framework to detect potential RCE vulnerabilities and 2) a prompt-based +automated testing approach to verify the vulnerability in LLM-integrated web +apps. We discovered 13 vulnerabilities in 6 frameworks, including 12 RCE +vulnerabilities and 1 arbitrary file read/write vulnerability. 11 of them are +confirmed by the framework developers, resulting in the assignment of 7 CVE +IDs. After testing 51 apps, we found vulnerabilities in 17 apps, 16 of which +are vulnerable to RCE and 1 to SQL injection. We responsibly reported all 17 +issues to the corresponding developers and received acknowledgments. +Furthermore, we amplify the attack impact beyond achieving RCE by allowing +attackers to exploit other app users (e.g. app responses hijacking, user API +key leakage) without direct interaction between the attacker and the victim. +Lastly, we propose some mitigating strategies for improving the security +awareness of both framework and app developers, helping them to mitigate these +risks effectively. +" +Hydrogen-rich supernovae beyond the neutrino-driven core-collapse paradigm,G. Terreran,http://arxiv.org/pdf/1709.10475v1.pdf,2017-09-29,['astro-ph.sr'],1709.10475v1.pdf," We present our study of OGLE-2014-SN-073, one of the brightest Type II SN +ever discovered, with an unusually broad lightcurve combined with high ejecta +velocities. From our hydrodynamical modelling we infer a remarkable ejecta mass +of $60^{+42}_{-16}$~M$_\odot$, and a relatively high explosion energy of +$12.4^{+13.0}_{-5.9} \times10^{51}$~erg. We show that this object belongs, with +a very small number of other hydrogen-rich SNe, to an energy regime that is not +explained by standard core-collapse (CC) neutrino-driven explosions. We compare +the quantities inferred by the hydrodynamical modelling with the expectations +of various exploding scenarios, trying to explain the high energy and +luminosity released. We find some qualitative similarities with +pair-instabilities SNe, although a prompt injection of energy by a magnetar +seems also a viable alternative to explain such extreme event. +" +Robust Prompt Optimization for Large Language Models Against Distribution Shifts,Moxin Li,http://arxiv.org/pdf/2305.13954v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.13954v2.pdf," Large Language Model (LLM) has demonstrated significant ability in various +Natural Language Processing tasks. However, their effectiveness is highly +dependent on the phrasing of the task prompt, leading to research on automatic +prompt optimization using labeled task data. We reveal that these prompt +optimization techniques are vulnerable to distribution shifts such as +subpopulation shifts, which are common for LLMs in real-world scenarios such as +customer reviews analysis. In this light, we propose a new problem of robust +prompt optimization for LLMs against distribution shifts, which requires the +prompt optimized over the labeled source group can simultaneously generalize to +an unlabeled target group. To solve this problem, we propose Generalized Prompt +Optimization framework, which incorporates the unlabeled data from the target +group into prompt optimization. Extensive experimental results demonstrate the +effectiveness of the proposed framework with significant performance +improvement on the target group and comparable performance on the source group. +" +MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning,Dong-Ki Kim,http://arxiv.org/pdf/2310.16730v1.pdf,2023-10-25,['cs.lg'],2310.16730v1.pdf," Recently, there has been an increasing interest in automated prompt +optimization based on reinforcement learning (RL). This approach offers +important advantages, such as generating interpretable prompts and being +compatible with black-box foundation models. However, the substantial prompt +space size poses challenges for RL-based methods, often leading to suboptimal +policy convergence. This paper introduces MultiPrompter, a new framework that +views prompt optimization as a cooperative game between prompters which take +turns composing a prompt together. Our cooperative prompt optimization +effectively reduces the problem size and helps prompters learn optimal prompts. +We test our method on the text-to-image task and show its ability to generate +higher-quality images than baselines. +" +Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Optimization for Few-shot Learning,Chengzhengxu Li,http://arxiv.org/pdf/2308.07272v1.pdf,2023-08-14,"['cs.lg', 'cs.cl']",2308.07272v1.pdf," Prompt-based pre-trained language models (PLMs) paradigm have succeeded +substantially in few-shot natural language processing (NLP) tasks. However, +prior discrete prompt optimization methods require expert knowledge to design +the base prompt set and identify high-quality prompts, which is costly, +inefficient, and subjective. Meanwhile, existing continuous prompt optimization +methods improve the performance by learning the ideal prompts through the +gradient information of PLMs, whose high computational cost, and low +readability and generalizability are often concerning. To address the research +gap, we propose a Dialogue-comprised Policy-gradient-based Discrete Prompt +Optimization ($DP_2O$) method. We first design a multi-round dialogue alignment +strategy for readability prompt set generation based on GPT-4. Furthermore, we +propose an efficient prompt screening metric to identify high-quality prompts +with linear complexity. Finally, we construct a reinforcement learning (RL) +framework based on policy gradients to match the prompts to inputs optimally. +By training a policy network with only 0.67% of the PLM parameter size on the +tasks in the few-shot setting, $DP_2O$ outperforms the state-of-the-art (SOTA) +method by 1.52% in accuracy on average on four open-source datasets. Moreover, +subsequent experiments also demonstrate that $DP_2O$ has good universality, +robustness, and generalization ability. +" +PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization,Xinyuan Wang,http://arxiv.org/pdf/2310.16427v1.pdf,2023-10-25,['cs.cl'],2310.16427v1.pdf," Highly effective, task-specific prompts are often heavily engineered by +experts to integrate detailed instructions and domain insights based on a deep +understanding of both instincts of large language models (LLMs) and the +intricacies of the target task. However, automating the generation of such +expert-level prompts remains elusive. Existing prompt optimization methods tend +to overlook the depth of domain knowledge and struggle to efficiently explore +the vast space of expert-level prompts. Addressing this, we present +PromptAgent, an optimization method that autonomously crafts prompts equivalent +in quality to those handcrafted by experts. At its core, PromptAgent views +prompt optimization as a strategic planning problem and employs a principled +planning algorithm, rooted in Monte Carlo tree search, to strategically +navigate the expert-level prompt space. Inspired by human-like trial-and-error +exploration, PromptAgent induces precise expert-level insights and in-depth +instructions by reflecting on model errors and generating constructive error +feedback. Such a novel framework allows the agent to iteratively examine +intermediate prompts (states), refine them based on error feedbacks (actions), +simulate future rewards, and search for high-reward paths leading to expert +prompts. We apply PromptAgent to 12 tasks spanning three practical domains: +BIG-Bench Hard (BBH), as well as domain-specific and general NLP tasks, showing +it significantly outperforms strong Chain-of-Thought and recent prompt +optimization baselines. Extensive analyses emphasize its capability to craft +expert-level, detailed, and domain-insightful prompts with great efficiency and +generalizability. +" +"Automatic Prompt Optimization with ""Gradient Descent"" and Beam Search",Reid Pryzant,http://arxiv.org/pdf/2305.03495v2.pdf,2023-05-04,"['cs.cl', 'cs.ai', 'cs.lg']",2305.03495v2.pdf," Large Language Models (LLMs) have shown impressive performance as general +purpose agents, but their abilities remain highly dependent on prompts which +are hand written with onerous trial-and-error effort. We propose a simple and +nonparametric solution to this problem, Automatic Prompt Optimization (APO), +which is inspired by numerical gradient descent to automatically improve +prompts, assuming access to training data and an LLM API. The algorithm uses +minibatches of data to form natural language ""gradients"" that criticize the +current prompt. The gradients are then ""propagated"" into the prompt by editing +the prompt in the opposite semantic direction of the gradient. These gradient +descent steps are guided by a beam search and bandit selection procedure which +significantly improves algorithmic efficiency. Preliminary results across three +benchmark NLP tasks and the novel problem of LLM jailbreak detection suggest +that Automatic Prompt Optimization can outperform prior prompt editing +techniques and improve an initial prompt's performance by up to 31%, by using +data to rewrite vague task descriptions into more precise annotation +instructions. +" +Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker,Sukmin Cho,http://arxiv.org/pdf/2305.13729v1.pdf,2023-05-23,"['cs.ir', 'cs.ai', 'cs.cl']",2305.13729v1.pdf," Re-rankers, which order retrieved documents with respect to the relevance +score on the given query, have gained attention for the information retrieval +(IR) task. Rather than fine-tuning the pre-trained language model (PLM), the +large-scale language model (LLM) is utilized as a zero-shot re-ranker with +excellent results. While LLM is highly dependent on the prompts, the impact and +the optimization of the prompts for the zero-shot re-ranker are not explored +yet. Along with highlighting the impact of optimization on the zero-shot +re-ranker, we propose a novel discrete prompt optimization method, Constrained +Prompt generation (Co-Prompt), with the metric estimating the optimum for +re-ranking. Co-Prompt guides the generated texts from PLM toward optimal +prompts based on the metric without parameter update. The experimental results +demonstrate that Co-Prompt leads to outstanding re-ranking performance against +the baselines. Also, Co-Prompt generates more interpretable prompts for humans +against other prompt optimization methods. +" +Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL,Hao Sun,http://arxiv.org/pdf/2309.06553v3.pdf,2023-09-13,"['cs.cl', 'cs.ai', 'cs.lg']",2309.06553v3.pdf," In this study, we aim to enhance the arithmetic reasoning ability of Large +Language Models (LLMs) through zero-shot prompt optimization. We identify a +previously overlooked objective of query dependency in such optimization and +elucidate two ensuing challenges that impede the successful and economical +design of prompt optimization techniques. One primary issue is the absence of +an effective method to evaluate prompts during inference when the golden answer +is unavailable. Concurrently, learning via interactions with the LLMs to +navigate the expansive natural language prompting space proves to be +resource-intensive. To address this, we introduce Prompt-OIRL, which harnesses +offline inverse reinforcement learning to draw insights from offline prompting +demonstration data. Such data exists as by-products when diverse prompts are +benchmarked on open-accessible datasets. With Prompt-OIRL, the query-dependent +prompt optimization objective is achieved by first learning an offline reward +model. This model can evaluate any query-prompt pairs without accessing LLMs. +Subsequently, a best-of-N strategy is deployed to recommend the optimal prompt. +Our experimental evaluations across various LLM scales and arithmetic reasoning +datasets underscore both the efficacy and economic viability of the proposed +approach. +" +ATT3D: Amortized Text-to-3D Object Synthesis,Jonathan Lorraine,http://arxiv.org/pdf/2306.07349v1.pdf,2023-06-06,"['cs.lg', 'cs.ai', 'cs.cv', '68t45', 'i.2.6; i.2.7; i.3.6; i.3.7']",2306.07349v1.pdf," Text-to-3D modelling has seen exciting progress by combining generative +text-to-image models with image-to-3D methods like Neural Radiance Fields. +DreamFusion recently achieved high-quality results but requires a lengthy, +per-prompt optimization to create 3D objects. To address this, we amortize +optimization over text prompts by training on many prompts simultaneously with +a unified model, instead of separately. With this, we share computation across +a prompt set, training in less time than per-prompt optimization. Our framework +- Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to +generalize to unseen setups and smooth interpolations between text for novel +assets and simple animations. +" +Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation,Chuyun Shen,http://arxiv.org/pdf/2306.08958v1.pdf,2023-06-15,"['cs.cv', 'cs.ai', 'cs.lg']",2306.08958v1.pdf," The Segmentation Anything Model (SAM) has recently emerged as a foundation +model for addressing image segmentation. Owing to the intrinsic complexity of +medical images and the high annotation cost, the medical image segmentation +(MIS) community has been encouraged to investigate SAM's zero-shot capabilities +to facilitate automatic annotation. Inspired by the extraordinary +accomplishments of interactive medical image segmentation (IMIS) paradigm, this +paper focuses on assessing the potential of SAM's zero-shot capabilities within +the IMIS paradigm to amplify its benefits in the MIS domain. Regrettably, we +observe that SAM's vulnerability to prompt forms (e.g., points, bounding boxes) +becomes notably pronounced in IMIS. This leads us to develop a framework that +adaptively offers suitable prompt forms for human experts. We refer to the +framework above as temporally-extended prompts optimization (TEPO) and model it +as a Markov decision process, solvable through reinforcement learning. +Numerical experiments on the standardized benchmark BraTS2020 demonstrate that +the learned TEPO agent can further enhance SAM's zero-shot capability in the +MIS context. +" +Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging,Ruben Glatt,http://arxiv.org/pdf/2306.17400v1.pdf,2023-06-30,"['cs.cv', '68t45', 'i.4.6']",2306.17400v1.pdf," Emerging foundation models in machine learning are models trained on vast +amounts of data that have been shown to generalize well to new tasks. Often +these models can be prompted with multi-modal inputs that range from natural +language descriptions over images to point clouds. In this paper, we propose +topological data analysis (TDA) guided prompt optimization for the Segment +Anything Model (SAM) and show preliminary results in the biological image +segmentation domain. Our approach replaces the standard grid search approach +that is used in the original implementation and finds point locations based on +their topological significance. Our results show that the TDA optimized point +cloud is much better suited for finding small objects and massively reduces +computational complexity despite the extra step in scenarios which require many +segmentations. +" +Emotion-Conditioned Text Generation through Automatic Prompt Optimization,Yarik Menchaca Resendiz,http://arxiv.org/pdf/2308.04857v1.pdf,2023-08-09,['cs.cl'],2308.04857v1.pdf," Conditional natural language generation methods often require either +expensive fine-tuning or training a large language model from scratch. Both are +unlikely to lead to good results without a substantial amount of data and +computational resources. Prompt learning without changing the parameters of a +large language model presents a promising alternative. It is a cost-effective +approach, while still achieving competitive results. While this procedure is +now established for zero- and few-shot text classification and structured +prediction, it has received limited attention in conditional text generation. +We present the first automatic prompt optimization approach for +emotion-conditioned text generation with instruction-fine-tuned models. Our +method uses an iterative optimization procedure that changes the prompt by +adding, removing, or replacing tokens. As objective function, we only require a +text classifier that measures the realization of the conditional variable in +the generated text. We evaluate the method on emotion-conditioned text +generation with a focus on event reports and compare it to manually designed +prompts that also act as the seed for the optimization procedure. The optimized +prompts achieve 0.75 macro-average F1 to fulfill the emotion condition in +contrast to manually designed seed prompts with only 0.22 macro-average F1. +" +Read-only Prompt Optimization for Vision-Language Few-shot Learning,Dongjun Lee,http://arxiv.org/pdf/2308.14960v1.pdf,2023-08-29,['cs.cv'],2308.14960v1.pdf," In recent years, prompt tuning has proven effective in adapting pre-trained +vision-language models to downstream tasks. These methods aim to adapt the +pre-trained models by introducing learnable prompts while keeping pre-trained +weights frozen. However, learnable prompts can affect the internal +representation within the self-attention module, which may negatively impact +performance variance and generalization, especially in data-deficient settings. +To address these issues, we propose a novel approach, Read-only Prompt +Optimization (RPO). RPO leverages masked attention to prevent the internal +representation shift in the pre-trained model. Further, to facilitate the +optimization of RPO, the read-only prompts are initialized based on special +tokens of the pre-trained model. Our extensive experiments demonstrate that RPO +outperforms CLIP and CoCoOp in base-to-new generalization and domain +generalization while displaying better robustness. Also, the proposed method +achieves better generalization on extremely data-deficient settings, while +improving parameter efficiency and computational overhead. Code is available at +https://github.com/mlvlab/RPO. +" +Large Language Models as Optimizers,Chengrun Yang,http://arxiv.org/pdf/2309.03409v1.pdf,2023-09-07,"['cs.lg', 'cs.ai', 'cs.cl']",2309.03409v1.pdf," Optimization is ubiquitous. While derivative-based algorithms have been +powerful tools for various problems, the absence of gradient imposes challenges +on many real-world applications. In this work, we propose Optimization by +PROmpting (OPRO), a simple and effective approach to leverage large language +models (LLMs) as optimizers, where the optimization task is described in +natural language. In each optimization step, the LLM generates new solutions +from the prompt that contains previously generated solutions with their values, +then the new solutions are evaluated and added to the prompt for the next +optimization step. We first showcase OPRO on linear regression and traveling +salesman problems, then move on to prompt optimization where the goal is to +find instructions that maximize the task accuracy. With a variety of LLMs, we +demonstrate that the best prompts optimized by OPRO outperform human-designed +prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks. +" +Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers,Qingyan Guo,http://arxiv.org/pdf/2309.08532v1.pdf,2023-09-15,"['cs.cl', 'cs.ai']",2309.08532v1.pdf," Large Language Models (LLMs) excel in various tasks, but they rely on +carefully crafted prompts that often demand substantial human effort. To +automate this process, in this paper, we propose a novel framework for discrete +prompt optimization, called EvoPrompt, which borrows the idea of evolutionary +algorithms (EAs) as they exhibit good performance and fast convergence. To +enable EAs to work on discrete prompts, which are natural language expressions +that need to be coherent and human-readable, we connect LLMs with EAs. This +approach allows us to simultaneously leverage the powerful language processing +capabilities of LLMs and the efficient optimization performance of EAs. +Specifically, abstaining from any gradients or parameters, EvoPrompt starts +from a population of prompts and iteratively generates new prompts with LLMs +based on the evolutionary operators, improving the population based on the +development set. We optimize prompts for both closed- and open-source LLMs +including GPT-3.5 and Alpaca, on 9 datasets spanning language understanding and +generation tasks. EvoPrompt significantly outperforms human-engineered prompts +and existing methods for automatic prompt generation by up to 25% and 14% +respectively. Furthermore, EvoPrompt demonstrates that connecting LLMs with EAs +creates synergies, which could inspire further research on the combination of +LLMs and conventional algorithms. +" +Black-Box Prompt Optimization: Aligning Large Language Models without Model Training,Jiale Cheng,http://arxiv.org/pdf/2311.04155v2.pdf,2023-11-07,['cs.cl'],2311.04155v2.pdf," Large language models (LLMs) have shown impressive success in various +applications. However, these models are often not well aligned with human +intents, which calls for additional treatments on them, that is, the alignment +problem. To make LLMs better follow user instructions, existing alignment +methods mostly focus on further training them. However, the extra training of +LLMs are usually expensive in terms of GPU compute; worse still, LLMs of +interest are oftentimes not accessible for user-demanded training, such as +GPTs. In this work, we take a different perspective -- Black-Box Prompt +Optimization (BPO) -- to perform alignments. The idea is to optimize user +prompts to suit LLMs' input understanding, so as to best realize users' intents +without updating LLMs' parameters. BPO is model-agnostic and the empirical +results demonstrate that the BPO-aligned ChatGPT yields a 22% increase in the +win rate against its original version, and 10% for GPT-4. Importantly, the +BPO-aligned LLMs can outperform the same models aligned by PPO and DPO, and it +also brings additional performance gains when combining BPO with PPO or DPO. +Code and datasets are released at https://github.com/thu-coai/BPO. +" +In-context Examples Selection for Machine Translation,Sweta Agrawal,http://arxiv.org/pdf/2212.02437v1.pdf,2022-12-05,['cs.cl'],2212.02437v1.pdf," Large-scale generative models show an impressive ability to perform a wide +range of Natural Language Processing (NLP) tasks using in-context learning, +where a few examples are used to describe a task to the model. For Machine +Translation (MT), these examples are typically randomly sampled from the +development dataset with a similar distribution as the evaluation set. However, +it is unclear how the choice of these in-context examples and their ordering +impacts the output translation quality. In this work, we aim to understand the +properties of good in-context examples for MT in both in-domain and +out-of-domain settings. We show that the translation quality and the domain of +the in-context examples matter and that 1-shot noisy unrelated example can have +a catastrophic impact on output quality. While concatenating multiple random +examples reduces the effect of noise, a single good prompt optimized to +maximize translation quality on the development dataset can elicit learned +information from the pre-trained language model. Adding similar examples based +on an n-gram overlap with the test source significantly and consistently +improves the translation quality of the outputs, outperforming a strong kNN-MT +baseline in 2 out of 4 out-of-domain datasets. +" +ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts,Kwanyoung Kim,http://arxiv.org/pdf/2301.12171v2.pdf,2023-01-28,"['cs.cv', 'cs.ai', 'cs.lg', 'stat.ml']",2301.12171v2.pdf," Recent success of large-scale Contrastive Language-Image Pre-training (CLIP) +has led to great promise in zero-shot semantic segmentation by transferring +image-text aligned knowledge to pixel-level classification. However, existing +methods usually require an additional image encoder or retraining/tuning the +CLIP module. Here, we propose a novel Zero-shot segmentation with Optimal +Transport (ZegOT) method that matches multiple text prompts with frozen image +embeddings through optimal transport. In particular, we introduce a novel +Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an +optimal mapping between multiple text prompts and visual feature maps of the +frozen image encoder hidden layers. This unique mapping method facilitates each +of the multiple text prompts to effectively focus on distinct visual semantic +attributes. Through extensive experiments on benchmark datasets, we show that +our method achieves the state-of-the-art (SOTA) performance over existing +Zero-shot Semantic Segmentation (ZS3) approaches. +" +DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation,Yueming Lyu,http://arxiv.org/pdf/2303.06285v1.pdf,2023-03-11,['cs.cv'],2303.06285v1.pdf," Text-driven image manipulation remains challenging in training or inference +flexibility. Conditional generative models depend heavily on expensive +annotated training data. Meanwhile, recent frameworks, which leverage +pre-trained vision-language models, are limited by either per text-prompt +optimization or inference-time hyper-parameters tuning. In this work, we +propose a novel framework named \textit{DeltaEdit} to address these problems. +Our key idea is to investigate and identify a space, namely delta image and +text space that has well-aligned distribution between CLIP visual feature +differences of two images and CLIP textual embedding differences of source and +target texts. Based on the CLIP delta space, the DeltaEdit network is designed +to map the CLIP visual features differences to the editing directions of +StyleGAN at training phase. Then, in inference phase, DeltaEdit predicts the +StyleGAN's editing directions from the differences of the CLIP textual +features. In this way, DeltaEdit is trained in a text-free manner. Once +trained, it can well generalize to various text prompts for zero-shot inference +without bells and whistles. Code is available at +https://github.com/Yueming6568/DeltaEdit. +" +Deep Language Networks: Joint Prompt Training of Stacked LLMs using Variational Inference,Alessandro Sordoni,http://arxiv.org/pdf/2306.12509v1.pdf,2023-06-21,"['cs.cl', 'cs.lg']",2306.12509v1.pdf," We view large language models (LLMs) as stochastic \emph{language layers} in +a network, where the learnable parameters are the natural language +\emph{prompts} at each layer. We stack two such layers, feeding the output of +one layer to the next. We call the stacked architecture a \emph{Deep Language +Network} (DLN). We first show how to effectively perform prompt optimization +for a 1-Layer language network (DLN-1). We then show how to train 2-layer DLNs +(DLN-2), where two prompts must be learnt. We consider the output of the first +layer as a latent variable to marginalize, and devise a variational inference +algorithm for joint prompt training. A DLN-2 reaches higher performance than a +single layer, sometimes comparable to few-shot GPT-4 even when each LLM in the +network is smaller and less powerful. The DLN code is open source: +https://github.com/microsoft/deep-language-networks . +" +Unnatural language processing: How do language models handle machine-generated prompts?,Corentin Kervadec,http://arxiv.org/pdf/2310.15829v1.pdf,2023-10-24,['cs.cl'],2310.15829v1.pdf," Language model prompt optimization research has shown that semantically and +grammatically well-formed manually crafted prompts are routinely outperformed +by automatically generated token sequences with no apparent meaning or +syntactic structure, including sequences of vectors from a model's embedding +space. We use machine-generated prompts to probe how models respond to input +that is not composed of natural language expressions. We study the behavior of +models of different sizes in multiple semantic tasks in response to both +continuous and discrete machine-generated prompts, and compare it to the +behavior in response to human-generated natural-language prompts. Even when +producing a similar output, machine-generated and human prompts trigger +different response patterns through the network processing pathways, including +different perplexities, different attention and output entropy distributions, +and different unit activation profiles. We provide preliminary insight into the +nature of the units activated by different prompt types, suggesting that only +natural language prompts recruit a genuinely linguistic circuit. +" +Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models,Paul Youssef,http://arxiv.org/pdf/2310.16570v1.pdf,2023-10-25,['cs.cl'],2310.16570v1.pdf," Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich +in world knowledge. This fact has sparked the interest of the community in +quantifying the amount of factual knowledge present in PLMs, as this explains +their performance on downstream tasks, and potentially justifies their use as +knowledge bases. In this work, we survey methods and datasets that are used to +probe PLMs for factual knowledge. Our contributions are: (1) We propose a +categorization scheme for factual probing methods that is based on how their +inputs, outputs and the probed PLMs are adapted; (2) We provide an overview of +the datasets used for factual probing; (3) We synthesize insights about +knowledge retention and prompt optimization in PLMs, analyze obstacles to +adopting PLMs as knowledge bases and outline directions for future work. +" +Task-driven Prompt Evolution for Foundation Models,Rachana Sathish,http://arxiv.org/pdf/2310.17128v1.pdf,2023-10-26,['cs.cv'],2310.17128v1.pdf," Promptable foundation models, particularly Segment Anything Model (SAM), have +emerged as a promising alternative to the traditional task-specific supervised +learning for image segmentation. However, many evaluation studies have found +that their performance on medical imaging modalities to be underwhelming +compared to conventional deep learning methods. In the world of large +pre-trained language and vision-language models, learning prompt from +downstream tasks has achieved considerable success in improving performance. In +this work, we propose a plug-and-play Prompt Optimization Technique for +foundation models like SAM (SAMPOT) that utilizes the downstream segmentation +task to optimize the human-provided prompt to obtain improved performance. We +demonstrate the utility of SAMPOT on lung segmentation in chest X-ray images +and obtain an improvement on a significant number of cases ($\sim75\%$) over +human-provided initial prompts. We hope this work will lead to further +investigations in the nascent field of automatic visual prompt-tuning. +" +RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning,Mingkai Deng,http://arxiv.org/pdf/2205.12548v3.pdf,2022-05-25,"['cs.cl', 'cs.lg']",2205.12548v3.pdf," Prompting has shown impressive success in enabling large pretrained language +models (LMs) to perform diverse NLP tasks, especially when only few downstream +data are available. Automatically finding the optimal prompt for each task, +however, is challenging. Most existing work resorts to tuning soft prompt +(e.g., embeddings) which falls short of interpretability, reusability across +LMs, and applicability when gradients are not accessible. Discrete prompt, on +the other hand, is difficult to optimize, and is often created by ""enumeration +(e.g., paraphrasing)-then-selection"" heuristics that do not explore the prompt +space systematically. This paper proposes RLPrompt, an efficient discrete +prompt optimization approach with reinforcement learning (RL). RLPrompt +formulates a parameter-efficient policy network that generates the desired +discrete prompt after training with reward. To overcome the complexity and +stochasticity of reward signals by the large LM environment, we incorporate +effective reward stabilization that substantially enhances the training +efficiency. RLPrompt is flexibly applicable to different types of LMs, such as +masked (e.g., BERT) and left-to-right models (e.g., GPTs), for both +classification and generation tasks. Experiments on few-shot classification and +unsupervised text style transfer show superior performance over a wide range of +existing finetuning or prompting methods. Interestingly, the resulting +optimized prompts are often ungrammatical gibberish text; and surprisingly, +those gibberish prompts are transferrable between different LMs to retain +significant performance, indicating LM prompting may not follow human language +patterns. +" +Diversity-Aware Meta Visual Prompting,Qidong Huang,http://arxiv.org/pdf/2303.08138v1.pdf,2023-03-14,['cs.cv'],2303.08138v1.pdf," We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and +effective prompting method for transferring pre-trained models to downstream +tasks with frozen backbone. A challenging issue in visual prompting is that +image datasets sometimes have a large data diversity whereas a per-dataset +generic prompt can hardly handle the complex distribution shift toward the +original pretraining data distribution properly. To address this issue, we +propose a dataset Diversity-Aware prompting strategy whose initialization is +realized by a Meta-prompt. Specifically, we cluster the downstream dataset into +small homogeneity subsets in a diversity-adaptive way, with each subset has its +own prompt optimized separately. Such a divide-and-conquer design reduces the +optimization difficulty greatly and significantly boosts the prompting +performance. Furthermore, all the prompts are initialized with a meta-prompt, +which is learned across several datasets. It is a bootstrapped paradigm, with +the key observation that the prompting knowledge learned from previous datasets +could help the prompt to converge faster and perform better on a new dataset. +During inference, we dynamically select a proper prompt for each input, based +on the feature distance between the input and each subset. Through extensive +experiments, our DAM-VP demonstrates superior efficiency and effectiveness, +clearly surpassing previous prompting methods in a series of downstream +datasets for different pretraining models. Our code is available at: +\url{https://github.com/shikiw/DAM-VP}. +" +DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning,Xiaocheng Lu,http://arxiv.org/pdf/2305.01239v1.pdf,2023-05-02,"['cs.cv', 'cs.ai']",2305.01239v1.pdf," Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts +composed of known knowledge without training samples. Standard CZSL either +identifies visual primitives or enhances unseen composed entities, and as a +result, entanglement between state and object primitives cannot be fully +utilized. Admittedly, vision-language models (VLMs) could naturally cope with +CZSL through tuning prompts, while uneven entanglement leads prompts to be +dragged into local optimum. In this paper, we take a further step to introduce +a novel Disentangled and Recurrent Prompt Tuning framework termed DRPT to +better tap the potential of VLMs in CZSL. Specifically, the state and object +primitives are deemed as learnable tokens of vocabulary embedded in prompts and +tuned on seen compositions. Instead of jointly tuning state and object, we +devise a disentangled and recurrent tuning strategy to suppress the traction +force caused by entanglement and gradually optimize the token parameters, +leading to a better prompt space. Notably, we develop a progressive fine-tuning +procedure that allows for incremental updates to the prompts, optimizing the +object first, then the state, and vice versa. Meanwhile, the optimization of +state and object is independent, thus clearer features can be learned to +further alleviate the issue of entangling misleading optimization. Moreover, we +quantify and analyze the entanglement in CZSL and supplement entanglement +rebalancing optimization schemes. DRPT surpasses representative +state-of-the-art methods on extensive benchmark datasets, demonstrating +superiority in both accuracy and efficiency. +" +Getting MoRE out of Mixture of Language Model Reasoning Experts,Chenglei Si,http://arxiv.org/pdf/2305.14628v2.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14628v2.pdf," While recent large language models (LLMs) improve on various question +answering (QA) datasets, it remains difficult for a single model to generalize +across question types that require distinct reasoning abilities. We provide +empirical evidence that state-of-the-art LLMs suffer from poor generalizability +on reasoning types beyond those seen in the prompt. To remedy this, we propose +a Mixture-of-Reasoning-Experts (MoRE) framework that ensembles diverse +specialized language models. We specialize the backbone language model with +prompts optimized for different reasoning categories, including factual, +multihop, mathematical, and commonsense reasoning. Our key insight is to +leverage agreement among the specialized experts to select the best answer for +each question, or to abstain from answering. This gives MoRE higher accuracy +than any single specialized model on a collection of 12 QA datasets from four +reasoning types. Beyond generalizability, the interpretable design of MoRE +improves selective question answering results compared to baselines without +incorporating inter-expert agreement. This framework is also more interpretable +and useful to human consumers of QA outputs. Our human study confirms that +presenting expert predictions and the answer selection process helps annotators +more accurately calibrate when to trust the system's output. We release all +code and data to facilitate future work. +" +Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media,Chuanbo Hu,http://arxiv.org/pdf/2307.03699v1.pdf,2023-07-07,"['cs.cl', 'cs.ai', 'cs.si']",2307.03699v1.pdf," Social media platforms such as Instagram and Twitter have emerged as critical +channels for drug marketing and illegal sale. Detecting and labeling online +illicit drug trafficking activities becomes important in addressing this issue. +However, the effectiveness of conventional supervised learning methods in +detecting drug trafficking heavily relies on having access to substantial +amounts of labeled data, while data annotation is time-consuming and +resource-intensive. Furthermore, these models often face challenges in +accurately identifying trafficking activities when drug dealers use deceptive +language and euphemisms to avoid detection. To overcome this limitation, we +conduct the first systematic study on leveraging large language models (LLMs), +such as ChatGPT, to detect illicit drug trafficking activities on social media. +We propose an analytical framework to compose \emph{knowledge-informed +prompts}, which serve as the interface that humans can interact with and use +LLMs to perform the detection task. Additionally, we design a Monte Carlo +dropout based prompt optimization method to further to improve performance and +interpretability. Our experimental findings demonstrate that the proposed +framework outperforms other baseline language models in terms of drug +trafficking detection accuracy, showing a remarkable improvement of nearly +12\%. By integrating prior knowledge and the proposed prompts, ChatGPT can +effectively identify and label drug trafficking activities on social networks, +even in the presence of deceptive language and euphemisms used by drug dealers +to evade detection. The implications of our research extend to social networks, +emphasizing the importance of incorporating prior knowledge and scenario-based +prompts into analytical tools to improve online security and public safety. +" +AutoHint: Automatic Prompt Optimization with Hint Generation,Hong Sun,http://arxiv.org/pdf/2307.07415v2.pdf,2023-07-13,"['cs.cl', 'cs.ai']",2307.07415v2.pdf," This paper presents AutoHint, a novel framework for automatic prompt +engineering and optimization for Large Language Models (LLM). While LLMs have +demonstrated remarkable ability in achieving high-quality annotation in various +tasks, the key to applying this ability to specific tasks lies in developing +high-quality prompts. Thus we propose a framework to inherit the merits of both +in-context learning and zero-shot learning by incorporating enriched +instructions derived from input-output demonstrations to optimize original +prompt. We refer to the enrichment as the hint and propose a framework to +automatically generate the hint from labeled data. More concretely, starting +from an initial prompt, our method first instructs a LLM to deduce new hints +for selected samples from incorrect predictions, and then summarizes from +per-sample hints and adds the results back to the initial prompt to form a new, +enriched instruction. The proposed method is evaluated on the BIG-Bench +Instruction Induction dataset for both zero-shot and few-short prompts, where +experiments demonstrate our method is able to significantly boost accuracy for +multiple tasks. +" +"Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study",Yinqiu Liu,http://arxiv.org/pdf/2309.01065v1.pdf,2023-09-03,['cs.ni'],2309.01065v1.pdf," As the next-generation paradigm for content creation, AI-Generated Content +(AIGC), i.e., generating content automatically by Generative AI (GAI) based on +user prompts, has gained great attention and success recently. With the +ever-increasing power of GAI, especially the emergence of Pretrained Foundation +Models (PFMs) that contain billions of parameters and prompt engineering +methods (i.e., finding the best prompts for the given task), the application +range of AIGC is rapidly expanding, covering various forms of information for +human, systems, and networks, such as network designs, channel coding, and +optimization solutions. In this article, we present the concept of mobile-edge +AI-Generated Everything (AIGX). Specifically, we first review the building +blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX +applications. Then, we present a unified mobile-edge AIGX framework, which +employs edge devices to provide PFM-empowered AIGX services and optimizes such +services via prompt engineering. More importantly, we demonstrate that +suboptimal prompts lead to poor generation quality, which adversely affects +user satisfaction, edge network performance, and resource utilization. +Accordingly, we conduct a case study, showcasing how to train an effective +prompt optimizer using ChatGPT and investigating how much improvement is +possible with prompt engineering in terms of user experience, quality of +generation, and network performance. +" +Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data,Ankita Sharma,http://arxiv.org/pdf/2309.01957v2.pdf,2023-09-05,['cs.db'],2309.01957v2.pdf," Existing approaches to automatic data transformation are insufficient to meet +the requirements in many real-world scenarios, such as the building sector. +First, there is no convenient interface for domain experts to provide domain +knowledge easily. Second, they require significant training data collection +overheads. Third, the accuracy suffers from complicated schema changes. To +bridge this gap, we present a novel approach that leverages the unique +capabilities of large language models (LLMs) in coding, complex reasoning, and +zero-shot learning to generate SQL code that transforms the source datasets +into the target datasets. We demonstrate the viability of this approach by +designing an LLM-based framework, termed SQLMorpher, which comprises a prompt +generator that integrates the initial prompt with optional domain knowledge and +historical patterns in external databases. It also implements an iterative +prompt optimization mechanism that automatically improves the prompt based on +flaw detection. The key contributions of this work include (1) pioneering an +end-to-end LLM-based solution for data transformation, (2) developing a +benchmark dataset of 105 real-world building energy data transformation +problems, and (3) conducting an extensive empirical evaluation where our +approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the +effectiveness of utilizing LLMs in complex, domain-specific challenges, +highlighting the potential of their potential to drive sustainable solutions. +" +Automatic Prompt Rewriting for Personalized Text Generation,Cheng Li,http://arxiv.org/pdf/2310.00152v1.pdf,2023-09-29,['cs.cl'],2310.00152v1.pdf," Facilitated by large language models (LLMs), personalized text generation has +become a rapidly growing research direction. Most existing studies focus on +designing specialized models for a particular domain, or they require +fine-tuning the LLMs to generate personalized text. We consider a typical +scenario in which the large language model, which generates personalized +output, is frozen and can only be accessed through APIs. Under this constraint, +all one can do is to improve the input text (i.e., text prompts) sent to the +LLM, a procedure that is usually done manually. In this paper, we propose a +novel method to automatically revise prompts for personalized text generation. +The proposed method takes the initial prompts generated by a state-of-the-art, +multistage framework for personalized generation and rewrites a few critical +components that summarize and synthesize the personal context. The prompt +rewriter employs a training paradigm that chains together supervised learning +(SL) and reinforcement learning (RL), where SL reduces the search space of RL +and RL facilitates end-to-end training of the rewriter. Using datasets from +three representative domains, we demonstrate that the rewritten prompts +outperform both the original prompts and the prompts optimized via supervised +learning or reinforcement learning alone. In-depth analysis of the rewritten +prompts shows that they are not only human readable, but also able to guide +manual revision of prompts when there is limited resource to employ +reinforcement learning to train the prompt rewriter, or when it is costly to +deploy an automatic prompt rewriter for inference. +" +DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing,Yueming Lyu,http://arxiv.org/pdf/2310.08785v1.pdf,2023-10-12,"['cs.cv', 'cs.ai']",2310.08785v1.pdf," Text-guided image editing faces significant challenges to training and +inference flexibility. Much literature collects large amounts of annotated +image-text pairs to train text-conditioned generative models from scratch, +which is expensive and not efficient. After that, some approaches that leverage +pre-trained vision-language models are put forward to avoid data collection, +but they are also limited by either per text-prompt optimization or +inference-time hyper-parameters tuning. To address these issues, we investigate +and identify a specific space, referred to as CLIP DeltaSpace, where the CLIP +visual feature difference of two images is semantically aligned with the CLIP +textual feature difference of their corresponding text descriptions. Based on +DeltaSpace, we propose a novel framework called DeltaEdit, which maps the CLIP +visual feature differences to the latent space directions of a generative model +during the training phase, and predicts the latent space directions from the +CLIP textual feature differences during the inference phase. And this design +endows DeltaEdit with two advantages: (1) text-free training; (2) +generalization to various text prompts for zero-shot inference. Extensive +experiments validate the effectiveness and versatility of DeltaEdit with +different generative models, including both the GAN model and the diffusion +model, in achieving flexible text-guided image editing. Code is available at +https://github.com/Yueming6568/DeltaEdit. +" +InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image,Jianhui Li,http://arxiv.org/pdf/2311.02826v1.pdf,2023-11-06,['cs.cv'],2311.02826v1.pdf," With the success of Neural Radiance Field (NeRF) in 3D-aware portrait +editing, a variety of works have achieved promising results regarding both +quality and 3D consistency. However, these methods heavily rely on per-prompt +optimization when handling natural language as editing instructions. Due to the +lack of labeled human face 3D datasets and effective architectures, the area of +human-instructed 3D-aware editing for open-world portraits in an end-to-end +manner remains under-explored. To solve this problem, we propose an end-to-end +diffusion-based framework termed InstructPix2NeRF, which enables instructed +3D-aware portrait editing from a single open-world image with human +instructions. At its core lies a conditional latent 3D diffusion process that +lifts 2D editing to 3D space by learning the correlation between the paired +images' difference and the instructions via triplet data. With the help of our +proposed token position randomization strategy, we could even achieve +multi-semantic editing through one single pass with the portrait identity +well-preserved. Besides, we further propose an identity consistency module that +directly modulates the extracted identity signals into our diffusion process, +which increases the multi-view 3D identity consistency. Extensive experiments +verify the effectiveness of our method and show its superiority against strong +baselines quantitatively and qualitatively. +" +What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers,Boseop Kim,http://arxiv.org/pdf/2109.04650v2.pdf,2021-09-10,['cs.cl'],2109.04650v2.pdf," GPT-3 shows remarkable in-context learning ability of large-scale language +models (LMs) trained on hundreds of billion scale data. Here we address some +remaining issues less reported by the GPT-3 paper, such as a non-English LM, +the performances of different sized models, and the effect of recently +introduced prompt optimization on in-context learning. To achieve this, we +introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric +corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA +with our training configuration shows state-of-the-art in-context zero-shot and +few-shot learning performances on various downstream tasks in Korean. Also, we +show the performance benefits of prompt-based learning and demonstrate how it +can be integrated into the prompt engineering pipeline. Then we discuss the +possibility of materializing the No Code AI paradigm by providing AI +prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio, +an interactive prompt engineering interface. Lastly, we demonstrate the +potential of our methods with three successful in-house applications. +" +MLLM-DataEngine: An Iterative Refinement Approach for MLLM,Zhiyuan Zhao,http://arxiv.org/pdf/2308.13566v2.pdf,2023-08-25,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cv']",2308.13566v2.pdf," Despite the great advance of Multimodal Large Language Models (MLLMs) in both +instruction dataset building and benchmarking, the independence of training and +evaluation makes current MLLMs hard to further improve their capability under +the guidance of evaluation results with a relatively low human cost. In this +paper, we propose MLLM-DataEngine, a novel closed-loop system that bridges data +generation, model training, and evaluation. Within each loop iteration, the +MLLM-DataEngine first analyze the weakness of the model based on the evaluation +results, then generate a proper incremental dataset for the next training +iteration and enhance the model capability iteratively. Compared with previous +data collection methods which are separate from the benchmarking, the data +generated by MLLM-DataEngine shows better targeting, quality, and correctness. +For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts +the ratio of different types of data within each incremental dataset based on +the benchmarking results. For quality, we resort to GPT-4 to generate +high-quality data with each given data type. For correctness, prompt design is +critical for the data generation results. Rather than previous hand-crafted +prompt, we propose an Interactive Prompt Optimization strategy, which optimizes +the prompt with the multi-round interaction between human and GPT, and improve +the correctness of generated data greatly. Through extensive experiments, we +find our MLLM-DataEngine could boost the MLLM capability in a targeted and +automatic manner, with only a few human participation. We hope it could be a +general solution for the following MLLMs building. The MLLM-DataEngine has been +open-sourced and is now available at +https://github.com/opendatalab/MLLM-DataEngine. +" +Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review,Banghao Chen,http://arxiv.org/pdf/2310.14735v2.pdf,2023-10-23,"['cs.cl', 'cs.ai', 'i.2.7']",2310.14735v2.pdf," This paper delves into the pivotal role of prompt engineering in unleashing +the capabilities of Large Language Models (LLMs). Prompt engineering is the +process of structuring input text for LLMs and is a technique integral to +optimizing the efficacy of LLMs. This survey elucidates foundational principles +of prompt engineering, such as role-prompting, one-shot, and few-shot +prompting, as well as more advanced methodologies such as the chain-of-thought +and tree-of-thoughts prompting. The paper sheds light on how external +assistance in the form of plugins can assist in this task, and reduce machine +hallucination by retrieving external knowledge. We subsequently delineate +prospective directions in prompt engineering research, emphasizing the need for +a deeper understanding of structures and the role of agents in Artificial +Intelligence-Generated Content (AIGC) tools. We discuss how to assess the +efficacy of prompt methods from different perspectives and using different +methods. Finally, we gather information about the application of prompt +engineering in such fields as education and programming, showing its +transformative potential. This comprehensive survey aims to serve as a friendly +guide for anyone venturing through the big world of LLMs and prompt +engineering. +" +Prompt Engineering For Students of Medicine and Their Teachers,Thomas F. Heston,http://arxiv.org/pdf/2308.11628v1.pdf,2023-08-08,['cs.hc'],2308.11628v1.pdf," ""Prompt Engineering for Students of Medicine and Their Teachers"" brings the +principles of prompt engineering for large language models such as ChatGPT and +Google Bard to medical education. This book contains a comprehensive guide to +prompt engineering to help both teachers and students improve education in the +medical field. Just as prompt engineering is critical in getting good +information out of an AI, it is also critical to get students to think and +understand more deeply. The principles of prompt engineering that we have +learned from AI systems have the potential to simultaneously revolutionize +learning in the healthcare field. The book analyzes from multiple angles the +anatomy of a good prompt for both AI models and students. The different types +of prompts are examined, showing how each style has unique characteristics and +applications. The principles of prompt engineering, applied properly, are +demonstrated to be effective in teaching across the diverse fields of anatomy, +physiology, pathology, pharmacology, and clinical skills. Just like ChatGPT and +similar large language AI models, students need clear and detailed prompting in +order for them to fully understand a topic. Using identical principles, a +prompt that gets good information from an AI will also cause a student to think +more deeply and accurately. The process of prompt engineering facilitates this +process. Because each chapter contains multiple examples and key takeaways, it +is a practical guide for implementing prompt engineering in the learning +process. It provides a hands-on approach to ensure readers can immediately +apply the concepts they learn +" +Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering,Jonas Oppenlaender,http://arxiv.org/pdf/2303.13534v1.pdf,2023-03-13,"['cs.hc', 'h.m']",2303.13534v1.pdf," Humankind is entering a novel era of creativity - an era in which anybody can +synthesize digital content. The paradigm under which this revolution takes +place is prompt-based learning (or in-context learning). This paradigm has +found fruitful application in text-to-image generation where it is being used +to synthesize digital images from zero-shot text prompts in natural language +for the purpose of creating AI art. This activity is referred to as prompt +engineering - the practice of iteratively crafting prompts to generate and +improve images. In this paper, we investigate prompt engineering as a novel +creative skill for creating prompt-based art. In three studies with +participants recruited from a crowdsourcing platform, we explore whether +untrained participants could 1) recognize the quality of prompts, 2) write +prompts, and 3) improve their prompts. Our results indicate that participants +could assess the quality of prompts and respective images. This ability +increased with the participants' experience and interest in art. Participants +further were able to write prompts in rich descriptive language. However, even +though participants were specifically instructed to generate artworks, +participants' prompts were missing the specific vocabulary needed to apply a +certain style to the generated images. Our results suggest that prompt +engineering is a learned skill that requires expertise and practice. Based on +our findings and experience with running our studies with participants +recruited from a crowdsourcing platform, we provide ten recommendations for +conducting experimental research on text-to-image generation and prompt +engineering with a paid crowd. Our studies offer a deeper understanding of +prompt engineering thereby opening up avenues for research on the future of +prompt engineering. We conclude by speculating on four possible futures of +prompt engineering. +" +Review of Large Vision Models and Visual Prompt Engineering,Jiaqi Wang,http://arxiv.org/pdf/2307.00855v1.pdf,2023-07-03,"['cs.cv', 'cs.ai']",2307.00855v1.pdf," Visual prompt engineering is a fundamental technology in the field of visual +and image Artificial General Intelligence, serving as a key component for +achieving zero-shot capabilities. As the development of large vision models +progresses, the importance of prompt engineering becomes increasingly evident. +Designing suitable prompts for specific visual tasks has emerged as a +meaningful research direction. This review aims to summarize the methods +employed in the computer vision domain for large vision models and visual +prompt engineering, exploring the latest advancements in visual prompt +engineering. We present influential large models in the visual domain and a +range of prompt engineering methods employed on these models. It is our hope +that this review provides a comprehensive and systematic description of prompt +engineering methods based on large visual models, offering valuable insights +for future researchers in their exploration of this field. +" +Prompt Engineering for Healthcare: Methodologies and Applications,Jiaqi Wang,http://arxiv.org/pdf/2304.14670v1.pdf,2023-04-28,['cs.ai'],2304.14670v1.pdf," This review will introduce the latest advances in prompt engineering in the +field of natural language processing (NLP) for the medical domain. First, we +will provide a brief overview of the development of prompt engineering and +emphasize its significant contributions to healthcare NLP applications such as +question-answering systems, text summarization, and machine translation. With +the continuous improvement of general large language models, the importance of +prompt engineering in the healthcare domain is becoming increasingly prominent. +The aim of this article is to provide useful resources and bridges for +healthcare NLP researchers to better explore the application of prompt +engineering in this field. We hope that this review can provide new ideas and +inspire ample possibilities for research and application in medical NLP. +" +A Brief History of Prompt: Leveraging Language Models,Golam Md Muktadir,http://arxiv.org/pdf/2310.04438v1.pdf,2023-09-30,"['cs.cl', 'cs.ai']",2310.04438v1.pdf," This paper presents a comprehensive exploration of the evolution of prompt +engineering and generation in the field of natural language processing (NLP). +Starting from the early language models and information retrieval systems, we +trace the key developments that have shaped prompt engineering over the years. +The introduction of attention mechanisms in 2015 revolutionized language +understanding, leading to advancements in controllability and +context-awareness. Subsequent breakthroughs in reinforcement learning +techniques further enhanced prompt engineering, addressing issues like exposure +bias and biases in generated text. We examine the significant contributions in +2018 and 2019, focusing on fine-tuning strategies, control codes, and +template-based generation. The paper also discusses the growing importance of +fairness, human-AI collaboration, and low-resource adaptation. In 2020 and +2021, contextual prompting and transfer learning gained prominence, while 2022 +and 2023 witnessed the emergence of advanced techniques like unsupervised +pre-training and novel reward shaping. Throughout the paper, we reference +specific research studies that exemplify the impact of various developments on +prompt engineering. The journey of prompt engineering continues, with ethical +considerations being paramount for the responsible and inclusive future of AI +systems. +" +A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models,Jindong Gu,http://arxiv.org/pdf/2307.12980v1.pdf,2023-07-24,['cs.cv'],2307.12980v1.pdf," Prompt engineering is a technique that involves augmenting a large +pre-trained model with task-specific hints, known as prompts, to adapt the +model to new tasks. Prompts can be created manually as natural language +instructions or generated automatically as either natural language instructions +or vector representations. Prompt engineering enables the ability to perform +predictions based solely on prompts without updating model parameters, and the +easier application of large pre-trained models in real-world tasks. In past +years, Prompt engineering has been well-studied in natural language processing. +Recently, it has also been intensively studied in vision-language modeling. +However, there is currently a lack of a systematic overview of prompt +engineering on pre-trained vision-language models. This paper aims to provide a +comprehensive survey of cutting-edge research in prompt engineering on three +types of vision-language models: multimodal-to-text generation models (e.g. +Flamingo), image-text matching models (e.g. CLIP), and text-to-image generation +models (e.g. Stable Diffusion). For each type of model, a brief model summary, +prompting methods, prompting-based applications, and the corresponding +responsibility and integrity issues are summarized and discussed. Furthermore, +the commonalities and differences between prompting on vision-language models, +language models, and vision models are also discussed. The challenges, future +directions, and research opportunities are summarized to foster future research +on this topic. +" +Prompt Engineering and Calibration for Zero-Shot Commonsense Reasoning,Chenkai Ma,http://arxiv.org/pdf/2304.06962v1.pdf,2023-04-14,"['cs.cl', 'cs.ai']",2304.06962v1.pdf," Prompt engineering and calibration make large language models excel at +reasoning tasks, including multiple choice commonsense reasoning. From a +practical perspective, we investigate and evaluate these strategies on smaller +language models. Through experiments on five commonsense reasoning benchmarks, +we find that each strategy favors certain models, but their joint effects are +mostly negative. +" +Just Tell Me: Prompt Engineering in Business Process Management,Kiran Busch,http://arxiv.org/pdf/2304.07183v1.pdf,2023-04-14,"['cs.ai', 'cs.cl', 'cs.lg']",2304.07183v1.pdf," GPT-3 and several other language models (LMs) can effectively address various +natural language processing (NLP) tasks, including machine translation and text +summarization. Recently, they have also been successfully employed in the +business process management (BPM) domain, e.g., for predictive process +monitoring and process extraction from text. This, however, typically requires +fine-tuning the employed LM, which, among others, necessitates large amounts of +suitable training data. A possible solution to this problem is the use of +prompt engineering, which leverages pre-trained LMs without fine-tuning them. +Recognizing this, we argue that prompt engineering can help bring the +capabilities of LMs to BPM research. We use this position paper to develop a +research agenda for the use of prompt engineering for BPM research by +identifying the associated potentials and challenges. +" +Revisiting Prompt Engineering via Declarative Crowdsourcing,Aditya G. Parameswaran,http://arxiv.org/pdf/2308.03854v1.pdf,2023-08-07,"['cs.db', 'cs.ai', 'cs.hc', 'cs.lg']",2308.03854v1.pdf," Large language models (LLMs) are incredibly powerful at comprehending and +generating data in the form of text, but are brittle and error-prone. There has +been an advent of toolkits and recipes centered around so-called prompt +engineering-the process of asking an LLM to do something via a series of +prompts. However, for LLM-powered data processing workflows, in particular, +optimizing for quality, while keeping cost bounded, is a tedious, manual +process. We put forth a vision for declarative prompt engineering. We view LLMs +like crowd workers and leverage ideas from the declarative crowdsourcing +literature-including leveraging multiple prompting strategies, ensuring +internal consistency, and exploring hybrid-LLM-non-LLM approaches-to make +prompt engineering a more principled process. Preliminary case studies on +sorting, entity resolution, and imputation demonstrate the promise of our +approach +" +How understanding large language models can inform their use in physics education,Giulia Polverini,http://arxiv.org/pdf/2309.12074v1.pdf,2023-09-21,['physics.ed-ph'],2309.12074v1.pdf," The paper aims to fulfil three main functions: (1) to serve as an +introduction for the physics education community to the functioning of Large +Language Models (LLMs), (2) to present a series of illustrative examples +demonstrating how prompt-engineering techniques can impact LLMs performance on +conceptual physics tasks and (3) to discuss potential implications of the +understanding of LLMs and prompt engineering for physics teaching and learning. +We first summarise existing research on the performance of a popular LLM-based +chatbot (ChatGPT) on physics tasks. We then give a basic account of how LLMs +work, illustrate essential features of their functioning, and discuss their +strengths and limitations. Equipped with this knowledge, we discuss some +challenges with generating useful output with ChatGPT-4 in the context of +introductory physics, paying special attention to conceptual questions and +problems. We then provide a condensed overview of relevant literature on prompt +engineering and demonstrate through illustrative examples how selected +prompt-engineering techniques can be employed to improve ChatGPT-4's output on +conceptual introductory physics problems. Qualitatively studying these examples +provides additional insights into ChatGPT's functioning and its utility in +physics problem solving. Finally, we consider how insights from the paper can +inform the use of LMMs in the teaching and learning of physics. +" +Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation,Seugnjun Lee,http://arxiv.org/pdf/2306.14514v2.pdf,2023-06-26,"['cs.cl', 'cs.ai']",2306.14514v2.pdf," In this paper, we introduce a data-driven approach for Formality-Sensitive +Machine Translation (FSMT) that caters to the unique linguistic properties of +four target languages. Our methodology centers on two core strategies: 1) +language-specific data handling, and 2) synthetic data generation using +large-scale language models and empirical prompt engineering. This approach +demonstrates a considerable improvement over the baseline, highlighting the +effectiveness of data-centric techniques. Our prompt engineering strategy +further improves performance by producing superior synthetic translation +examples. +" +Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineering,Edward Junprung,http://arxiv.org/pdf/2308.07411v1.pdf,2023-08-14,"['cs.ai', 'cs.ma']",2308.07411v1.pdf," The final frontier for simulation is the accurate representation of complex, +real-world social systems. While agent-based modeling (ABM) seeks to study the +behavior and interactions of agents within a larger system, it is unable to +faithfully capture the full complexity of human-driven behavior. Large language +models (LLMs), like ChatGPT, have emerged as a potential solution to this +bottleneck by enabling researchers to explore human-driven interactions in +previously unimaginable ways. Our research investigates simulations of human +interactions using LLMs. Through prompt engineering, inspired by Park et al. +(2023), we present two simulations of believable proxies of human behavior: a +two-agent negotiation and a six-agent murder mystery game. +" +Large Language Models Are Human-Level Prompt Engineers,Yongchao Zhou,http://arxiv.org/pdf/2211.01910v2.pdf,2022-11-03,"['cs.lg', 'cs.ai', 'cs.cl']",2211.01910v2.pdf," By conditioning on natural language instructions, large language models +(LLMs) have displayed impressive capabilities as general-purpose computers. +However, task performance depends significantly on the quality of the prompt +used to steer the model, and most effective prompts have been handcrafted by +humans. Inspired by classical program synthesis and the human approach to +prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic +instruction generation and selection. In our method, we treat the instruction +as the ""program,"" optimized by searching over a pool of instruction candidates +proposed by an LLM in order to maximize a chosen score function. To evaluate +the quality of the selected instruction, we evaluate the zero-shot performance +of another LLM following the selected instruction. Experiments on 24 NLP tasks +show that our automatically generated instructions outperform the prior LLM +baseline by a large margin and achieve better or comparable performance to the +instructions generated by human annotators on 19/24 tasks. We conduct extensive +qualitative and quantitative analyses to explore the performance of APE. We +show that APE-engineered prompts can be applied to steer models toward +truthfulness and/or informativeness, as well as to improve few-shot learning +performance by simply prepending them to standard in-context learning prompts. +Please check out our webpage at +https://sites.google.com/view/automatic-prompt-engineer. +" +Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales,Martin Ruskov,http://arxiv.org/pdf/2302.08961v2.pdf,2023-02-17,"['cs.cl', 'cs.ai', 'cs.hc', 'i.2']",2302.08961v2.pdf," The quality of text-to-image generation is continuously improving, yet the +boundaries of its applicability are still unclear. In particular, refinement of +the text input with the objective of achieving better results - commonly called +prompt engineering - so far seems to have not been geared towards work with +pre-existing texts. We investigate whether text-to-image generation and prompt +engineering could be used to generate basic illustrations of popular +fairytales. Using Midjourney v4, we engage in action research with a dual aim: +to attempt to generate 5 believable illustrations for each of 5 popular +fairytales, and to define a prompt engineering process that starts from a +pre-existing text and arrives at an illustration of it. We arrive at a +tentative 4-stage process: i) initial prompt, ii) composition adjustment, iii) +style refinement, and iv) variation selection. We also discuss three reasons +why the generation model struggles with certain illustrations: difficulties +with counts, bias from stereotypical configurations and inability to depict +overly fantastic situations. Our findings are not limited to the specific +generation model and are intended to be generalisable to future ones. +" +A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT,Jules White,http://arxiv.org/pdf/2302.11382v1.pdf,2023-02-21,"['cs.se', 'cs.ai']",2302.11382v1.pdf," Prompt engineering is an increasingly important skill set needed to converse +effectively with large language models (LLMs), such as ChatGPT. Prompts are +instructions given to an LLM to enforce rules, automate processes, and ensure +specific qualities (and quantities) of generated output. Prompts are also a +form of programming that can customize the outputs and interactions with an +LLM. This paper describes a catalog of prompt engineering techniques presented +in pattern form that have been applied to solve common problems when conversing +with LLMs. Prompt patterns are a knowledge transfer method analogous to +software patterns since they provide reusable solutions to common problems +faced in a particular context, i.e., output generation and interaction when +working with LLMs. This paper provides the following contributions to research +on prompt engineering that apply LLMs to automate software development tasks. +First, it provides a framework for documenting patterns for structuring prompts +to solve a range of problems so that they can be adapted to different domains. +Second, it presents a catalog of patterns that have been applied successfully +to improve the outputs of LLM conversations. Third, it explains how prompts can +be built from multiple patterns and illustrates prompt patterns that benefit +from combination with other prompt patterns. +" +Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models,Fobo Shi,http://arxiv.org/pdf/2306.03799v1.pdf,2023-06-06,['cs.cl'],2306.03799v1.pdf," Prompt engineering is an essential technique for enhancing the abilities of +large language models (LLMs) by providing explicit and specific instructions. +It enables LLMs to excel in various tasks, such as arithmetic reasoning, +question answering, summarization, relation extraction, machine translation, +and sentiment analysis. Researchers have been actively exploring different +prompt engineering strategies, such as Chain of Thought (CoT), Zero-CoT, and +In-context learning. However, an unresolved problem arises from the fact that +current approaches lack a solid theoretical foundation for determining optimal +prompts. To address this issue in prompt engineering, we propose a new and +effective approach called Prompt Space. Our methodology utilizes text +embeddings to obtain basis vectors by matrix decomposition, and then constructs +a space for representing all prompts. Prompt Space significantly outperforms +state-of-the-art prompt paradigms on ten public reasoning benchmarks. Notably, +without the help of the CoT method and the prompt ""Let's think step by step"", +Prompt Space shows superior performance over the few-shot method. Overall, our +approach provides a robust and fundamental theoretical framework for selecting +simple and effective prompts. This advancement marks a significant step towards +improving prompt engineering for a wide variety of applications in LLMs. +" +An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing,Sonish Sivarajkumar,http://arxiv.org/pdf/2309.08008v1.pdf,2023-09-14,"['cs.cl', 'cs.ai']",2309.08008v1.pdf," Large language models (LLMs) have shown remarkable capabilities in Natural +Language Processing (NLP), especially in domains where labeled data is scarce +or expensive, such as clinical domain. However, to unlock the clinical +knowledge hidden in these LLMs, we need to design effective prompts that can +guide them to perform specific clinical NLP tasks without any task-specific +training data. This is known as in-context learning, which is an art and +science that requires understanding the strengths and weaknesses of different +LLMs and prompt engineering approaches. In this paper, we present a +comprehensive and systematic experimental study on prompt engineering for five +clinical NLP tasks: Clinical Sense Disambiguation, Biomedical Evidence +Extraction, Coreference Resolution, Medication Status Extraction, and +Medication Attribute Extraction. We assessed the prompts proposed in recent +literature, including simple prefix, simple cloze, chain of thought, and +anticipatory prompts, and introduced two new types of prompts, namely heuristic +prompting and ensemble prompting. We evaluated the performance of these prompts +on three state-of-the-art LLMs: GPT-3.5, BARD, and LLAMA2. We also contrasted +zero-shot prompting with few-shot prompting, and provide novel insights and +guidelines for prompt engineering for LLMs in clinical NLP. To the best of our +knowledge, this is one of the first works on the empirical evaluation of +different prompt engineering approaches for clinical NLP in this era of +generative AI, and we hope that it will inspire and inform future research in +this area. +" +Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks,Jiho Shin,http://arxiv.org/pdf/2310.10508v1.pdf,2023-10-11,['cs.se'],2310.10508v1.pdf," In this paper, we investigate the effectiveness of state-of-the-art LLM, +i.e., GPT-4, with three different prompting engineering techniques (i.e., basic +prompting, in-context learning, and task-specific prompting) against 18 +fine-tuned LLMs on three typical ASE tasks, i.e., code generation, code +summarization, and code translation. Our quantitative analysis of these +prompting strategies suggests that prompt engineering GPT-4 cannot necessarily +and significantly outperform fine-tuning smaller/older LLMs in all three tasks. +For comment generation, GPT-4 with the best prompting strategy (i.e., +task-specific prompt) had outperformed the first-ranked fine-tuned model by +8.33% points on average in BLEU. However, for code generation, the first-ranked +fine-tuned model outperforms GPT-4 with best prompting by 16.61% and 28.3% +points, on average in BLEU. For code translation, GPT-4 and fine-tuned +baselines tie as they outperform each other on different translation tasks. To +explore the impact of different prompting strategies, we conducted a user study +with 27 graduate students and 10 industry practitioners. From our qualitative +analysis, we find that the GPT-4 with conversational prompts (i.e., when a +human provides feedback and instructions back and forth with a model to achieve +best results) showed drastic improvement compared to GPT-4 with automatic +prompting strategies. Moreover, we observe that participants tend to request +improvements, add more context, or give specific instructions as conversational +prompts, which goes beyond typical and generic prompting strategies. Our study +suggests that, at its current state, GPT-4 with conversational prompting has +great potential for ASE tasks, but fully automated prompt engineering with no +human in the loop requires more study and improvement. +" +An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels,Taylor Sorensen,http://arxiv.org/pdf/2203.11364v1.pdf,2022-03-21,"['cs.cl', 'cs.lg']",2203.11364v1.pdf," Pre-trained language models derive substantial linguistic and factual +knowledge from the massive corpora on which they are trained, and prompt +engineering seeks to align these models to specific tasks. Unfortunately, +existing prompt engineering methods require significant amounts of labeled +data, access to model parameters, or both. We introduce a new method for +selecting prompt templates \textit{without labeled examples} and +\textit{without direct access to the model}. Specifically, over a set of +candidate templates, we choose the template that maximizes the mutual +information between the input and the corresponding model output. Across 8 +datasets representing 7 distinct NLP tasks, we show that when a template has +high mutual information, it also has high accuracy on the task. On the largest +model, selecting prompts with our method gets 90\% of the way from the average +prompt accuracy to the best prompt accuracy and requires no ground truth +labels. +" +Unsupervised Prompt Learning for Vision-Language Models,Tony Huang,http://arxiv.org/pdf/2204.03649v2.pdf,2022-04-07,['cs.cv'],2204.03649v2.pdf," Contrastive vision-language models like CLIP have shown great progress in +transfer learning. In the inference stage, the proper text description, also +known as prompt, needs to be carefully designed to correctly classify the given +images. In order to avoid laborious prompt engineering, recent works such as +CoOp, CLIP-Adapter and Tip-Adapter propose to adapt vision-language models for +downstream image recognition tasks on a small set of labeled data. Though +promising improvements are achieved, requiring labeled data from the target +datasets may restrict the scalability. In this paper, we explore a different +scenario, in which the labels of the target datasets are unprovided, and we +present an unsupervised prompt learning (UPL) approach to avoid prompt +engineering while simultaneously improving transfer performance of CLIP-like +vision-language models. As far as we know, UPL is the first work to introduce +unsupervised learning into prompt learning. Experimentally, our UPL outperforms +original CLIP with prompt engineering on ImageNet as well as other 10 datasets. +An enhanced version of UPL is even competitive with the 8-shot CoOp and the +8-shot TIP-Adapter on most datasets. Code and models are available at +https://github.com/tonyhuang2022/UPL. +" +ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing,Ian Arawjo,http://arxiv.org/pdf/2309.09128v1.pdf,2023-09-17,"['cs.hc', 'cs.ai', 'h.5.2; i.2']",2309.09128v1.pdf," Evaluating outputs of large language models (LLMs) is challenging, requiring +making -- and making sense of -- many responses. Yet tools that go beyond basic +prompting tend to require knowledge of programming APIs, focus on narrow +domains, or are closed-source. We present ChainForge, an open-source visual +toolkit for prompt engineering and on-demand hypothesis testing of text +generation LLMs. ChainForge provides a graphical interface for comparison of +responses across models and prompt variations. Our system was designed to +support three tasks: model selection, prompt template design, and hypothesis +testing (e.g., auditing). We released ChainForge early in its development and +iterated on its design with academics and online users. Through in-lab and +interview studies, we find that a range of people could use ChainForge to +investigate hypotheses that matter to them, including in real-world settings. +We identify three modes of prompt engineering and LLM hypothesis testing: +opportunistic exploration, limited evaluation, and iterative refinement. +" +CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming,Felicia Li Feng,http://arxiv.org/pdf/2310.09235v1.pdf,2023-10-13,['cs.hc'],2310.09235v1.pdf," Natural language (NL) programming has become more approachable due to the +powerful code-generation capability of large language models (LLMs). This shift +to using NL to program enhances collaborative programming by reducing +communication barriers and context-switching among programmers from varying +backgrounds. However, programmers may face challenges during prompt engineering +in a collaborative setting as they need to actively keep aware of their +collaborators' progress and intents. In this paper, we aim to investigate ways +to assist programmers' prompt engineering in a collaborative context. We first +conducted a formative study to understand the workflows and challenges of +programmers when using NL for collaborative programming. Based on our findings, +we implemented a prototype, CoPrompt, to support collaborative prompt +engineering by providing referring, requesting, sharing, and linking +mechanisms. Our user study indicates that CoPrompt assists programmers in +comprehending collaborators' prompts and building on their collaborators' work, +reducing repetitive updates and communication costs. +" +Prompt-Engineering and Transformer-based Question Generation and Evaluation,Rubaba Amyeen,http://arxiv.org/pdf/2310.18867v1.pdf,2023-10-29,"['cs.cl', 'cs.ai']",2310.18867v1.pdf," Question generation has numerous applications in the educational context. +Question generation can prove helpful for students when reviewing content and +testing themselves. Furthermore, a question generation model can aid teachers +by lessening the burden of creating assessments and other practice material. +This paper aims to find the best method to generate questions from textual data +through a transformer model and prompt engineering. In this research, we +finetuned a pretrained distilBERT model on the SQuAD question answering dataset +to generate questions. In addition to training a transformer model, prompt +engineering was applied to generate questions effectively using the LLaMA +model. The generated questions were compared against the baseline questions in +the SQuAD dataset to evaluate the effectiveness of four different prompts. All +four prompts demonstrated over 60% similarity on average. Of the +prompt-generated questions, 30% achieved a high similarity score greater than +70%. +" +A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models,James Urquhart Allingham,http://arxiv.org/pdf/2302.06235v2.pdf,2023-02-13,"['cs.lg', 'cs.cv', 'stat.ml']",2302.06235v2.pdf," Contrastively trained text-image models have the remarkable ability to +perform zero-shot classification, that is, classifying previously unseen images +into categories that the model has never been explicitly trained to identify. +However, these zero-shot classifiers need prompt engineering to achieve high +accuracy. Prompt engineering typically requires hand-crafting a set of prompts +for individual downstream tasks. In this work, we aim to automate this prompt +engineering and improve zero-shot accuracy through prompt ensembling. In +particular, we ask ""Given a large pool of prompts, can we automatically score +the prompts and ensemble those that are most suitable for a particular +downstream dataset, without needing access to labeled validation data?"". We +demonstrate that this is possible. In doing so, we identify several pathologies +in a naive prompt scoring method where the score can be easily overconfident +due to biases in pre-training and test data, and we propose a novel prompt +scoring method that corrects for the biases. Using our proposed scoring method +to create a weighted average prompt ensemble, our method outperforms equal +average ensemble, as well as hand-crafted prompts, on ImageNet, 4 of its +variants, and 11 fine-grained classification benchmarks, all while being fully +automatic, optimization-free, and not requiring access to labeled validation +data. +" +Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification,Benjamin Clavié,http://arxiv.org/pdf/2303.07142v3.pdf,2023-03-13,['cs.cl'],2303.07142v3.pdf," This case study investigates the task of job classification in a real-world +setting, where the goal is to determine whether an English-language job posting +is appropriate for a graduate or entry-level position. We explore multiple +approaches to text classification, including supervised approaches such as +traditional models like Support Vector Machines (SVMs) and state-of-the-art +deep learning methods such as DeBERTa. We compare them with Large Language +Models (LLMs) used in both few-shot and zero-shot classification settings. To +accomplish this task, we employ prompt engineering, a technique that involves +designing prompts to guide the LLMs towards the desired output. Specifically, +we evaluate the performance of two commercially available state-of-the-art +GPT-3.5-based language models, text-davinci-003 and gpt-3.5-turbo. We also +conduct a detailed analysis of the impact of different aspects of prompt +engineering on the model's performance. Our results show that, with a +well-designed prompt, a zero-shot gpt-3.5-turbo classifier outperforms all +other models, achieving a 6% increase in Precision@95% Recall compared to the +best supervised approach. Furthermore, we observe that the wording of the +prompt is a critical factor in eliciting the appropriate ""reasoning"" in the +model, and that seemingly minor aspects of the prompt significantly affect the +model's performance. +" +Simulating H.P. Lovecraft horror literature with the ChatGPT large language model,Eduardo C. Garrido-Merchán,http://arxiv.org/pdf/2305.03429v1.pdf,2023-05-05,['cs.cl'],2305.03429v1.pdf," In this paper, we present a novel approach to simulating H.P. Lovecraft's +horror literature using the ChatGPT large language model, specifically the +GPT-4 architecture. Our study aims to generate text that emulates Lovecraft's +unique writing style and themes, while also examining the effectiveness of +prompt engineering techniques in guiding the model's output. To achieve this, +we curated a prompt containing several specialized literature references and +employed advanced prompt engineering methods. We conducted an empirical +evaluation of the generated text by administering a survey to a sample of +undergraduate students. Utilizing statistical hypothesis testing, we assessed +the students ability to distinguish between genuine Lovecraft works and those +generated by our model. Our findings demonstrate that the participants were +unable to reliably differentiate between the two, indicating the effectiveness +of the GPT-4 model and our prompt engineering techniques in emulating +Lovecraft's literary style. In addition to presenting the GPT model's +capabilities, this paper provides a comprehensive description of its underlying +architecture and offers a comparative analysis with related work that simulates +other notable authors and philosophers, such as Dennett. By exploring the +potential of large language models in the context of literary emulation, our +study contributes to the body of research on the applications and limitations +of these models in various creative domains. +" +CXR-LLaVA: Multimodal Large Language Model for Interpreting Chest X-ray Images,Seowoo Lee,http://arxiv.org/pdf/2310.18341v2.pdf,2023-10-22,"['cs.cl', 'cs.ai']",2310.18341v2.pdf," Purpose: Recent advancements in large language models (LLMs) have expanded +their capabilities in a multimodal fashion, potentially replicating the image +interpretation of human radiologists. This study aimed to develop open-source +multimodal large language model for interpreting chest X-ray images +(CXR-LLaVA). We also examined the effect of prompt engineering and model +parameters such as temperature and nucleus sampling. + Materials and Methods: For training, we collected 659,287 publicly available +CXRs: 417,336 CXRs had labels for certain radiographic abnormalities (dataset +1); 241,951 CXRs provided free-text radiology reports (dataset 2). After +pre-training the Resnet50 as an image encoder, the contrastive language-image +pre-training was used to align CXRs and corresponding radiographic +abnormalities. Then, the Large Language Model Meta AI-2 was fine-tuned using +dataset 2, which were refined using GPT-4, with generating various question +answering scenarios. The code can be found at +https://github.com/ECOFRI/CXR_LLaVA. + Results: In the test set, we observed that the model's performance fluctuated +based on its parameters. On average, it achieved F1 score of 0.34 for five +pathologic findings (atelectasis, cardiomegaly, consolidation, edema, and +pleural effusion), which was improved to 0.46 through prompt engineering. In +the independent set, the model achieved an average F1 score of 0.30 for the +same pathologic findings. Notably, for the pediatric chest radiograph dataset, +which was unseen during training, the model differentiated abnormal radiographs +with an F1 score ranging from 0.84 to 0.85. + Conclusion: CXR-LLaVA demonstrates promising potential in CXR interpretation. +Both prompt engineering and model parameter adjustments can play pivotal roles +in interpreting CXRs. +" +A Taxonomy of Prompt Modifiers for Text-To-Image Generation,Jonas Oppenlaender,http://arxiv.org/pdf/2204.13988v3.pdf,2022-04-20,"['cs.mm', 'cs.cl', 'cs.hc', 'h.5; h.m; j.5']",2204.13988v3.pdf," Text-to-image generation has seen an explosion of interest since 2021. Today, +beautiful and intriguing digital images and artworks can be synthesized from +textual inputs (""prompts"") with deep generative models. Online communities +around text-to-image generation and AI generated art have quickly emerged. This +paper identifies six types of prompt modifiers used by practitioners in the +online community based on a 3-month ethnographic study. The novel taxonomy of +prompt modifiers provides researchers a conceptual starting point for +investigating the practice of text-to-image generation, but may also help +practitioners of AI generated art improve their images. We further outline how +prompt modifiers are applied in the practice of ""prompt engineering."" We +discuss research opportunities of this novel creative practice in the field of +Human-Computer Interaction (HCI). The paper concludes with a discussion of +broader implications of prompt engineering from the perspective of Human-AI +Interaction (HAI) in future applications beyond the use case of text-to-image +generation and AI generated art. +" +What GPT Knows About Who is Who,Xiaohan Yang,http://arxiv.org/pdf/2205.07407v1.pdf,2022-05-16,"['cs.cl', 'cs.lg']",2205.07407v1.pdf," Coreference resolution -- which is a crucial task for understanding discourse +and language at large -- has yet to witness widespread benefits from large +language models (LLMs). Moreover, coreference resolution systems largely rely +on supervised labels, which are highly expensive and difficult to annotate, +thus making it ripe for prompt engineering. In this paper, we introduce a +QA-based prompt-engineering method and discern \textit{generative}, pre-trained +LLMs' abilities and limitations toward the task of coreference resolution. Our +experiments show that GPT-2 and GPT-Neo can return valid answers, but that +their capabilities to identify coreferent mentions are limited and +prompt-sensitive, leading to inconsistent results. +" +Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements,Conrad Borchers,http://arxiv.org/pdf/2205.11374v1.pdf,2022-05-23,"['cs.cl', 'cs.ai']",2205.11374v1.pdf," The growing capability and availability of generative language models has +enabled a wide range of new downstream tasks. Academic research has identified, +quantified and mitigated biases present in language models but is rarely +tailored to downstream tasks where wider impact on individuals and society can +be felt. In this work, we leverage one popular generative language model, +GPT-3, with the goal of writing unbiased and realistic job advertisements. We +first assess the bias and realism of zero-shot generated advertisements and +compare them to real-world advertisements. We then evaluate prompt-engineering +and fine-tuning as debiasing methods. We find that prompt-engineering with +diversity-encouraging prompts gives no significant improvement to bias, nor +realism. Conversely, fine-tuning, especially on unbiased real advertisements, +can improve realism and reduce bias. +" +Arguments to Key Points Mapping with Prompt-based Learning,Ahnaf Mozib Samin,http://arxiv.org/pdf/2211.14995v1.pdf,2022-11-28,['cs.cl'],2211.14995v1.pdf," Handling and digesting a huge amount of information in an efficient manner +has been a long-term demand in modern society. Some solutions to map key points +(short textual summaries capturing essential information and filtering +redundancies) to a large number of arguments/opinions have been provided +recently (Bar-Haim et al., 2020). To complement the full picture of the +argument-to-keypoint mapping task, we mainly propose two approaches in this +paper. The first approach is to incorporate prompt engineering for fine-tuning +the pre-trained language models (PLMs). The second approach utilizes +prompt-based learning in PLMs to generate intermediary texts, which are then +combined with the original argument-keypoint pairs and fed as inputs to a +classifier, thereby mapping them. Furthermore, we extend the experiments to +cross/in-domain to conduct an in-depth analysis. In our evaluation, we find +that i) using prompt engineering in a more direct way (Approach 1) can yield +promising results and improve the performance; ii) Approach 2 performs +considerably worse than Approach 1 due to the negation issue of the PLM. +" +Legal Prompt Engineering for Multilingual Legal Judgement Prediction,Dietrich Trautmann,http://arxiv.org/pdf/2212.02199v1.pdf,2022-12-05,"['cs.cl', 'cs.ai']",2212.02199v1.pdf," Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and +assist a large language model (LLM) with performing a natural legal language +processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal +documents for the Legal Judgement Prediction (LJP) task. We investigate the +performance of zero-shot LPE for given facts in case-texts from the European +Court of Human Rights (in English) and the Federal Supreme Court of Switzerland +(in German, French and Italian). Our results show that zero-shot LPE is better +compared to the baselines, but it still falls short compared to current state +of the art supervised approaches. Nevertheless, the results are important, +since there was 1) no explicit domain-specific data used - so we show that the +transfer to the legal domain is possible for general-purpose LLMs, and 2) the +LLMs where directly applied without any further training or fine-tuning - which +in turn saves immensely in terms of additional computational costs. +" +The Infinite Index: Information Retrieval on Generative Text-To-Image Models,Niklas Deckers,http://arxiv.org/pdf/2212.07476v2.pdf,2022-12-14,"['cs.ir', 'cs.cl', 'cs.cv']",2212.07476v2.pdf," Conditional generative models such as DALL-E and Stable Diffusion generate +images based on a user-defined text, the prompt. Finding and refining prompts +that produce a desired image has become the art of prompt engineering. +Generative models do not provide a built-in retrieval model for a user's +information need expressed through prompts. In light of an extensive literature +review, we reframe prompt engineering for generative models as interactive +text-based retrieval on a novel kind of ""infinite index"". We apply these +insights for the first time in a case study on image generation for game design +with an expert. Finally, we envision how active learning may help to guide the +retrieval of generated images. +" +"Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering",Sue Lim,http://arxiv.org/pdf/2212.07507v1.pdf,2022-12-14,['cs.cl'],2212.07507v1.pdf," This study introduces and examines the potential of an AI system to generate +health awareness messages. The topic of folic acid, a vitamin that is critical +during pregnancy, served as a test case. Using prompt engineering, we generated +messages that could be used to raise awareness and compared them to retweeted +human-generated messages via computational and human evaluation methods. The +system was easy to use and prolific, and computational analyses revealed that +the AI-generated messages were on par with human-generated ones in terms of +sentiment, reading ease, and semantic content. Also, the human evaluation study +showed that AI-generated messages ranked higher in message quality and clarity. +We discuss the theoretical, practical, and ethical implications of these +results. +" +What does CLIP know about a red circle? Visual prompt engineering for VLMs,Aleksandar Shtedritski,http://arxiv.org/pdf/2304.06712v2.pdf,2023-04-13,['cs.cv'],2304.06712v2.pdf," Large-scale Vision-Language Models, such as CLIP, learn powerful image-text +representations that have found numerous applications, from zero-shot +classification to text-to-image generation. Despite that, their capabilities +for solving novel discriminative tasks via prompting fall behind those of large +language models, such as GPT-3. Here we explore the idea of visual prompt +engineering for solving computer vision tasks beyond classification by editing +in image space instead of text. In particular, we discover an emergent ability +of CLIP, where, by simply drawing a red circle around an object, we can direct +the model's attention to that region, while also maintaining global +information. We show the power of this simple approach by achieving +state-of-the-art in zero-shot referring expressions comprehension and strong +performance in keypoint localization tasks. Finally, we draw attention to some +potential ethical concerns of large language-vision models. +" +Prompt Engineering for Transformer-based Chemical Similarity Search Identifies Structurally Distinct Functional Analogues,Clayton W. Kosonocky,http://arxiv.org/pdf/2305.16330v1.pdf,2023-05-17,"['physics.chem-ph', 'cs.lg']",2305.16330v1.pdf," Chemical similarity searches are widely used in-silico methods for +identifying new drug-like molecules. These methods have historically relied on +structure-based comparisons to compute molecular similarity. Here, we use a +chemical language model to create a vector-based chemical search. We extend +implementations by creating a prompt engineering strategy that utilizes two +different chemical string representation algorithms: one for the query and the +other for the database. We explore this method by reviewing the search results +from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine, +lysergic acid diethylamide, and fentanyl) and three dye-like query molecules +(acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this +novel method identifies molecules that are functionally similar to the query, +indicated by the associated patent literature, and that many of these molecules +are structurally distinct from the query, making them unlikely to be found with +traditional chemical similarity search methods. This method may aid in the +discovery of novel structural classes of molecules that achieve target +functionality. +" +Submodular Minimax Optimization: Finding Effective Sets,Loay Mualem,http://arxiv.org/pdf/2305.16903v1.pdf,2023-05-26,"['cs.lg', 'cs.dm', 'math.oc', '68r05 (primary) 90c26, 90c20, 68t20, 68w40 (secondary)', 'g.2.1; i.2.m; f.2.2']",2305.16903v1.pdf," Despite the rich existing literature about minimax optimization in continuous +settings, only very partial results of this kind have been obtained for +combinatorial settings. In this paper, we fill this gap by providing a +characterization of submodular minimax optimization, the problem of finding a +set (for either the min or the max player) that is effective against every +possible response. We show when and under what conditions we can find such +sets. We also demonstrate how minimax submodular optimization provides robust +solutions for downstream machine learning applications such as (i) efficient +prompt engineering for question answering, (ii) prompt engineering for dialog +state tracking, (iii) identifying robust waiting locations for ride-sharing, +(iv) ride-share difficulty kernelization, and (v) finding adversarial images. +Our experiments demonstrate that our proposed algorithms consistently +outperform other baselines. +" +Unsupervised Human Activity Recognition through Two-stage Prompting with ChatGPT,Qingxin Xia,http://arxiv.org/pdf/2306.02140v1.pdf,2023-06-03,"['cs.hc', 'cs.cl']",2306.02140v1.pdf," Wearable sensor devices, which offer the advantage of recording daily objects +used by a person while performing an activity, enable the feasibility of +unsupervised Human Activity Recognition (HAR). Unfortunately, previous +unsupervised approaches using the usage sequence of objects usually require a +proper description of activities manually prepared by humans. Instead, we +leverage the knowledge embedded in a Large Language Model (LLM) of ChatGPT. +Because the sequence of objects robustly characterizes the activity identity, +it is possible that ChatGPT already learned the association between activities +and objects from existing contexts. However, previous prompt engineering for +ChatGPT exhibits limited generalization ability when dealing with a list of +words (i.e., sequence of objects) due to the similar weighting assigned to each +word in the list. In this study, we propose a two-stage prompt engineering, +which first guides ChatGPT to generate activity descriptions associated with +objects while emphasizing important objects for distinguishing similar +activities; then outputs activity classes and explanations for enhancing the +contexts that are helpful for HAR. To the best of our knowledge, this is the +first study that utilizes ChatGPT to recognize activities using objects in an +unsupervised manner. We conducted our approach on three datasets and +demonstrated the state-of-the-art performance. +" +User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques,Sunwoo Kim,http://arxiv.org/pdf/2306.02717v1.pdf,2023-06-05,['cs.cv'],2306.02717v1.pdf," Recent text-driven image editing in diffusion models has shown remarkable +success. However, the existing methods assume that the user's description +sufficiently grounds the contexts in the source image, such as objects, +background, style, and their relations. This assumption is unsuitable for +real-world applications because users have to manually engineer text prompts to +find optimal descriptions for different images. From the users' standpoint, +prompt engineering is a labor-intensive process, and users prefer to provide a +target word for editing instead of a full sentence. To address this problem, we +first demonstrate the importance of a detailed text description of the source +image, by dividing prompts into three categories based on the level of semantic +details. Then, we propose simple yet effective methods by combining prompt +generation frameworks, thereby making the prompt engineering process more +user-friendly. Extensive qualitative and quantitative experiments demonstrate +the importance of prompts in text-driven image editing and our method is +comparable to ground-truth prompts. +" +PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation,Yingchaojie Feng,http://arxiv.org/pdf/2307.09036v2.pdf,2023-07-18,"['cs.ai', 'cs.hc']",2307.09036v2.pdf," Generative text-to-image models have gained great popularity among the public +for their powerful capability to generate high-quality images based on natural +language prompts. However, developing effective prompts for desired images can +be challenging due to the complexity and ambiguity of natural language. This +research proposes PromptMagician, a visual analysis system that helps users +explore the image results and refine the input prompts. The backbone of our +system is a prompt recommendation model that takes user prompts as input, +retrieves similar prompt-image pairs from DiffusionDB, and identifies special +(important and relevant) prompt keywords. To facilitate interactive prompt +refinement, PromptMagician introduces a multi-level visualization for the +cross-modal embedding of the retrieved images and recommended keywords, and +supports users in specifying multiple criteria for personalized exploration. +Two usage scenarios, a user study, and expert interviews demonstrate the +effectiveness and usability of our system, suggesting it facilitates prompt +engineering and improves the creativity support of the generative text-to-image +model. +" +Is GPT a Computational Model of Emotion? Detailed Analysis,Ala N. Tak,http://arxiv.org/pdf/2307.13779v1.pdf,2023-07-25,"['cs.cl', 'cs.ai', 'cs.cy', 'cs.hc']",2307.13779v1.pdf," This paper investigates the emotional reasoning abilities of the GPT family +of large language models via a component perspective. The paper first examines +how the model reasons about autobiographical memories. Second, it +systematically varies aspects of situations to impact emotion intensity and +coping tendencies. Even without the use of prompt engineering, it is shown that +GPT's predictions align significantly with human-provided appraisals and +emotional labels. However, GPT faces difficulties predicting emotion intensity +and coping responses. GPT-4 showed the highest performance in the initial study +but fell short in the second, despite providing superior results after minor +prompt engineering. This assessment brings up questions on how to effectively +employ the strong points and address the weak areas of these models, +particularly concerning response variability. These studies underscore the +merits of evaluating models from a componential perspective. +" +Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability,Alberto D. Rodriguez,http://arxiv.org/pdf/2308.00229v1.pdf,2023-08-01,['cs.se'],2308.00229v1.pdf," Large Language Models (LLMs) have the potential to revolutionize automated +traceability by overcoming the challenges faced by previous methods and +introducing new possibilities. However, the optimal utilization of LLMs for +automated traceability remains unclear. This paper explores the process of +prompt engineering to extract link predictions from an LLM. We provide detailed +insights into our approach for constructing effective prompts, offering our +lessons learned. Additionally, we propose multiple strategies for leveraging +LLMs to generate traceability links, improving upon previous zero-shot methods +on the ranking of candidate links after prompt refinement. The primary +objective of this paper is to inspire and assist future researchers and +engineers by highlighting the process of constructing traceability prompts to +effectively harness LLMs for advancing automatic traceability. +" +CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought,Bowen Zhang,http://arxiv.org/pdf/2309.11143v1.pdf,2023-09-20,"['cs.cl', 'cs.ai']",2309.11143v1.pdf," Unsupervised sentence representation learning aims to transform input +sentences into fixed-length vectors enriched with intricate semantic +information while obviating the reliance on labeled data. Recent progress +within this field, propelled by contrastive learning and prompt engineering, +has significantly bridged the gap between unsupervised and supervised +strategies. Nonetheless, the potential utilization of Chain-of-Thought, remains +largely untapped within this trajectory. To unlock latent capabilities within +pre-trained models, such as BERT, we propose a two-stage approach for sentence +representation: comprehension and summarization. Subsequently, the output of +the latter phase is harnessed as the vectorized representation of the input +sentence. For further performance enhancement, we meticulously refine both the +contrastive learning loss function and the template denoising technique for +prompt engineering. Rigorous experimentation substantiates our method, +CoT-BERT, transcending a suite of robust baselines without necessitating other +text representation models or external databases. +" +How does prompt engineering affect ChatGPT performance on unsupervised entity resolution?,Khanin Sisaengsuwanchai,http://arxiv.org/pdf/2310.06174v1.pdf,2023-10-09,"['cs.ai', 'cs.se']",2310.06174v1.pdf," Entity Resolution (ER) is the problem of semi-automatically determining when +two entities refer to the same underlying entity, with applications ranging +from healthcare to e-commerce. Traditional ER solutions required considerable +manual expertise, including feature engineering, as well as identification and +curation of training data. In many instances, such techniques are highly +dependent on the domain. With recent advent in large language models (LLMs), +there is an opportunity to make ER much more seamless and domain-independent. +However, it is also well known that LLMs can pose risks, and that the quality +of their outputs can depend on so-called prompt engineering. Unfortunately, a +systematic experimental study on the effects of different prompting methods for +addressing ER, using LLMs like ChatGPT, has been lacking thus far. This paper +aims to address this gap by conducting such a study. Although preliminary in +nature, our results show that prompting can significantly affect the quality of +ER, although it affects some metrics more than others, and can also be dataset +dependent. +" +Interactive Task Planning with Language Models,Boyi Li,http://arxiv.org/pdf/2310.10645v1.pdf,2023-10-16,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.hc']",2310.10645v1.pdf," An interactive robot framework accomplishes long-horizon task planning and +can easily generalize to new goals or distinct tasks, even during execution. +However, most traditional methods require predefined module design, which makes +it hard to generalize to different goals. Recent large language model based +approaches can allow for more open-ended planning but often require heavy +prompt engineering or domain-specific pretrained models. To tackle this, we +propose a simple framework that achieves interactive task planning with +language models. Our system incorporates both high-level planning and low-level +function execution via language. We verify the robustness of our system in +generating novel high-level instructions for unseen objectives and its ease of +adaptation to different tasks by merely substituting the task guidelines, +without the need for additional complex prompt engineering. Furthermore, when +the user sends a new request, our system is able to replan accordingly with +precision based on the new request, task guidelines and previously executed +steps. Please check more details on our https://wuphilipp.github.io/itp_site +and https://youtu.be/TrKLuyv26_g. +" +Prompt Engineering Through the Lens of Optimal Control,Yifan Luo,http://arxiv.org/pdf/2310.14201v2.pdf,2023-10-22,"['cs.lg', 'math.oc']",2310.14201v2.pdf," Prompt Engineering (PE) has emerged as a critical technique for guiding Large +Language Models (LLMs) in solving intricate tasks. Its importance is +highlighted by its potential to significantly enhance the efficiency and +effectiveness of human-machine interaction. As tasks grow increasingly complex, +recent advanced PE methods have extended beyond the limitations of single-round +interactions to embrace multi-round interactions, which allows for a deeper and +more nuanced engagement with LLMs. In this paper, we propose an optimal control +framework tailored for multi-round interactions with LLMs. This framework +provides a unified mathematical structure that not only systematizes the +existing PE methods but also sets the stage for rigorous analytical +improvements. Furthermore, we extend this framework to include PE via ensemble +methods and multi-agent collaboration, thereby enlarging the scope of +applicability. By adopting an optimal control perspective, we offer fresh +insights into existing PE methods and highlight theoretical challenges that +warrant future research. Besides, our work lays a foundation for the +development of more effective and interpretable PE methods. +" +A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models,Yuanfeng Song,http://arxiv.org/pdf/2310.18358v1.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.18358v1.pdf," The springing up of Large Language Models (LLMs) has shifted the community +from single-task-orientated natural language processing (NLP) research to a +holistic end-to-end multi-task learning paradigm. Along this line of research +endeavors in the area, LLM-based prompting methods have attracted much +attention, partially due to the technological advantages brought by prompt +engineering (PE) as well as the underlying NLP principles disclosed by various +prompting methods. Traditional supervised learning usually requires training a +model based on labeled data and then making predictions. In contrast, PE +methods directly use the powerful capabilities of existing LLMs (i.e., GPT-3 +and GPT-4) via composing appropriate prompts, especially under few-shot or +zero-shot scenarios. Facing the abundance of studies related to the prompting +and the ever-evolving nature of this field, this article aims to (i) illustrate +a novel perspective to review existing PE methods, within the well-established +communication theory framework; (ii) facilitate a better/deeper understanding +of developing trends of existing PE methods used in four typical tasks; (iii) +shed light on promising research directions for future PE methods. +" +Apollo: Zero-shot MultiModal Reasoning with Multiple Experts,Daniela Ben-David,http://arxiv.org/pdf/2310.18369v1.pdf,2023-10-25,"['cs.cl', 'cs.ai', 'cs.cv', 'i.2.7; i.5.4']",2310.18369v1.pdf," We propose a modular framework that leverages the expertise of different +foundation models over different modalities and domains in order to perform a +single, complex, multi-modal task, without relying on prompt engineering or +otherwise tailor-made multi-modal training. Our approach enables decentralized +command execution and allows each model to both contribute and benefit from the +expertise of the other models. Our method can be extended to a variety of +foundation models (including audio and vision), above and beyond only language +models, as it does not depend on prompts. We demonstrate our approach on two +tasks. On the well-known task of stylized image captioning, our experiments +show that our approach outperforms semi-supervised state-of-the-art models, +while being zero-shot and avoiding costly training, data collection, and prompt +engineering. We further demonstrate this method on a novel task, audio-aware +image captioning, in which an image and audio are given and the task is to +generate text that describes the image within the context of the provided +audio. Our code is available on GitHub. +" +Towards Zero-Shot and Few-Shot Table Question Answering using GPT-3,Pragya Srivastava,http://arxiv.org/pdf/2210.17284v1.pdf,2022-10-31,"['cs.lg', '14j60 (primary)']",2210.17284v1.pdf," We present very early results on using GPT-3 to perform question answering on +tabular data. We find that stock pre-trained GPT-3 is able to zero-shot learn +the table structure from a serialized JSON array-of-arrays representation, and +able to answer lookup queries and simple comparison questions in natural +language without any fine-tuning. We further find that simple prompt +engineering to include few-shot static Q&A examples significantly improves +accuracy. Lastly, we find that intermixing passage text improves accuracy even +further on heterogeneous data. We apply our approach on a novel dataset of +simple tables in newspaper infographics with promising results. Overall, we +find much cause for optimism in this basic approach. +" +Investigating Prompt Engineering in Diffusion Models,Sam Witteveen,http://arxiv.org/pdf/2211.15462v1.pdf,2022-11-21,"['cs.cv', 'cs.ai', 'cs.cl']",2211.15462v1.pdf," With the spread of the use of Text2Img diffusion models such as DALL-E 2, +Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is +selecting the right prompts to achieve the desired artistic output. We present +techniques for measuring the effect that specific words and phrases in prompts +have, and (in the Appendix) present guidance on the selection of prompts to +produce desired effects. +" +Refining the Responses of LLMs by Themselves,Tianqiang Yan,http://arxiv.org/pdf/2305.04039v1.pdf,2023-05-06,"['cs.cl', 'cs.ai']",2305.04039v1.pdf," In this paper, we propose a simple yet efficient approach based on prompt +engineering that leverages the large language model itself to optimize its +answers without relying on auxiliary models. We introduce an iterative +self-evaluating optimization mechanism, with the potential for improved output +quality as iterations progress, removing the need for manual intervention. The +experiment's findings indicate that utilizing our response refinement framework +on the GPT-3.5 model yields results that are on par with, or even surpass, +those generated by the cutting-edge GPT-4 model. Detailed implementation +strategies and illustrative examples are provided to demonstrate the +superiority of our proposed solution. +" +Efficient Black-Box Adversarial Attacks on Neural Text Detectors,Vitalii Fishchuk,http://arxiv.org/pdf/2311.01873v1.pdf,2023-11-03,['cs.cl'],2311.01873v1.pdf," Neural text detectors are models trained to detect whether a given text was +generated by a language model or written by a human. In this paper, we +investigate three simple and resource-efficient strategies (parameter tweaking, +prompt engineering, and character-level mutations) to alter texts generated by +GPT-3.5 that are unsuspicious or unnoticeable for humans but cause +misclassification by neural text detectors. The results show that especially +parameter tweaking and character-level mutations are effective strategies. +" +Prompted Software Engineering in the Era of AI Models,Dae-Kyoo Kim,http://arxiv.org/pdf/2311.03359v1.pdf,2023-09-07,['cs.se'],2311.03359v1.pdf," This paper introduces prompted software engineering (PSE), which integrates +prompt engineering to build effective prompts for language-based AI models, to +enhance the software development process. PSE enables the use of AI models in +software development to produce high-quality software with fewer resources, +automating tedious tasks and allowing developers to focus on more innovative +aspects. However, effective prompts are necessary to guide software development +in generating accurate, relevant, and useful responses, while mitigating risks +of misleading outputs. This paper describes how productive prompts should be +built throughout the software development cycle. +" +Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language,Paul Denny,http://arxiv.org/pdf/2210.15157v1.pdf,2022-10-27,"['cs.hc', 'cs.ai']",2210.15157v1.pdf," GitHub Copilot is an artificial intelligence model for automatically +generating source code from natural language problem descriptions. Since June +2022, Copilot has officially been available for free to all students as a +plug-in to development environments like Visual Studio Code. Prior work +exploring OpenAI Codex, the underlying model that powers Copilot, has shown it +performs well on typical CS1 problems thus raising concerns about the impact it +will have on how introductory programming courses are taught. However, little +is known about the types of problems for which Copilot does not perform well, +or about the natural language interactions that a student might have with +Copilot when resolving errors. We explore these questions by evaluating the +performance of Copilot on a publicly available dataset of 166 programming +problems. We find that it successfully solves around half of these problems on +its very first attempt, and that it solves 60\% of the remaining problems using +only natural language changes to the problem description. We argue that this +type of prompt engineering, which we believe will become a standard interaction +between human and Copilot when it initially fails, is a potentially useful +learning activity that promotes computational thinking skills, and is likely to +change the nature of code writing skill development. +" +ChatGPT4PCG Competition: Character-like Level Generation for Science Birds,Pittawat Taveekitworachai,http://arxiv.org/pdf/2303.15662v2.pdf,2023-03-28,"['cs.ai', 'cs.cl', 'i.2.7; i.2.8']",2303.15662v2.pdf," This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE +Conference on Games. The objective of this competition is for participants to +create effective prompts for ChatGPT--enabling it to generate Science Birds +levels with high stability and character-like qualities--fully using their +creativity as well as prompt engineering skills. ChatGPT is a conversational +agent developed by OpenAI. Science Birds is selected as the competition +platform because designing an Angry Birds-like level is not a trivial task due +to the in-game gravity; the quality of the levels is determined by their +stability. To lower the entry barrier to the competition, we limit the task to +the generation of capitalized English alphabetical characters. We also allow +only a single prompt to be used for generating all the characters. Here, the +quality of the generated levels is determined by their stability and similarity +to the given characters. A sample prompt is provided to participants for their +reference. An experiment is conducted to determine the effectiveness of several +modified versions of this sample prompt on level stability and similarity by +testing them on several characters. To the best of our knowledge, we believe +that ChatGPT4PCG is the first competition of its kind and hope to inspire +enthusiasm for prompt engineering in procedural content generation. +" +Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering,Rishov Paul,http://arxiv.org/pdf/2304.07840v2.pdf,2023-04-16,"['cs.lg', 'cs.se']",2304.07840v2.pdf," Sequence-to-sequence models have been used to transform erroneous programs +into correct ones when trained with a large enough dataset. Some recent studies +also demonstrated strong empirical evidence that code review could improve the +program repair further. Large language models, trained with Natural Language +(NL) and Programming Language (PL), can contain inherent knowledge of both. In +this study, we investigate if this inherent knowledge of PL and NL can be +utilized to improve automated program repair. We applied PLBART and CodeT5, two +state-of-the-art language models that are pre-trained with both PL and NL, on +two such natural language-based program repair datasets and found that the +pre-trained language models fine-tuned with datasets containing both code +review and subsequent code changes notably outperformed each of the previous +models. With the advent of code generative models like Codex and GPT-3.5-Turbo, +we also performed zero-shot and few-shots learning-based prompt engineering to +assess their performance on these datasets. However, the practical application +of using LLMs in the context of automated program repair is still a long way +off based on our manual analysis of the generated repaired codes by the +learning models. +" +Conceptual Design Generation Using Large Language Models,Kevin Ma,http://arxiv.org/pdf/2306.01779v1.pdf,2023-05-30,"['cs.cl', 'cs.ai']",2306.01779v1.pdf," Concept generation is a creative step in the conceptual design phase, where +designers often turn to brainstorming, mindmapping, or crowdsourcing design +ideas to complement their own knowledge of the domain. Recent advances in +natural language processing (NLP) and machine learning (ML) have led to the +rise of Large Language Models (LLMs) capable of generating seemingly creative +outputs from textual prompts. The success of these models has led to their +integration and application across a variety of domains, including art, +entertainment, and other creative work. In this paper, we leverage LLMs to +generate solutions for a set of 12 design problems and compare them to a +baseline of crowdsourced solutions. We evaluate the differences between +generated and crowdsourced design solutions through multiple perspectives, +including human expert evaluations and computational metrics. Expert +evaluations indicate that the LLM-generated solutions have higher average +feasibility and usefulness while the crowdsourced solutions have more novelty. +We experiment with prompt engineering and find that leveraging few-shot +learning can lead to the generation of solutions that are more similar to the +crowdsourced solutions. These findings provide insight into the quality of +design solutions generated with LLMs and begins to evaluate prompt engineering +techniques that could be leveraged by practitioners to generate higher-quality +design solutions synergistically with LLMs. +" +Cheap-fake Detection with LLM using Prompt Engineering,Guangyang Wu,http://arxiv.org/pdf/2306.02776v1.pdf,2023-06-05,['cs.cv'],2306.02776v1.pdf," The misuse of real photographs with conflicting image captions in news items +is an example of the out-of-context (OOC) misuse of media. In order to detect +OOC media, individuals must determine the accuracy of the statement and +evaluate whether the triplet (~\textit{i.e.}, the image and two captions) +relates to the same event. This paper presents a novel learnable approach for +detecting OOC media in ICME'23 Grand Challenge on Detecting Cheapfakes. The +proposed method is based on the COSMOS structure, which assesses the coherence +between an image and captions, as well as between two captions. We enhance the +baseline algorithm by incorporating a Large Language Model (LLM), GPT3.5, as a +feature extractor. Specifically, we propose an innovative approach to feature +extraction utilizing prompt engineering to develop a robust and reliable +feature extractor with GPT3.5 model. The proposed method captures the +correlation between two captions and effectively integrates this module into +the COSMOS baseline model, which allows for a deeper understanding of the +relationship between captions. By incorporating this module, we demonstrate the +potential for significant improvements in cheap-fakes detection performance. +The proposed methodology holds promising implications for various applications +such as natural language processing, image captioning, and text-to-image +synthesis. Docker for submission is available at +https://hub.docker.com/repository/docker/mulns/ acmmmcheapfakes. +" +Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis,James R. Kirk,http://arxiv.org/pdf/2306.06770v3.pdf,2023-06-11,"['cs.ai', 'cs.hc', 'cs.ro', 'i.2.6; i.2.7']",2306.06770v3.pdf," Large language models (LLMs) offer significant promise as a knowledge source +for task learning. Prompt engineering has been shown to be effective for +eliciting knowledge from an LLM, but alone it is insufficient for acquiring +relevant, situationally grounded knowledge for an embodied agent learning novel +tasks. We describe a cognitive-agent approach that extends and complements +prompt engineering, mitigating its limitations and thus enabling an agent to +acquire new task knowledge matched to its native language capabilities, +embodiment, environment, and user preferences. The approach is to increase the +response space of LLMs and deploy general strategies, embedded within the +autonomous agent, to evaluate, repair, and select among candidate responses +produced by the LLM. We describe the approach and experiments that show how an +agent, by retrieving and evaluating a breadth of responses from the LLM, can +achieve 77-94% task completion in one-shot learning without user oversight. The +approach achieves 100% task completion when human oversight (such as an +indication of preference) is provided. Further, the type of oversight largely +shifts from explicit, natural language instruction to simple +confirmation/discomfirmation of high-quality responses that have been vetted by +the agent before presentation to a user. +" +ChatGPT for Robotics: Design Principles and Model Abilities,Sai Vemprala,http://arxiv.org/pdf/2306.17582v2.pdf,2023-02-20,"['cs.ai', 'cs.cl', 'cs.hc', 'cs.lg', 'cs.ro']",2306.17582v2.pdf," This paper presents an experimental study regarding the use of OpenAI's +ChatGPT for robotics applications. We outline a strategy that combines design +principles for prompt engineering and the creation of a high-level function +library which allows ChatGPT to adapt to different robotics tasks, simulators, +and form factors. We focus our evaluations on the effectiveness of different +prompt engineering techniques and dialog strategies towards the execution of +various types of robotics tasks. We explore ChatGPT's ability to use free-form +dialog, parse XML tags, and to synthesize code, in addition to the use of +task-specific prompting functions and closed-loop reasoning through dialogues. +Our study encompasses a range of tasks within the robotics domain, from basic +logical, geometrical, and mathematical reasoning all the way to complex domains +such as aerial navigation, manipulation, and embodied agents. We show that +ChatGPT can be effective at solving several of such tasks, while allowing users +to interact with it primarily via natural language instructions. In addition to +these studies, we introduce an open-sourced research tool called PromptCraft, +which contains a platform where researchers can collaboratively upload and vote +on examples of good prompting schemes for robotics applications, as well as a +sample robotics simulator with ChatGPT integration, making it easier for users +to get started with using ChatGPT for robotics. +" +Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPT,David James Woo,http://arxiv.org/pdf/2307.05493v1.pdf,2023-06-19,"['cs.hc', 'cs.ai', 'cs.cl']",2307.05493v1.pdf," ChatGPT is a state-of-the-art (SOTA) chatbot. Although it has potential to +support English as a foreign language (EFL) students' writing, to effectively +collaborate with it, a student must learn to engineer prompts, that is, the +skill of crafting appropriate instructions so that ChatGPT produces desired +outputs. However, writing an appropriate prompt for ChatGPT is not +straightforward for non-technical users who suffer a trial-and-error process. +This paper examines the content of EFL students' ChatGPT prompts when +completing a writing task and explores patterns in the quality and quantity of +the prompts. The data come from iPad screen recordings of secondary school EFL +students who used ChatGPT and other SOTA chatbots for the first time to +complete the same writing task. The paper presents a case study of four +distinct pathways that illustrate the trial-and-error process and show +different combinations of prompt content and quantity. The cases contribute +evidence for the need to provide prompt engineering education in the context of +the EFL writing classroom, if students are to move beyond an individual +trial-and-error process, learning a greater variety of prompt content and more +sophisticated prompts to support their writing. +" +"Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering",Angus Addlesee,http://arxiv.org/pdf/2308.15231v1.pdf,2023-08-29,"['cs.cl', 'cs.hc']",2308.15231v1.pdf," This paper evaluates the extent to which current Large Language Models (LLMs) +can capture task-oriented multi-party conversations (MPCs). We have recorded +and transcribed 29 MPCs between patients, their companions, and a social robot +in a hospital. We then annotated this corpus for multi-party goal-tracking and +intent-slot recognition. People share goals, answer each other's goals, and +provide other people's goals in MPCs - none of which occur in dyadic +interactions. To understand user goals in MPCs, we compared three methods in +zero-shot and few-shot settings: we fine-tuned T5, created pre-training tasks +to train DialogLM using LED, and employed prompt engineering techniques with +GPT-3.5-turbo, to determine which approach can complete this novel task with +limited data. GPT-3.5-turbo significantly outperformed the others in a few-shot +setting. The `reasoning' style prompt, when given 7% of the corpus as example +annotated conversations, was the best performing method. It correctly annotated +62.32% of the goal tracking MPCs, and 69.57% of the intent-slot recognition +MPCs. A `story' style prompt increased model hallucination, which could be +detrimental if deployed in safety-critical settings. We conclude that +multi-party conversations still challenge state-of-the-art LLMs. +" +Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation,Dawei Gao,http://arxiv.org/pdf/2308.15363v3.pdf,2023-08-29,"['cs.db', 'cs.cl', 'cs.lg']",2308.15363v3.pdf," Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL +task. However, the absence of a systematical benchmark inhibits the development +of designing effective, efficient and economic LLM-based Text-to-SQL solutions. +To address this challenge, in this paper, we first conduct a systematical and +extensive comparison over existing prompt engineering methods, including +question representation, example selection and example organization, and with +these experimental results, we elaborate their pros and cons. Based on these +findings, we propose a new integrated solution, named DAIL-SQL, which refreshes +the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To +explore the potential of open-source LLM, we investigate them in various +scenarios, and further enhance their performance with supervised fine-tuning. +Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well +as the advantages and disadvantages of the supervised fine-tuning. +Additionally, towards an efficient and economic LLM-based Text-to-SQL solution, +we emphasize the token efficiency in prompt engineering and compare the prior +studies under this metric. We hope that our work provides a deeper +understanding of Text-to-SQL with LLMs, and inspires further investigations and +broad applications. +" +PRE: Vision-Language Prompt Learning with Reparameterization Encoder,Anh Pham Thi Minh,http://arxiv.org/pdf/2309.07760v2.pdf,2023-09-14,"['cs.cv', 'cs.ai', 'cs.lg', 'i.4.0']",2309.07760v2.pdf," Large pre-trained vision-language models such as CLIP have demonstrated great +potential in zero-shot transferability to downstream tasks. However, to attain +optimal performance, the manual selection of prompts is necessary to improve +alignment between the downstream image distribution and the textual class +descriptions. This manual prompt engineering is the major challenge for +deploying such models in practice since it requires domain expertise and is +extremely time-consuming. To avoid non-trivial prompt engineering, recent work +Context Optimization (CoOp) introduced the concept of prompt learning to the +vision domain using learnable textual tokens. While CoOp can achieve +substantial improvements over manual prompts, its learned context is worse +generalizable to wider unseen classes within the same dataset. In this work, we +present Prompt Learning with Reparameterization Encoder (PRE) - a simple and +efficient method that enhances the generalization ability of the learnable +prompt to unseen classes while maintaining the capacity to learn Base classes. +Instead of directly optimizing the prompts, PRE employs a prompt encoder to +reparameterize the input prompt embeddings, enhancing the exploration of +task-specific knowledge from few-shot samples. Experiments and extensive +ablation studies on 8 benchmarks demonstrate that our approach is an efficient +method for prompt learning. Specifically, PRE achieves a notable enhancement of +5.60% in average accuracy on New classes and 3% in Harmonic mean compared to +CoOp in the 16-shot setting, all achieved within a good training time. +" +PEACE: Prompt Engineering Automation for CLIPSeg Enhancement in Aerial Robotics,Haechan Mark Bong,http://arxiv.org/pdf/2310.00085v1.pdf,2023-09-29,['cs.ro'],2310.00085v1.pdf," From industrial to space robotics, safe landing is an essential component for +flight operations. With the growing interest in artificial intelligence, we +direct our attention to learning based safe landing approaches. This paper +extends our previous work, DOVESEI, which focused on a reactive UAV system by +harnessing the capabilities of open vocabulary image segmentation. Prompt-based +safe landing zone segmentation using an open vocabulary based model is no more +just an idea, but proven to be feasible by the work of DOVESEI. However, a +heuristic selection of words for prompt is not a reliable solution since it +cannot take the changing environment into consideration and detrimental +consequences can occur if the observed environment is not well represented by +the given prompt. Therefore, we introduce PEACE (Prompt Engineering Automation +for CLIPSeg Enhancement), powering DOVESEI to automate the prompt generation +and engineering to adapt to data distribution shifts. Our system is capable of +performing safe landing operations with collision avoidance at altitudes as low +as 20 meters using only monocular cameras and image segmentation. We take +advantage of DOVESEI's dynamic focus to circumvent abrupt fluctuations in the +terrain segmentation between frames in a video stream. PEACE shows promising +improvements in prompt generation and engineering for aerial images compared to +the standard prompt used for CLIP and CLIPSeg. Combining DOVESEI and PEACE, our +system was able improve successful safe landing zone selections by 58.62% +compared to using only DOVESEI. All the source code is open source and +available online. +" +Understanding prompt engineering may not require rethinking generalization,Victor Akinwande,http://arxiv.org/pdf/2310.03957v1.pdf,2023-10-06,"['cs.lg', 'cs.cv']",2310.03957v1.pdf," Zero-shot learning in prompted vision-language models, the practice of +crafting prompts to build classifiers without an explicit training process, has +achieved impressive performance in many settings. This success presents a +seemingly surprising observation: these methods suffer relatively little from +overfitting, i.e., when a prompt is manually engineered to achieve low error on +a given training set (thus rendering the method no longer actually zero-shot), +the approach still performs well on held-out test data. In this paper, we show +that we can explain such performance well via recourse to classical PAC-Bayes +bounds. Specifically, we show that the discrete nature of prompts, combined +with a PAC-Bayes prior given by a language model, results in generalization +bounds that are remarkably tight by the standards of the literature: for +instance, the generalization bound of an ImageNet classifier is often within a +few percentage points of the true test error. We demonstrate empirically that +this holds for existing handcrafted prompts and prompts generated through +simple greedy search. Furthermore, the resulting bound is well-suited for model +selection: the models with the best bound typically also have the best test +performance. This work thus provides a possible justification for the +widespread practice of prompt engineering, even if it seems that such methods +could potentially overfit the training data. +" +What's the Magic Word? A Control Theory of LLM Prompting,Aman Bhargava,http://arxiv.org/pdf/2310.04444v2.pdf,2023-10-02,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.ne']",2310.04444v2.pdf," Prompt engineering is effective and important in the deployment of LLMs but +is poorly understood mathematically. Here, we formalize prompt engineering as +an optimal control problem on LLMs -- where the prompt is considered a control +variable for modulating the output distribution of the LLM. Within this +framework, we ask a simple question: given a sequence of tokens, does there +always exist a prompt we can prepend that will steer the LLM toward accurately +predicting the final token? We call such an optimal prompt the magic word since +prepending the prompt causes the LLM to output the correct answer. If magic +words exist, can we find them? If so, what are their properties? We offer +analytic analysis on the controllability of the self-attention head where we +prove a bound on controllability as a function of the singular values of its +weight matrices. We take inspiration from control theory to propose a metric +called $k-\epsilon$ controllability to characterize LLM steerability. We +compute the $k-\epsilon$ controllability of a panel of large language models, +including Falcon-7b, Llama-7b, and Falcon-40b on 5000 WikiText causal language +modeling tasks. Remarkably, we find that magic words of 10 tokens or less exist +for over 97% of WikiText instances surveyed for each model. +" +Configuration Validation with Large Language Models,Xinyu Lian,http://arxiv.org/pdf/2310.09690v1.pdf,2023-10-15,"['cs.se', 'cs.ai', 'cs.os']",2310.09690v1.pdf," Misconfigurations are the major causes of software failures. Existing +configuration validation techniques rely on manually written rules or test +cases, which are expensive to implement and maintain, and are hard to be +comprehensive. Leveraging machine learning (ML) and natural language processing +(NLP) for configuration validation is considered a promising direction, but has +been facing challenges such as the need of not only large-scale configuration +data, but also system-specific features and models which are hard to +generalize. Recent advances in Large Language Models (LLMs) show the promises +to address some of the long-lasting limitations of ML/NLP-based configuration +validation techniques. In this paper, we present an exploratory analysis on the +feasibility and effectiveness of using LLMs like GPT and Codex for +configuration validation. Specifically, we take a first step to empirically +evaluate LLMs as configuration validators without additional fine-tuning or +code generation. We develop a generic LLM-based validation framework, named +Ciri, which integrates different LLMs. Ciri devises effective prompt +engineering with few-shot learning based on both valid configuration and +misconfiguration data. Ciri also validates and aggregates the outputs of LLMs +to generate validation results, coping with known hallucination and +nondeterminism of LLMs. We evaluate the validation effectiveness of Ciri on +five popular LLMs using configuration data of six mature, widely deployed +open-source systems. Our analysis (1) confirms the potential of using LLMs for +configuration validation, (2) understands the design space of LLMbased +validators like Ciri, especially in terms of prompt engineering with few-shot +learning, and (3) reveals open challenges such as ineffectiveness in detecting +certain types of misconfigurations and biases to popular configuration +parameters. +" +Learning to Prompt for Vision-Language Models,Kaiyang Zhou,http://arxiv.org/pdf/2109.01134v6.pdf,2021-09-02,"['cs.cv', 'cs.ai', 'cs.lg']",2109.01134v6.pdf," Large pre-trained vision-language models like CLIP have shown great potential +in learning representations that are transferable across a wide range of +downstream tasks. Different from the traditional representation learning that +is based mostly on discretized labels, vision-language pre-training aligns +images and texts in a common feature space, which allows zero-shot transfer to +a downstream task via prompting, i.e., classification weights are synthesized +from natural language describing classes of interest. In this work, we show +that a major challenge for deploying such models in practice is prompt +engineering, which requires domain expertise and is extremely time-consuming -- +one needs to spend a significant amount of time on words tuning since a slight +change in wording could have a huge impact on performance. Inspired by recent +advances in prompt learning research in natural language processing (NLP), we +propose Context Optimization (CoOp), a simple approach specifically for +adapting CLIP-like vision-language models for downstream image recognition. +Concretely, CoOp models a prompt's context words with learnable vectors while +the entire pre-trained parameters are kept fixed. To handle different image +recognition tasks, we provide two implementations of CoOp: unified context and +class-specific context. Through extensive experiments on 11 datasets, we +demonstrate that CoOp requires as few as one or two shots to beat hand-crafted +prompts with a decent margin and is able to gain significant improvements over +prompt engineering with more shots, e.g., with 16 shots the average gain is +around 15% (with the highest reaching over 45%). Despite being a learning-based +approach, CoOp achieves superb domain generalization performance compared with +the zero-shot model using hand-crafted prompts. +" +"Prompt-Free Diffusion: Taking ""Text"" out of Text-to-Image Diffusion Models",Xingqian Xu,http://arxiv.org/pdf/2305.16223v2.pdf,2023-05-25,['cs.cv'],2305.16223v2.pdf," Text-to-image (T2I) research has grown explosively in the past year, owing to +the large-scale pre-trained diffusion models and many emerging personalization +and editing approaches. Yet, one pain point persists: the text prompt +engineering, and searching high-quality text prompts for customized results is +more art than science. Moreover, as commonly argued: ""an image is worth a +thousand words"" - the attempt to describe a desired image with texts often ends +up being ambiguous and cannot comprehensively cover delicate visual details, +hence necessitating more additional controls from the visual domain. In this +paper, we take a bold step forward: taking ""Text"" out of a pre-trained T2I +diffusion model, to reduce the burdensome prompt engineering efforts for users. +Our proposed framework, Prompt-Free Diffusion, relies on only visual inputs to +generate new images: it takes a reference image as ""context"", an optional image +structural conditioning, and an initial noise, with absolutely no text prompt. +The core architecture behind the scene is Semantic Context Encoder (SeeCoder), +substituting the commonly used CLIP-based or LLM-based text encoder. The +reusability of SeeCoder also makes it a convenient drop-in component: one can +also pre-train a SeeCoder in one T2I model and reuse it for another. Through +extensive experiments, Prompt-Free Diffusion is experimentally found to (i) +outperform prior exemplar-based image synthesis approaches; (ii) perform on par +with state-of-the-art T2I models using prompts following the best practice; and +(iii) be naturally extensible to other downstream applications such as anime +figure generation and virtual try-on, with promising quality. Our code and +models are open-sourced at https://github.com/SHI-Labs/Prompt-Free-Diffusion. +" +Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models,Robert L. Logan IV,http://arxiv.org/pdf/2106.13353v2.pdf,2021-06-24,"['cs.cl', 'cs.lg']",2106.13353v2.pdf," Prompting language models (LMs) with training examples and task descriptions +has been seen as critical to recent successes in few-shot learning. In this +work, we show that finetuning LMs in the few-shot setting can considerably +reduce the need for prompt engineering. In fact, one can use null prompts, +prompts that contain neither task-specific templates nor training examples, and +achieve competitive accuracy to manually-tuned prompts across a wide range of +tasks. While finetuning LMs does introduce new parameters for each downstream +task, we show that this memory overhead can be substantially reduced: +finetuning only the bias terms can achieve comparable or better accuracy than +standard finetuning while only updating 0.1% of the parameters. All in all, we +recommend finetuning LMs for few-shot learning as it is more accurate, robust +to different prompts, and can be made nearly as efficient as using frozen LMs. +" +An Empirical Study on Few-shot Knowledge Probing for Pretrained Language Models,Tianxing He,http://arxiv.org/pdf/2109.02772v2.pdf,2021-09-06,['cs.ai'],2109.02772v2.pdf," Prompt-based knowledge probing for 1-hop relations has been used to measure +how much world knowledge is stored in pretrained language models. Existing work +uses considerable amounts of data to tune the prompts for better performance. +In this work, we compare a variety of approaches under a few-shot knowledge +probing setting, where only a small number (e.g., 10 or 20) of example triples +are available. In addition, we create a new dataset named TREx-2p, which +contains 2-hop relations. We report that few-shot examples can strongly boost +the probing performance for both 1-hop and 2-hop relations. In particular, we +find that a simple-yet-effective approach of finetuning the bias vectors in the +model outperforms existing prompt-engineering methods. Our dataset and code are +available at \url{https://github.com/cloudygoose/fewshot_lama}. +" +Design Guidelines for Prompt Engineering Text-to-Image Generative Models,Vivian Liu,http://arxiv.org/pdf/2109.06977v3.pdf,2021-09-14,['cs.hc'],2109.06977v3.pdf," Text-to-image generative models are a new and powerful way to generate visual +artwork. However, the open-ended nature of text as interaction is double-edged; +while users can input anything and have access to an infinite range of +generations, they also must engage in brute-force trial and error with the text +prompt when the result quality is poor. We conduct a study exploring what +prompt keywords and model hyperparameters can help produce coherent outputs. In +particular, we study prompts structured to include subject and style keywords +and investigate success and failure modes of these prompts. Our evaluation of +5493 generations over the course of five experiments spans 51 abstract and +concrete subjects as well as 51 abstract and figurative styles. From this +evaluation, we present design guidelines that can help people produce better +outcomes from text-to-image generative models. +" +Cut the CARP: Fishing for zero-shot story evaluation,Shahbuland Matiana,http://arxiv.org/pdf/2110.03111v3.pdf,2021-10-06,['cs.cl'],2110.03111v3.pdf," Recent advances in large-scale language models (Raffel et al., 2019; Brown et +al., 2020) have brought significant qualitative and quantitative improvements +in machine-driven text generation. Despite this, generation and evaluation of +machine-generated narrative text remains a challenging problem. Objective +evaluation of computationally-generated stories may be prohibitively expensive, +require meticulously annotated datasets, or may not adequately measure the +logical coherence of a generated story's narratological structure. + Informed by recent advances in contrastive learning (Radford et al., 2021), +we present Contrastive Authoring and Reviewing Pairing (CARP): a scalable, +efficient method for performing qualitatively superior, zero-shot evaluation of +stories. We show a strong correlation between human evaluation of stories and +those of CARP. Model outputs more significantly correlate with corresponding +human input than those language-model based methods which utilize finetuning or +prompt engineering approaches. We also present and analyze the Story-Critique +Dataset, a new corpora composed of 1.3 million aligned story-critique pairs +derived from over 80,000 stories. We expect this corpus to be of interest to +NLP researchers. +" +Solving Probability and Statistics Problems by Program Synthesis,Leonard Tang,http://arxiv.org/pdf/2111.08267v1.pdf,2021-11-16,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.pl']",2111.08267v1.pdf," We solve university level probability and statistics questions by program +synthesis using OpenAI's Codex, a Transformer trained on text and fine-tuned on +code. We transform course problems from MIT's 18.05 Introduction to Probability +and Statistics and Harvard's STAT110 Probability into programming tasks. We +then execute the generated code to get a solution. Since these course questions +are grounded in probability, we often aim to have Codex generate probabilistic +programs that simulate a large number of probabilistic dependencies to compute +its solution. Our approach requires prompt engineering to transform the +question from its original form to an explicit, tractable form that results in +a correct program and solution. To estimate the amount of work needed to +translate an original question into its tractable form, we measure the +similarity between original and transformed questions. Our work is the first to +introduce a new dataset of university-level probability and statistics problems +and solve these problems in a scalable fashion using the program synthesis +capabilities of large language models. +" +StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation,Umut Kocasari,http://arxiv.org/pdf/2112.08493v1.pdf,2021-12-15,"['cs.cv', 'cs.lg']",2112.08493v1.pdf," Discovering meaningful directions in the latent space of GANs to manipulate +semantic attributes typically requires large amounts of labeled data. Recent +work aims to overcome this limitation by leveraging the power of Contrastive +Language-Image Pre-training (CLIP), a joint text-image model. While promising, +these methods require several hours of preprocessing or training to achieve the +desired manipulations. In this paper, we present StyleMC, a fast and efficient +method for text-driven image generation and manipulation. StyleMC uses a +CLIP-based loss and an identity loss to manipulate images via a single text +prompt without significantly affecting other attributes. Unlike prior work, +StyleMC requires only a few seconds of training per text prompt to find stable +global directions, does not require prompt engineering and can be used with any +pre-trained StyleGAN2 model. We demonstrate the effectiveness of our method and +compare it to state-of-the-art methods. Our code can be found at +http://catlab-team.github.io/stylemc. +" +QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition,Andy T. Liu,http://arxiv.org/pdf/2203.01543v2.pdf,2022-03-03,"['cs.cl', 'cs.ai', 'cs.lg']",2203.01543v2.pdf," Recently, prompt-based learning for pre-trained language models has succeeded +in few-shot Named Entity Recognition (NER) by exploiting prompts as task +guidance to increase label efficiency. However, previous prompt-based methods +for few-shot NER have limitations such as a higher computational complexity, +poor zero-shot ability, requiring manual prompt engineering, or lack of prompt +robustness. In this work, we address these shortcomings by proposing a new +prompt-based learning NER method with Question Answering (QA), called QaNER. +Our approach includes 1) a refined strategy for converting NER problems into +the QA formulation; 2) NER prompt generation for QA models; 3) prompt-based +tuning with QA models on a few annotated NER examples; 4) zero-shot NER by +prompting the QA model. Comparing the proposed approach with previous methods, +QaNER is faster at inference, insensitive to the prompt quality, and robust to +hyper-parameters, as well as demonstrating significantly better low-resource +performance and zero-shot capability. +" +Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?,Chris Lengerich,http://arxiv.org/pdf/2204.12639v1.pdf,2022-04-27,['cs.cl'],2204.12639v1.pdf," We develop the few-shot continual learning task from first principles and +hypothesize an evolutionary motivation and mechanism of action for executive +function as a contrastive value policy which resamples and relabels perception +data via hindsight summarization to minimize attended prediction error, similar +to an online prompt engineering problem. This is made feasible by the use of a +memory policy and a pretrained network with inductive biases for a grammar of +learning and is trained to maximize evolutionary survival. We show how this +model of executive function can be used to implement hypothesis testing as a +stream of consciousness and may explain observations of human few-shot learning +and neuroanatomy. +" +Polyglot Prompt: Multilingual Multitask PrompTraining,Jinlan Fu,http://arxiv.org/pdf/2204.14264v2.pdf,2022-04-29,['cs.cl'],2204.14264v2.pdf," This paper aims for a potential architectural improvement for multilingual +learning and asks: Can different tasks from different languages be modeled in a +monolithic framework, i.e. without any task/language-specific module? The +benefit of achieving this could open new doors for future multilingual +research, including allowing systems trained on low resources to be further +assisted by other languages as well as other tasks. We approach this goal by +developing a learning framework named Polyglot Prompting to exploit prompting +methods for learning a unified semantic space for different languages and tasks +with multilingual prompt engineering. We performed a comprehensive evaluation +of 6 tasks, namely topic classification, sentiment classification, named entity +recognition, question answering, natural language inference, and summarization, +covering 24 datasets and 49 languages. The experimental results demonstrated +the efficacy of multilingual multitask prompt-based learning and led to +inspiring observations. We also present an interpretable multilingual +evaluation methodology and show how the proposed framework, multilingual +multitask prompt training, works. We release all datasets prompted in the best +setting and code. +" +CLIP-CLOP: CLIP-Guided Collage and Photomontage,Piotr Mirowski,http://arxiv.org/pdf/2205.03146v3.pdf,2022-05-06,"['cs.cv', 'cs.ai']",2205.03146v3.pdf," The unabated mystique of large-scale neural networks, such as the CLIP dual +image-and-text encoder, popularized automatically generated art. Increasingly +more sophisticated generators enhanced the artworks' realism and visual +appearance, and creative prompt engineering enabled stylistic expression. +Guided by an artist-in-the-loop ideal, we design a gradient-based generator to +produce collages. It requires the human artist to curate libraries of image +patches and to describe (with prompts) the whole image composition, with the +option to manually adjust the patches' positions during generation, thereby +allowing humans to reclaim some control of the process and achieve greater +creative freedom. We explore the aesthetic potentials of high-resolution +collages, and provide an open-source Google Colab as an artistic tool. +" +Toxicity Detection with Generative Prompt-based Inference,Yau-Shian Wang,http://arxiv.org/pdf/2205.12390v1.pdf,2022-05-24,"['cs.cl', 'cs.ai']",2205.12390v1.pdf," Due to the subtleness, implicity, and different possible interpretations +perceived by different people, detecting undesirable content from text is a +nuanced difficulty. It is a long-known risk that language models (LMs), once +trained on corpus containing undesirable content, have the power to manifest +biases and toxicity. However, recent studies imply that, as a remedy, LMs are +also capable of identifying toxic content without additional fine-tuning. +Prompt-methods have been shown to effectively harvest this surprising +self-diagnosing capability. However, existing prompt-based methods usually +specify an instruction to a language model in a discriminative way. In this +work, we explore the generative variant of zero-shot prompt-based toxicity +detection with comprehensive trials on prompt engineering. We evaluate on three +datasets with toxicity labels annotated on social media posts. Our analysis +highlights the strengths of our generative classification approach both +quantitatively and qualitatively. Interesting aspects of self-diagnosis and its +ethical implications are discussed. +" +The Creativity of Text-to-Image Generation,Jonas Oppenlaender,http://arxiv.org/pdf/2206.02904v4.pdf,2022-05-13,"['cs.hc', 'cs.gr', 'h.5; h.m']",2206.02904v4.pdf," Text-guided synthesis of images has made a giant leap towards becoming a +mainstream phenomenon. With text-to-image generation systems, anybody can +create digital images and artworks. This provokes the question of whether +text-to-image generation is creative. This paper expounds on the nature of +human creativity involved in text-to-image art (so-called ""AI art"") with a +specific focus on the practice of prompt engineering. The paper argues that the +current product-centered view of creativity falls short in the context of +text-to-image generation. A case exemplifying this shortcoming is provided and +the importance of online communities for the creative ecosystem of +text-to-image art is highlighted. The paper provides a high-level summary of +this online ecosystem drawing on Rhodes' conceptual four P model of creativity. +Challenges for evaluating the creativity of text-to-image generation and +opportunities for research on text-to-image generation in the field of +Human-Computer Interaction (HCI) are discussed. +" +Rationale-Augmented Ensembles in Language Models,Xuezhi Wang,http://arxiv.org/pdf/2207.00747v1.pdf,2022-07-02,['cs.cl'],2207.00747v1.pdf," Recent research has shown that rationales, or step-by-step chains of thought, +can be used to improve performance in multi-step reasoning tasks. We reconsider +rationale-augmented prompting for few-shot in-context learning, where (input -> +output) prompts are expanded to (input, rationale -> output) prompts. For +rationale-augmented prompting we demonstrate how existing approaches, which +rely on manual prompt engineering, are subject to sub-optimal rationales that +may harm performance. To mitigate this brittleness, we propose a unified +framework of rationale-augmented ensembles, where we identify rationale +sampling in the output space as the key component to robustly improve +performance. This framework is general and can easily be extended to common +natural language processing tasks, even those that do not traditionally +leverage intermediate steps, such as question answering, word sense +disambiguation, and sentiment analysis. We demonstrate that rationale-augmented +ensembles achieve more accurate and interpretable results than existing +prompting approaches--including standard prompting without rationales and +rationale-based chain-of-thought prompting--while simultaneously improving +interpretability of model predictions through the associated rationales. +" +Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models,Robin Rombach,http://arxiv.org/pdf/2207.13038v1.pdf,2022-07-26,['cs.cv'],2207.13038v1.pdf," Novel architectures have recently improved generative image synthesis leading +to excellent visual quality in various tasks. Of particular note is the field +of ``AI-Art'', which has seen unprecedented growth with the emergence of +powerful multimodal models such as CLIP. By combining speech and image +synthesis models, so-called ``prompt-engineering'' has become established, in +which carefully selected and composed sentences are used to achieve a certain +visual style in the synthesized image. In this note, we present an alternative +approach based on retrieval-augmented diffusion models (RDMs). In RDMs, a set +of nearest neighbors is retrieved from an external database during training for +each training instance, and the diffusion model is conditioned on these +informative samples. During inference (sampling), we replace the retrieval +database with a more specialized database that contains, for example, only +images of a particular visual style. This provides a novel way to prompt a +general trained model after training and thereby specify a particular visual +style. As shown by our experiments, this approach is superior to specifying the +visual style within the text prompt. We open-source code and model weights at +https://github.com/CompVis/latent-diffusion . +" +Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models,Hendrik Strobelt,http://arxiv.org/pdf/2208.07852v1.pdf,2022-08-16,"['cs.cl', 'cs.hc', 'cs.lg']",2208.07852v1.pdf," State-of-the-art neural language models can now be used to solve ad-hoc +language tasks through zero-shot prompting without the need for supervised +training. This approach has gained popularity in recent years, and researchers +have demonstrated prompts that achieve strong accuracy on specific NLP tasks. +However, finding a prompt for new tasks requires experimentation. Different +prompt templates with different wording choices lead to significant accuracy +differences. PromptIDE allows users to experiment with prompt variations, +visualize prompt performance, and iteratively optimize prompts. We developed a +workflow that allows users to first focus on model feedback using small data +before moving on to a large data regime that allows empirical grounding of +promising prompts using quantitative measures of the task. The tool then allows +easy deployment of the newly created ad-hoc models. We demonstrate the utility +of PromptIDE (demo at http://prompt.vizhub.ai) and our workflow using several +real-world use cases. +" +Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction,Michiel van der Meer,http://arxiv.org/pdf/2209.08966v2.pdf,2022-09-19,"['cs.cl', 'cs.ai']",2209.08966v2.pdf," This paper describes our contributions to the Shared Task of the 9th Workshop +on Argument Mining (2022). Our approach uses Large Language Models for the task +of Argument Quality Prediction. We perform prompt engineering using GPT-3, and +also investigate the training paradigms multi-task learning, contrastive +learning, and intermediate-task training. We find that a mixed prediction setup +outperforms single models. Prompting GPT-3 works best for predicting argument +validity, and argument novelty is best estimated by a model trained using all +three training paradigms. +" +Legal Prompting: Teaching a Language Model to Think Like a Lawyer,Fangyi Yu,http://arxiv.org/pdf/2212.01326v2.pdf,2022-12-02,"['cs.cl', 'cs.ai', 'i.2.7']",2212.01326v2.pdf," Large language models that are capable of zero or few-shot prompting +approaches have given rise to the new research area of prompt engineering. +Recent advances showed that for example Chain-of-Thought (CoT) prompts can +improve arithmetic or common sense tasks significantly. We explore how such +approaches fare with legal reasoning tasks and take the COLIEE entailment task +based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning +approaches. Our findings show that while CoT prompting and fine-tuning with +explanations approaches show improvements, the best results are produced by +prompts that are derived from specific legal reasoning techniques such as IRAC +(Issue, Rule, Application, Conclusion). Based on our experiments we improve the +2021 best result from 0.7037 accuracy to 0.8148 accuracy and beat the 2022 best +system of 0.6789 accuracy with an accuracy of 0.7431. +" +Controllable Image Captioning via Prompting,Ning Wang,http://arxiv.org/pdf/2212.01803v1.pdf,2022-12-04,['cs.cv'],2212.01803v1.pdf," Despite the remarkable progress of image captioning, existing captioners +typically lack the controllable capability to generate desired image captions, +e.g., describing the image in a rough or detailed manner, in a factual or +emotional view, etc. In this paper, we show that a unified model is qualified +to perform well in diverse domains and freely switch among multiple styles. +Such a controllable capability is achieved by embedding the prompt learning +into the image captioning framework. To be specific, we design a set of prompts +to fine-tune the pre-trained image captioner. These prompts allow the model to +absorb stylized data from different domains for joint training, without +performance degradation in each domain. Furthermore, we optimize the prompts +with learnable vectors in the continuous word embedding space, avoiding the +heuristic prompt engineering and meanwhile exhibiting superior performance. In +the inference stage, our model is able to generate desired stylized captions by +choosing the corresponding prompts. Extensive experiments verify the +controllable capability of the proposed method. Notably, we achieve outstanding +performance on two diverse image captioning benchmarks including COCO Karpathy +split and TextCaps using a unified model. +" +Fake it till you make it: Learning transferable representations from synthetic ImageNet clones,Mert Bulent Sariyildiz,http://arxiv.org/pdf/2212.08420v2.pdf,2022-12-16,"['cs.cv', 'cs.lg']",2212.08420v2.pdf," Recent image generation models such as Stable Diffusion have exhibited an +impressive ability to generate fairly realistic images starting from a simple +text prompt. Could such models render real images obsolete for training image +prediction models? In this paper, we answer part of this provocative question +by investigating the need for real images when training models for ImageNet +classification. Provided only with the class names that have been used to build +the dataset, we explore the ability of Stable Diffusion to generate synthetic +clones of ImageNet and measure how useful these are for training classification +models from scratch. We show that with minimal and class-agnostic prompt +engineering, ImageNet clones are able to close a large part of the gap between +models produced by synthetic images and models trained with real images, for +the several standard classification benchmarks that we consider in this study. +More importantly, we show that models trained on synthetic images exhibit +strong generalization properties and perform on par with models trained on real +data for transfer. Project page: https://europe.naverlabs.com/imagenet-sd/ +" +Explanation Regeneration via Information Bottleneck,Qintong Li,http://arxiv.org/pdf/2212.09603v2.pdf,2022-12-19,['cs.cl'],2212.09603v2.pdf," Explaining the black-box predictions of NLP models naturally and accurately +is an important open problem in natural language generation. These free-text +explanations are expected to contain sufficient and carefully-selected evidence +to form supportive arguments for predictions. Due to the superior generative +capacity of large pretrained language models, recent work built on prompt +engineering enables explanation generation without specific training. However, +explanation generated through single-pass prompting often lacks sufficiency and +conciseness. To address this problem, we develop an information bottleneck +method EIB to produce refined explanations that are sufficient and concise. Our +approach regenerates the free-text explanation by polishing the single-pass +output from the pretrained language model but retaining the information that +supports the contents being explained. Experiments on two out-of-domain tasks +verify the effectiveness of EIB through automatic evaluation and +thoroughly-conducted human evaluation. +" +Optimizing Prompts for Text-to-Image Generation,Yaru Hao,http://arxiv.org/pdf/2212.09611v1.pdf,2022-12-19,"['cs.cl', 'cs.cv']",2212.09611v1.pdf," Well-designed prompts can guide text-to-image models to generate amazing +images. However, the performant prompts are often model-specific and misaligned +with user input. Instead of laborious human engineering, we propose prompt +adaptation, a general framework that automatically adapts original user input +to model-preferred prompts. Specifically, we first perform supervised +fine-tuning with a pretrained language model on a small collection of manually +engineered prompts. Then we use reinforcement learning to explore better +prompts. We define a reward function that encourages the policy to generate +more aesthetically pleasing images while preserving the original user +intentions. Experimental results on Stable Diffusion show that our method +outperforms manual prompt engineering in terms of both automatic metrics and +human preference ratings. Moreover, reinforcement learning further boosts +performance, especially on out-of-domain prompts. The pretrained checkpoints +are available at https://aka.ms/promptist. The demo can be found at +https://aka.ms/promptist-demo. +" +Using Large Language Models to Generate Engaging Captions for Data Visualizations,Ashley Liew,http://arxiv.org/pdf/2212.14047v1.pdf,2022-12-27,"['cs.cl', 'cs.ai', 'cs.hc']",2212.14047v1.pdf," Creating compelling captions for data visualizations has been a longstanding +challenge. Visualization researchers are typically untrained in journalistic +reporting and hence the captions that are placed below data visualizations tend +to be not overly engaging and rather just stick to basic observations about the +data. In this work we explore the opportunities offered by the newly emerging +crop of large language models (LLM) which use sophisticated deep learning +technology to produce human-like prose. We ask, can these powerful software +devices be purposed to produce engaging captions for generic data +visualizations like a scatterplot. It turns out that the key challenge lies in +designing the most effective prompt for the LLM, a task called prompt +engineering. We report on first experiments using the popular LLM GPT-3 and +deliver some promising results. +" +Fixing Hardware Security Bugs with Large Language Models,Baleegh Ahmad,http://arxiv.org/pdf/2302.01215v1.pdf,2023-02-02,['cs.cr'],2302.01215v1.pdf," Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI's +Codex have demonstrated capabilities in many coding-adjacent domains. In this +work we consider how LLMs maybe leveraged to automatically repair security +relevant bugs present in hardware designs. We focus on bug repair in code +written in the Hardware Description Language Verilog. For this study we build a +corpus of domain-representative hardware security bugs. We then design and +implement a framework to quantitatively evaluate the performance of any LLM +tasked with fixing the specified bugs. The framework supports design space +exploration of prompts (i.e., prompt engineering) and identifying the best +parameters for the LLM. We show that an ensemble of LLMs can repair all ten of +our benchmarks. This ensemble outperforms the state-of-the-art Cirfix hardware +bug repair tool on its own suite of bugs. These results show that LLMs can +repair hardware security bugs and the framework is an important step towards +the ultimate goal of an automated end-to-end bug repair framework. +" +UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation,Daixuan Cheng,http://arxiv.org/pdf/2303.08518v3.pdf,2023-03-15,['cs.cl'],2303.08518v3.pdf," Large Language Models (LLMs) are popular for their impressive abilities, but +the need for model-specific fine-tuning or task-specific prompt engineering can +hinder their generalization. We propose UPRISE (Universal Prompt Retrieval for +Improving zero-Shot Evaluation), which tunes a lightweight and versatile +retriever that automatically retrieves prompts for a given zero-shot task +input. Specifically, we demonstrate universality in a cross-task and +cross-model scenario: the retriever is tuned on a diverse set of tasks, but +tested on unseen task types; we use a small frozen LLM, GPT-Neo-2.7B, for +tuning the retriever, but test the retriever on different LLMs of much larger +scales, such as BLOOM-7.1B, OPT-66B and GPT3-175B. Additionally, we show that +UPRISE mitigates the hallucination problem in our experiments with ChatGPT, +suggesting its potential to improve even the strongest LLMs. Our model and code +are available at https://github.com/microsoft/LMOps. +" +Patch-Token Aligned Bayesian Prompt Learning for Vision-Language Models,Xinyang Liu,http://arxiv.org/pdf/2303.09100v1.pdf,2023-03-16,"['cs.cv', 'cs.cl', 'cs.lg']",2303.09100v1.pdf," For downstream applications of vision-language pre-trained models, there has +been significant interest in constructing effective prompts. Existing works on +prompt engineering, which either require laborious manual designs or optimize +the prompt tuning as a point estimation problem, may fail to describe diverse +characteristics of categories and limit their applications. We introduce a +Bayesian probabilistic resolution to prompt learning, where the label-specific +stochastic prompts are generated hierarchically by first sampling a latent +vector from an underlying distribution and then employing a lightweight +generative model. Importantly, we semantically regularize prompt learning with +the visual knowledge and view images and the corresponding prompts as patch and +token sets under optimal transport, which pushes the prompt tokens to +faithfully capture the label-specific visual concepts, instead of overfitting +the training categories. Moreover, the proposed model can also be +straightforwardly extended to the conditional case where the +instance-conditional prompts are generated to improve the generalizability. +Extensive experiments on 15 datasets show promising transferability and +generalization performance of our proposed model. +" +Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT,Yi Qi,http://arxiv.org/pdf/2304.01246v2.pdf,2023-04-03,"['cs.cl', 'cs.ai', 'cs.cy', 'cs.se']",2304.01246v2.pdf," Can safety analysis make use of Large Language Models (LLMs)? A case study +explores Systems Theoretic Process Analysis (STPA) applied to Automatic +Emergency Brake (AEB) and Electricity Demand Side Management (DSM) systems +using ChatGPT. We investigate how collaboration schemes, input semantic +complexity, and prompt guidelines influence STPA results. Comparative results +show that using ChatGPT without human intervention may be inadequate due to +reliability related issues, but with careful design, it may outperform human +experts. No statistically significant differences are found when varying the +input semantic complexity or using common prompt guidelines, which suggests the +necessity for developing domain-specific prompt engineering. We also highlight +future challenges, including concerns about LLM trustworthiness and the +necessity for standardisation and regulation in this domain. +" +Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering,Krishna Kumar,http://arxiv.org/pdf/2304.02138v3.pdf,2023-04-04,"['cs.cl', 'physics.geo-ph', 'i.2.7; j.2.6']",2304.02138v3.pdf," The widespread adoption of large language models (LLMs), such as OpenAI's +ChatGPT, could revolutionize various industries, including geotechnical +engineering. However, GPT models can sometimes generate plausible-sounding but +false outputs, leading to hallucinations. In this article, we discuss the +importance of prompt engineering in mitigating these risks and harnessing the +full potential of GPT for geotechnical applications. We explore the challenges +and pitfalls associated with LLMs and highlight the role of context in ensuring +accurate and valuable responses. Furthermore, we examine the development of +context-specific search engines and the potential of LLMs to become a natural +interface for complex tasks, such as data analysis and design. We also develop +a unified interface using natural language to handle complex geotechnical +engineering tasks and data analysis. By integrating GPT into geotechnical +engineering workflows, professionals can streamline their work and develop +sustainable and resilient infrastructure systems for the future. +" +Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification,Shan Chen,http://arxiv.org/pdf/2304.02496v1.pdf,2023-04-05,"['cs.cl', 'cs.ai']",2304.02496v1.pdf," Recent advances in large language models (LLMs) have shown impressive ability +in biomedical question-answering, but have not been adequately investigated for +more specific biomedical applications. This study investigates the performance +of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical +tasks beyond question-answering. Because no patient data can be passed to the +OpenAI API public interface, we evaluated model performance with over 10000 +samples as proxies for two fundamental tasks in the clinical domain - +classification and reasoning. The first task is classifying whether statements +of clinical and policy recommendations in scientific literature constitute +health advice. The second task is causal relation detection from the biomedical +literature. We compared LLMs with simpler models, such as bag-of-words (BoW) +with logistic regression, and fine-tuned BioBERT models. Despite the excitement +around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks +remained the best strategy. The simple BoW model performed on par with the most +complex LLM prompting. Prompt engineering required significant investment. +" +"VOICE: Visual Oracle for Interaction, Conversation, and Explanation",Donggang Jia,http://arxiv.org/pdf/2304.04083v1.pdf,2023-04-08,"['cs.hc', 'cs.gr']",2304.04083v1.pdf," We present VOICE, a novel approach for connecting large language models' +(LLM) conversational capabilities with interactive exploratory visualization. +VOICE introduces several innovative technical contributions that drive our +conversational visualization framework. Our foundation is a pack-of-bots that +can perform specific tasks, such as assigning tasks, extracting instructions, +and generating coherent content. We employ fine-tuning and prompt engineering +techniques to tailor bots' performance to their specific roles and accurately +respond to user queries, and a new prompt-based iterative scene-tree generation +establishes a coupling with a structural model. Our text-to-visualization +method generates a flythrough sequence matching the content explanation. +Finally, 3D natural language interaction provides capabilities to navigate and +manipulate the 3D models in real-time. The VOICE framework can receive +arbitrary voice commands from the user and responds verbally, tightly coupled +with corresponding visual representation with low latency and high accuracy. We +demonstrate the effectiveness and high generalizability potential of our +approach by applying it to two distinct domains: analyzing three 3D molecular +models with multi-scale and multi-instance attributes, and showcasing its +effectiveness on a cartographic map visualization. A free copy of this paper +and all supplemental materials are available at https://osf.io/g7fbr/. +" +Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization,Puyuan Peng,http://arxiv.org/pdf/2305.11095v3.pdf,2023-05-18,"['eess.as', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.sd']",2305.11095v3.pdf," We investigate the emergent abilities of the recently proposed web-scale +speech model Whisper, by adapting it to unseen tasks with prompt engineering. +We selected three tasks: audio-visual speech recognition (AVSR), code-switched +speech recognition (CS-ASR), and speech translation (ST) on unseen language +pairs. We design task-specific prompts, by either leveraging another +large-scale model, or simply manipulating the special tokens in the default +prompts. Experiments show that compared to the default prompts, our proposed +prompts improve performance by 10% to 45% on the three zero-shot tasks, and +even outperform SotA supervised models on some datasets. In addition, our +experiments reveal many interesting properties of Whisper, including its +robustness to prompts, bias on accents, and the multilingual understanding in +its latent space. Code is available at +https://github.com/jasonppy/PromptingWhisper +" +Constructing Dreams using Generative AI,Safinah Ali,http://arxiv.org/pdf/2305.12013v1.pdf,2023-05-19,"['cs.hc', 'cs.ai', 'cs.cy']",2305.12013v1.pdf," Generative AI tools introduce new and accessible forms of media creation for +youth. They also raise ethical concerns about the generation of fake media, +data protection, privacy and ownership of AI-generated art. Since generative AI +is already being used in products used by youth, it is critical that they +understand how these tools work and how they can be used or misused. In this +work, we facilitated students' generative AI learning through expression of +their imagined future identities. We designed a learning workshop - Dreaming +with AI - where students learned about the inner workings of generative AI +tools, used text-to-image generation algorithms to create their imaged future +dreams, reflected on the potential benefits and harms of generative AI tools +and voiced their opinions about policies for the use of these tools in +classrooms. In this paper, we present the learning activities and experiences +of 34 high school students who engaged in our workshops. Students reached +creative learning objectives by using prompt engineering to create their future +dreams, gained technical knowledge by learning the abilities, limitations, +text-visual mappings and applications of generative AI, and identified most +potential societal benefits and harms of generative AI. +" +Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration,Qifan Yu,http://arxiv.org/pdf/2305.12799v1.pdf,2023-05-22,['cs.cv'],2305.12799v1.pdf," Recent text-to-image generation models have shown promising results in +generating high-fidelity photo-realistic images. In parallel, the problem of +data scarcity has brought a growing interest in employing AIGC technology for +high-quality data expansion. However, this paradigm requires well-designed +prompt engineering that cost-less data expansion and labeling remain +under-explored. Inspired by LLM's powerful capability in task guidance, we +propose a new paradigm of annotated data expansion named as ChatGenImage. The +core idea behind it is to leverage the complementary strengths of diverse +models to establish a highly effective and user-friendly pipeline for +interactive data augmentation. In this work, we extensively study how LLMs +communicate with AIGC model to achieve more controllable image generation and +make the first attempt to collaborate them for automatic data augmentation for +a variety of downstream tasks. Finally, we present fascinating results obtained +from our ChatGenImage framework and demonstrate the powerful potential of our +synthetic data for systematic vision adaptation. Our codes are available at +https://github.com/Yuqifan1117/Labal-Anything-Pipeline. +" +Making Language Models Better Tool Learners with Execution Feedback,Shuofei Qiao,http://arxiv.org/pdf/2305.13068v1.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.hc', 'cs.ir', 'cs.lg']",2305.13068v1.pdf," Tools serve as pivotal interfaces that enable humans to understand and +reshape the world. With the advent of foundational models, AI systems can +utilize tools to expand their capabilities and interact with the world. +Existing tool learning methodologies, encompassing supervised fine-tuning and +prompt engineering approaches, often induce language models to utilize tools +indiscriminately, as complex problems often exceed their own competencies. +However, introducing tools for simple tasks, which the models themselves can +readily resolve, can inadvertently propagate errors rather than enhance +performance. This leads to the research question: can we teach language models +when and how to use tools? To meet this need, we propose Tool leaRning wIth +exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the +model to continually learn through feedback derived from tool execution, +thereby learning when and how to use tools effectively. Experimental results, +backed by further analysis, show that TRICE can make the language model to +selectively use tools by decreasing the model's dependency on tools while +enhancing the performance. Code and datasets will be available in +https://github.com/zjunlp/trice. +" +Prompt position really matters in few-shot and zero-shot NLU tasks,Junyu Mao,http://arxiv.org/pdf/2305.14493v2.pdf,2023-05-23,['cs.cl'],2305.14493v2.pdf," Prompt-based models have made remarkable advancements in the fields of +zero-shot and few-shot learning, attracting a lot of attention from +researchers. Developing an effective prompt template plays a critical role. +However, prior studies have mainly focused on prompt vocabulary selection or +embedding initialization with the reserved prompt position fixed. In this +empirical study, we conduct the most comprehensive analysis to date of prompt +position option for natural language understanding tasks. Our findings quantify +the substantial impact prompt position has on model performance. We observe +that the prompt position used in prior studies is often sub-optimal for both +zero-shot and few-shot settings. These findings suggest prompt position +optimisation as an interesting research direction alongside the existing focus +on prompt engineering. +" +ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER,Amirhossein Layegh,http://arxiv.org/pdf/2305.17951v1.pdf,2023-05-29,"['cs.cl', 'cs.ai']",2305.17951v1.pdf," Prompt-based language models have produced encouraging results in numerous +applications, including Named Entity Recognition (NER) tasks. NER aims to +identify entities in a sentence and provide their types. However, the strong +performance of most available NER approaches is heavily dependent on the design +of discrete prompts and a verbalizer to map the model-predicted outputs to +entity categories, which are complicated undertakings. To address these +challenges, we present ContrastNER, a prompt-based NER framework that employs +both discrete and continuous tokens in prompts and uses a contrastive learning +approach to learn the continuous prompts and forecast entity types. The +experimental results demonstrate that ContrastNER obtains competitive +performance to the state-of-the-art NER methods in high-resource settings and +outperforms the state-of-the-art models in low-resource circumstances without +requiring extensive manual prompt engineering and verbalizer design. +" +Conformal Prediction with Large Language Models for Multi-Choice Question Answering,Bhawesh Kumar,http://arxiv.org/pdf/2305.18404v3.pdf,2023-05-28,"['cs.cl', 'cs.lg', 'stat.ml']",2305.18404v3.pdf," As large language models continue to be widely developed, robust uncertainty +quantification techniques will become crucial for their safe deployment in +high-stakes scenarios. In this work, we explore how conformal prediction can be +used to provide uncertainty quantification in language models for the specific +task of multiple-choice question-answering. We find that the uncertainty +estimates from conformal prediction are tightly correlated with prediction +accuracy. This observation can be useful for downstream applications such as +selective classification and filtering out low-quality predictions. We also +investigate the exchangeability assumption required by conformal prediction to +out-of-subject questions, which may be a more realistic scenario for many +practical applications. Our work contributes towards more trustworthy and +reliable usage of large language models in safety-critical situations, where +robust guarantees of error rate are required. +" +Test-Time Training on Nearest Neighbors for Large Language Models,Moritz Hardt,http://arxiv.org/pdf/2305.18466v2.pdf,2023-05-29,"['cs.cl', 'cs.lg']",2305.18466v2.pdf," Many recent efforts aim to augment language models with relevant information +retrieved from a database at test time. We avoid the need for prompt +engineering by directly fine-tuning the model on data retrieved at test time +using its standard training setup. For this purpose, we build a large-scale +distributed nearest neighbor index based on text embeddings of the Pile +dataset. Given a query to a language model, our system retrieves the neighbors +of the query and fine-tunes the model on the text data corresponding to those +neighbors. Surprisingly, retrieving and training on as few as 20 neighbors, +each for only one gradient iteration, drastically improves performance across +more than twenty language modeling tasks in the Pile benchmark. For example, +test-time training significantly narrows the performance gap between a small +GPT2 model and a GPTNeo model, more than ten times larger, that was +specifically trained to convergence on the Pile. Sufficient index quality and +size, however, are important. Our work establishes a valuable first baseline +for implementing test-time training in the context of large language models, +opening the door to numerous promising research avenues. +" +CONA: A novel CONtext-Aware instruction paradigm for communication using large language model,Nan Zhou,http://arxiv.org/pdf/2305.18620v1.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.hc']",2305.18620v1.pdf," We introduce CONA, a novel context-aware instruction paradigm for effective +knowledge dissemination using generative pre-trained transformer (GPT) models. +CONA is a flexible framework designed to leverage the capabilities of Large +Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge, +Wisdom) hierarchy to automatically instruct and optimise presentation content, +anticipate potential audience inquiries, and provide context-aware answers that +adaptive to the knowledge level of the audience group. The unique aspect of the +CONA paradigm lies in its combination of an independent advisory mechanism and +a recursive feedback loop rooted on the DIKW hierarchy. This synergy +significantly enhances context-aware contents, ensuring they are accessible and +easily comprehended by the audience. This paradigm is an early pioneer to +explore new methods for knowledge dissemination and communication in the LLM +era, offering effective support for everyday knowledge sharing scenarios. We +conduct experiments on a range of audience roles, along with materials from +various disciplines using GPT4. Both quantitative and qualitative results +demonstrated that the proposed CONA paradigm achieved remarkable performance +compared to the outputs guided by conventional prompt engineering. +" +GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction,Rui Yang,http://arxiv.org/pdf/2305.18752v1.pdf,2023-05-30,"['cs.cv', 'cs.cl']",2305.18752v1.pdf," This paper aims to efficiently enable Large Language Models (LLMs) to use +multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have +shown great potential for tool usage through sophisticated prompt engineering. +Nevertheless, these models typically rely on prohibitive computational costs +and publicly inaccessible data. To address these challenges, we propose the +GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and +OPT, to use tools. It generates an instruction-following dataset by prompting +an advanced teacher with various multi-modal contexts. By using the Low-Rank +Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs +to solve a range of visual problems, including visual comprehension and image +generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to +use tools, which is performed in both zero-shot and fine-tuning ways. Extensive +experiments demonstrate the effectiveness of our method on various language +models, which not only significantly improves the accuracy of invoking seen +tools, but also enables the zero-shot capacity for unseen tools. The code and +demo are available at https://github.com/StevenGrove/GPT4Tools. +" +Contextualizing Problems to Student Interests at Scale in Intelligent Tutoring System Using Large Language Models,Gautam Yadav,http://arxiv.org/pdf/2306.00190v1.pdf,2023-05-31,['cs.hc'],2306.00190v1.pdf," Contextualizing problems to align with student interests can significantly +improve learning outcomes. However, this task often presents scalability +challenges due to resource and time constraints. Recent advancements in Large +Language Models (LLMs) like GPT-4 offer potential solutions to these issues. +This study explores the ability of GPT-4 in the contextualization of problems +within CTAT, an intelligent tutoring system, aiming to increase student +engagement and enhance learning outcomes. Through iterative prompt engineering, +we achieved meaningful contextualization that preserved the difficulty and +original intent of the problem, thereby not altering values or overcomplicating +the questions. While our research highlights the potential of LLMs in +educational settings, we acknowledge current limitations, particularly with +geometry problems, and emphasize the need for ongoing evaluation and research. +Future work includes systematic studies to measure the impact of this tool on +students' learning outcomes and enhancements to handle a broader range of +problems. +" +Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective,David James Woo,http://arxiv.org/pdf/2306.01798v1.pdf,2023-06-01,"['cs.cy', 'cs.ai']",2306.01798v1.pdf," This study applies Activity Theory to investigate how English as a foreign +language (EFL) students prompt generative artificial intelligence (AI) tools +during short story writing. Sixty-seven Hong Kong secondary school students +created generative-AI tools using open-source language models and wrote short +stories with them. The study collected and analyzed the students' generative-AI +tools, short stories, and written reflections on their conditions or purposes +for prompting. The research identified three main themes regarding the purposes +for which students prompt generative-AI tools during short story writing: a +lack of awareness of purposes, overcoming writer's block, and developing, +expanding, and improving the story. The study also identified common +characteristics of students' activity systems, including the sophistication of +their generative-AI tools, the quality of their stories, and their school's +overall academic achievement level, for their prompting of generative-AI tools +for the three purposes during short story writing. The study's findings suggest +that teachers should be aware of students' purposes for prompting generative-AI +tools to provide tailored instructions and scaffolded guidance. The findings +may also help designers provide differentiated instructions for users at +various levels of story development when using a generative-AI tool. +" +Prompting Is All You Need: Automated Android Bug Replay with Large Language Models,Sidong Feng,http://arxiv.org/pdf/2306.01987v2.pdf,2023-06-03,['cs.se'],2306.01987v2.pdf," Bug reports are vital for software maintenance that allow users to inform +developers of the problems encountered while using the software. As such, +researchers have committed considerable resources toward automating bug replay +to expedite the process of software maintenance. Nonetheless, the success of +current automated approaches is largely dictated by the characteristics and +quality of bug reports, as they are constrained by the limitations of +manually-crafted patterns and pre-defined vocabulary lists. Inspired by the +success of Large Language Models (LLMs) in natural language understanding, we +propose AdbGPT, a new lightweight approach to automatically reproduce the bugs +from bug reports through prompt engineering, without any training and +hard-coding effort. AdbGPT leverages few-shot learning and chain-of-thought +reasoning to elicit human knowledge and logical reasoning from LLMs to +accomplish the bug replay in a manner similar to a developer. Our evaluations +demonstrate the effectiveness and efficiency of our AdbGPT to reproduce 81.3% +of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines +and ablation studies. We also conduct a small-scale user study to confirm the +usefulness of AdbGPT in enhancing developers' bug replay capabilities. +" +ChatGPT as a mapping assistant: A novel method to enrich maps with generative AI and content derived from street-level photographs,Levente Juhász,http://arxiv.org/pdf/2306.03204v1.pdf,2023-06-05,"['cs.cy', 'cs.cv']",2306.03204v1.pdf," This paper explores the concept of leveraging generative AI as a mapping +assistant for enhancing the efficiency of collaborative mapping. We present +results of an experiment that combines multiple sources of volunteered +geographic information (VGI) and large language models (LLMs). Three analysts +described the content of crowdsourced Mapillary street-level photographs taken +along roads in a small test area in Miami, Florida. GPT-3.5-turbo was +instructed to suggest the most appropriate tagging for each road in +OpenStreetMap (OSM). The study also explores the utilization of BLIP-2, a +state-of-the-art multimodal pre-training method as an artificial analyst of +street-level photographs in addition to human analysts. Results demonstrate two +ways to effectively increase the accuracy of mapping suggestions without +modifying the underlying AI models: by (1) providing a more detailed +description of source photographs, and (2) combining prompt engineering with +additional context (e.g. location and objects detected along a road). The first +approach increases the suggestion accuracy by up to 29%, and the second one by +up to 20%. +" +An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge,Tan John Chong Min,http://arxiv.org/pdf/2306.03553v1.pdf,2023-06-06,['cs.ai'],2306.03553v1.pdf," We utilise the power of Large Language Models (LLMs), in particular GPT4, to +be prompt engineered into performing an arbitrary task. Here, we give the model +some human priors via text, along with some typical procedures for solving the +ARC tasks, and ask it to generate the i) broad description of the input-output +relation, ii) detailed steps of the input-output mapping, iii) use the detailed +steps to perform manipulation on the test input and derive the test output. The +current GPT3.5/GPT4 prompt solves 2 out of 4 tested small ARC challenges (those +with small grids of 8x8 and below). With tweaks to the prompt to make it more +specific for the use case, it can solve more. We posit that when scaled to a +multi-agent system with usage of past memory and equipped with an image +interpretation tool via Visual Question Answering, we may actually be able to +solve the majority of the ARC challenge +" +Protect Your Prompts: Protocols for IP Protection in LLM Applications,M. A. van Wyk,http://arxiv.org/pdf/2306.06297v1.pdf,2023-06-09,"['cs.cl', 'cs.ai', '91d10, 68t10, 03d40', 'i.2.6; k.6.5; f.3.2']",2306.06297v1.pdf," With the rapid adoption of AI in the form of large language models (LLMs), +the potential value of carefully engineered prompts has become significant. +However, to realize this potential, prompts should be tradable on an open +market. Since prompts are, at present, generally economically non-excludable, +by virtue of their nature as text, no general competitive market has yet been +established. This note discusses two protocols intended to provide protection +of prompts, elevating their status as intellectual property, thus confirming +the intellectual property rights of prompt engineers, and potentially +supporting the flourishing of an open market for LLM prompts. +" +Scalable 3D Captioning with Pretrained Models,Tiange Luo,http://arxiv.org/pdf/2306.07279v2.pdf,2023-06-12,['cs.cv'],2306.07279v2.pdf," We introduce Cap3D, an automatic approach for generating descriptive text for +3D objects. This approach utilizes pretrained models from image captioning, +image-text alignment, and LLM to consolidate captions from multiple views of a +3D asset, completely side-stepping the time-consuming and costly process of +manual annotation. We apply Cap3D to the recently introduced large-scale 3D +dataset, Objaverse, resulting in 660k 3D-text pairs. Our evaluation, conducted +using 41k human annotations from the same dataset, demonstrates that Cap3D +surpasses human-authored descriptions in terms of quality, cost, and speed. +Through effective prompt engineering, Cap3D rivals human performance in +generating geometric descriptions on 17k collected annotations from the ABO +dataset. Finally, we finetune Text-to-3D models on Cap3D and human captions, +and show Cap3D outperforms; and benchmark the SOTA including Point-E, Shape-E, +and DreamFusion. +" +FALL-E: A Foley Sound Synthesis Model and Strategies,Minsung Kang,http://arxiv.org/pdf/2306.09807v2.pdf,2023-06-16,"['eess.as', 'cs.lg', 'cs.sd']",2306.09807v2.pdf," This paper introduces FALL-E, a foley synthesis system and its +training/inference strategies. The FALL-E model employs a cascaded approach +comprising low-resolution spectrogram generation, spectrogram super-resolution, +and a vocoder. We trained every sound-related model from scratch using our +extensive datasets, and utilized a pre-trained language model. We conditioned +the model with dataset-specific texts, enabling it to learn sound quality and +recording environment based on text input. Moreover, we leveraged external +language models to improve text descriptions of our datasets and performed +prompt engineering for quality, coherence, and diversity. FALL-E was evaluated +by an objective measure as well as listening tests in the DCASE 2023 challenge +Task 7. The submission achieved the second place on average, while achieving +the best score for diversity, second place for audio quality, and third place +for class fitness. +" +The Cultivated Practices of Text-to-Image Generation,Jonas Oppenlaender,http://arxiv.org/pdf/2306.11393v1.pdf,2023-06-20,"['cs.cy', 'cs.ai', 'k.4; j.5; i.2.0; k.5.m']",2306.11393v1.pdf," Humankind is entering a novel creative era in which anybody can synthesize +digital information using generative artificial intelligence (AI). +Text-to-image generation, in particular, has become vastly popular and millions +of practitioners produce AI-generated images and AI art online. This chapter +first gives an overview of the key developments that enabled a healthy +co-creative online ecosystem around text-to-image generation to rapidly emerge, +followed by a high-level description of key elements in this ecosystem. A +particular focus is placed on prompt engineering, a creative practice that has +been embraced by the AI art community. It is then argued that the emerging +co-creative ecosystem constitutes an intelligent system on its own - a system +that both supports human creativity, but also potentially entraps future +generations and limits future development efforts in AI. The chapter discusses +the potential risks and dangers of cultivating this co-creative ecosystem, such +as the bias inherent in today's training data, potential quality degradation in +future image generation systems due to synthetic data becoming common place, +and the potential long-term effects of text-to-image generation on people's +imagination, ambitions, and development. +" +Solving and Generating NPR Sunday Puzzles with Large Language Models,Jingmiao Zhao,http://arxiv.org/pdf/2306.12255v1.pdf,2023-06-21,['cs.cl'],2306.12255v1.pdf," We explore the ability of large language models to solve and generate puzzles +from the NPR Sunday Puzzle game show using PUZZLEQA, a dataset comprising 15 +years of on-air puzzles. We evaluate four large language models using PUZZLEQA, +in both multiple choice and free response formats, and explore two prompt +engineering techniques to improve free response performance: chain-of-thought +reasoning and prompt summarization. We find that state-of-the-art large +language models can solve many PUZZLEQA puzzles: the best model, GPT-3.5, +achieves 50.2% loose accuracy. However, in our few-shot puzzle generation +experiment, we find no evidence that models can generate puzzles: GPT-3.5 +generates puzzles with answers that do not conform to the generated rules. +Puzzle generation remains a challenging task for future work. +" +Federated Large Language Model: A Position Paper,Chaochao Chen,http://arxiv.org/pdf/2307.08925v1.pdf,2023-07-18,"['cs.lg', 'cs.ai', 'cs.cl']",2307.08925v1.pdf," Large scale language models (LLM) have received significant attention and +found diverse applications across various domains, but their development +encounters challenges in real-world scenarios. These challenges arise due to +the scarcity of public domain data availability and the need to maintain +privacy with respect to private domain data. To address these issues, federated +learning (FL) has emerged as a promising technology that enables collaborative +training of shared models while preserving decentralized data. We propose the +concept of federated LLM, which comprises three key components, i.e., federated +LLM pre-training, federated LLM fine-tuning, and federated LLM prompt +engineering. For each component, we discuss its advantage over traditional LLM +training methods and propose specific engineering strategies for +implementation. Furthermore, we explore the novel challenges introduced by the +integration of FL and LLM. We analyze existing solutions and identify potential +obstacles faced by these solutions within the context of federated LLM. +" +Chit-Chat or Deep Talk: Prompt Engineering for Process Mining,Urszula Jessen,http://arxiv.org/pdf/2307.09909v1.pdf,2023-07-19,['cs.ai'],2307.09909v1.pdf," This research investigates the application of Large Language Models (LLMs) to +augment conversational agents in process mining, aiming to tackle its inherent +complexity and diverse skill requirements. While LLM advancements present novel +opportunities for conversational process mining, generating efficient outputs +is still a hurdle. We propose an innovative approach that amend many issues in +existing solutions, informed by prior research on Natural Language Processing +(NLP) for conversational agents. Leveraging LLMs, our framework improves both +accessibility and agent performance, as demonstrated by experiments on public +question and data sets. Our research sets the stage for future explorations +into LLMs' role in process mining and concludes with propositions for enhancing +LLM memory, implementing real-time user testing, and examining diverse data +sets. +" +Large Language Models can accomplish Business Process Management Tasks,Michael Grohs,http://arxiv.org/pdf/2307.09923v1.pdf,2023-07-19,['cs.cl'],2307.09923v1.pdf," Business Process Management (BPM) aims to improve organizational activities +and their outcomes by managing the underlying processes. To achieve this, it is +often necessary to consider information from various sources, including +unstructured textual documents. Therefore, researchers have developed several +BPM-specific solutions that extract information from textual documents using +Natural Language Processing techniques. These solutions are specific to their +respective tasks and cannot accomplish multiple process-related problems as a +general-purpose instrument. However, in light of the recent emergence of Large +Language Models (LLMs) with remarkable reasoning capabilities, such a +general-purpose instrument with multiple applications now appears attainable. +In this paper, we illustrate how LLMs can accomplish text-related BPM tasks by +applying a specific LLM to three exemplary tasks: mining imperative process +models from textual descriptions, mining declarative process models from +textual descriptions, and assessing the suitability of process tasks from +textual descriptions for robotic process automation. We show that, without +extensive configuration or prompt engineering, LLMs perform comparably to or +better than existing solutions and discuss implications for future BPM research +as well as practical usage. +" +SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning,Kiana Kheiri,http://arxiv.org/pdf/2307.10234v2.pdf,2023-07-16,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.si']",2307.10234v2.pdf," This study presents a thorough examination of various Generative Pretrained +Transformer (GPT) methodologies in sentiment analysis, specifically in the +context of Task 4 on the SemEval 2017 dataset. Three primary strategies are +employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) +fine-tuning GPT models, and 3) an inventive approach to embedding +classification. The research yields detailed comparative insights among these +strategies and individual GPT models, revealing their unique strengths and +potential limitations. Additionally, the study compares these GPT-based +methodologies with other current, high-performing models previously used with +the same dataset. The results illustrate the significant superiority of the GPT +approaches in terms of predictive performance, more than 22\% in F1-score +compared to the state-of-the-art. Further, the paper sheds light on common +challenges in sentiment analysis tasks, such as understanding context and +detecting sarcasm. It underscores the enhanced capabilities of the GPT models +to effectively handle these complexities. Taken together, these findings +highlight the promising potential of GPT models in sentiment analysis, setting +the stage for future research in this field. The code can be found at +https://github.com/DSAatUSU/SentimentGPT +" +Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain,Yun Tang,http://arxiv.org/pdf/2307.11769v1.pdf,2023-07-17,['cs.cl'],2307.11769v1.pdf," Engineering knowledge-based (or expert) systems require extensive manual +effort and domain knowledge. As Large Language Models (LLMs) are trained using +an enormous amount of cross-domain knowledge, it becomes possible to automate +such engineering processes. This paper presents an empirical automation and +semi-automation framework for domain knowledge distillation using prompt +engineering and the LLM ChatGPT. We assess the framework empirically in the +autonomous driving domain and present our key observations. In our +implementation, we construct the domain knowledge ontology by ""chatting"" with +ChatGPT. The key finding is that while fully automated domain ontology +construction is possible, human supervision and early intervention typically +improve efficiency and output quality as they lessen the effects of response +randomness and the butterfly effect. We, therefore, also develop a web-based +distillation assistant enabling supervision and flexible intervention at +runtime. We hope our findings and tools could inspire future research toward +revolutionizing the engineering of knowledge-based systems across application +domains. +" +Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models,Chee Wei Tan,http://arxiv.org/pdf/2307.14349v1.pdf,2023-07-08,"['cs.se', 'cs.ai']",2307.14349v1.pdf," This paper presents an AI-assisted programming tool called Copilot for Xcode +for program composition and design to support human software developers. By +seamlessly integrating cloud-based Large Language Models (LLM) with Apple's +local development environment, Xcode, this tool enhances productivity and +unleashes creativity for software development in Apple software ecosystem +(e.g., iOS apps, macOS). Leveraging advanced natural language processing (NLP) +techniques, Copilot for Xcode effectively processes source code tokens and +patterns within code repositories, enabling features such as code generation, +autocompletion, documentation, and error detection. Software developers can +also query and make ""small"" decisions for program composition, some of which +can be made simultaneously, and this is facilitated through prompt engineering +in a chat interface of Copilot for Xcode. Finally, we present simple case +studies as evidence of the effectiveness of utilizing NLP in Xcode to prompt +popular LLM services like OpenAI ChatGPT for program composition and design. +" +Backdoor Attacks for In-Context Learning with Language Models,Nikhil Kandpal,http://arxiv.org/pdf/2307.14692v1.pdf,2023-07-27,['cs.cr'],2307.14692v1.pdf," Because state-of-the-art language models are expensive to train, most +practitioners must make use of one of the few publicly available language +models or language model APIs. This consolidation of trust increases the +potency of backdoor attacks, where an adversary tampers with a machine learning +model in order to make it perform some malicious behavior on inputs that +contain a predefined backdoor trigger. We show that the in-context learning +ability of large language models significantly complicates the question of +developing backdoor attacks, as a successful backdoor must work against various +prompting strategies and should not affect the model's general purpose +capabilities. We design a new attack for eliciting targeted misclassification +when language models are prompted to perform a particular target task and +demonstrate the feasibility of this attack by backdooring multiple large +language models ranging in size from 1.3 billion to 6 billion parameters. +Finally we study defenses to mitigate the potential harms of our attack: for +example, while in the white-box setting we show that fine-tuning models for as +few as 500 steps suffices to remove the backdoor behavior, in the black-box +setting we are unable to develop a successful defense that relies on prompt +engineering alone. +" +Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models,Keyu Pan,http://arxiv.org/pdf/2307.16180v1.pdf,2023-07-30,['cs.cl'],2307.16180v1.pdf," The field of large language models (LLMs) has made significant progress, and +their knowledge storage capacity is approaching that of human beings. +Furthermore, advanced techniques, such as prompt learning and reinforcement +learning, are being employed to address ethical concerns and hallucination +problems associated with LLMs, bringing them closer to aligning with human +values. This situation naturally raises the question of whether LLMs with +human-like abilities possess a human-like personality? In this paper, we aim to +investigate the feasibility of using the Myers-Briggs Type Indicator (MBTI), a +widespread human personality assessment tool, as an evaluation metric for LLMs. +Specifically, extensive experiments will be conducted to explore: 1) the +personality types of different LLMs, 2) the possibility of changing the +personality types by prompt engineering, and 3) How does the training dataset +affect the model's personality. Although the MBTI is not a rigorous assessment, +it can still reflect the similarity between LLMs and human personality. In +practice, the MBTI has the potential to serve as a rough indicator. Our codes +are available at +https://github.com/HarderThenHarder/transformers_tasks/tree/main/LLM/llms_mbti. +" +Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment,Saizhuo Wang,http://arxiv.org/pdf/2308.00016v1.pdf,2023-07-31,"['q-fin.cp', 'cs.ai', 'cs.cl']",2308.00016v1.pdf," One of the most important tasks in quantitative investment research is mining +new alphas (effective trading signals or factors). Traditional alpha mining +methods, either hand-crafted factor synthesizing or algorithmic factor mining +(e.g., search with genetic programming), have inherent limitations, especially +in implementing the ideas of quants. In this work, we propose a new alpha +mining paradigm by introducing human-AI interaction, and a novel prompt +engineering algorithmic framework to implement this paradigm by leveraging the +power of large language models. Moreover, we develop Alpha-GPT, a new +interactive alpha mining system framework that provides a heuristic way to +``understand'' the ideas of quant researchers and outputs creative, insightful, +and effective alphas. We demonstrate the effectiveness and advantage of +Alpha-GPT via a number of alpha mining experiments. +" +Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizability,Masaru Yamada,http://arxiv.org/pdf/2308.01391v1.pdf,2023-08-02,['cs.cl'],2308.01391v1.pdf," This paper explores the influence of integrating the purpose of the +translation and the target audience into prompts on the quality of translations +produced by ChatGPT. Drawing on previous translation studies, industry +practices, and ISO standards, the research underscores the significance of the +pre-production phase in the translation process. The study reveals that the +inclusion of suitable prompts in large-scale language models like ChatGPT can +yield flexible translations, a feat yet to be realized by conventional Machine +Translation (MT). The research scrutinizes the changes in translation quality +when prompts are used to generate translations that meet specific conditions. +The evaluation is conducted from a practicing translator's viewpoint, both +subjectively and qualitatively, supplemented by the use of OpenAI's word +embedding API for cosine similarity calculations. The findings suggest that the +integration of the purpose and target audience into prompts can indeed modify +the generated translations, generally enhancing the translation quality by +industry standards. The study also demonstrates the practical application of +the ""good translation"" concept, particularly in the context of marketing +documents and culturally dependent idioms. +" +InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent,Po-Lin Chen,http://arxiv.org/pdf/2308.01552v1.pdf,2023-08-03,"['cs.ai', 'cs.cl', 'cs.lg']",2308.01552v1.pdf," This research paper delves into the integration of OpenAI's ChatGPT into +embodied agent systems, evaluating its influence on interactive decision-making +benchmark. Drawing a parallel to the concept of people assuming roles according +to their unique strengths, we introduce InterAct. In this approach, we feed +ChatGPT with varied prompts, assigning it a numerous roles like a checker and a +sorter, then integrating them with the original language model. Our research +shows a remarkable success rate of 98% in AlfWorld, which consists of 6 +different tasks in a simulated household environment, emphasizing the +significance of proficient prompt engineering. The results highlight ChatGPT's +competence in comprehending and performing intricate tasks effectively in +real-world settings, thus paving the way for further advancements in task +planning. +" +RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model,Yao Lu,http://arxiv.org/pdf/2308.05345v2.pdf,2023-08-10,"['cs.lg', 'cs.ar']",2308.05345v2.pdf," Inspired by the recent success of large language models (LLMs) like ChatGPT, +researchers start to explore the adoption of LLMs for agile hardware design, +such as generating design RTL based on natural-language instructions. However, +in existing works, their target designs are all relatively simple and in a +small scale, and proposed by the authors themselves, making a fair comparison +among different LLM solutions challenging. In addition, many prior works only +focus on the design correctness, without evaluating the design qualities of +generated design RTL. In this work, we propose an open-source benchmark named +RTLLM, for generating design RTL with natural language instructions. To +systematically evaluate the auto-generated design RTL, we summarized three +progressive goals, named syntax goal, functionality goal, and design quality +goal. This benchmark can automatically provide a quantitative evaluation of any +given LLM-based solution. Furthermore, we propose an easy-to-use yet +surprisingly effective prompt engineering technique named self-planning, which +proves to significantly boost the performance of GPT-3.5 in our proposed +benchmark. +" +"LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked",Mansi Phute,http://arxiv.org/pdf/2308.07308v3.pdf,2023-08-14,"['cs.cl', 'cs.ai']",2308.07308v3.pdf," Large language models (LLMs) are popular for high-quality text generation but +can produce harmful content, even when aligned with human values through +reinforcement learning. Adversarial prompts can bypass their safety measures. +We propose LLM Self Defense, a simple approach to defend against these attacks +by having an LLM screen the induced responses. Our method does not require any +fine-tuning, input preprocessing, or iterative output generation. Instead, we +incorporate the generated content into a pre-defined prompt and employ another +instance of an LLM to analyze the text and predict whether it is harmful. We +test LLM Self Defense on GPT 3.5 and Llama 2, two of the current most prominent +LLMs against various types of attacks, such as forcefully inducing affirmative +responses to prompts and prompt engineering attacks. Notably, LLM Self Defense +succeeds in reducing the attack success rate to virtually 0 using both GPT 3.5 +and Llama 2. +" +Data Race Detection Using Large Language Models,Le Chen,http://arxiv.org/pdf/2308.07505v2.pdf,2023-08-15,"['cs.lg', 'cs.cl']",2308.07505v2.pdf," Large language models (LLMs) are demonstrating significant promise as an +alternate strategy to facilitate analyses and optimizations of high-performance +computing programs, circumventing the need for resource-intensive manual tool +creation. In this paper, we explore a novel LLM-based data race detection +approach combining prompting engineering and fine-tuning techniques. We create +a dedicated dataset named DRB-ML, which is derived from DataRaceBench, with +fine-grain labels showing the presence of data race pairs and their associated +variables, line numbers, and read/write information. DRB-ML is then used to +evaluate representative LLMs and fine-tune open-source ones. Our experiment +shows that LLMs can be a viable approach to data race detection. However, they +still cannot compete with traditional data race detection tools when we need +detailed information about variable pairs causing data races. +" +Accelerated materials language processing enabled by GPT,Jaewoong Choi,http://arxiv.org/pdf/2308.09354v1.pdf,2023-08-18,"['cs.cl', 'cond-mat.mtrl-sci']",2308.09354v1.pdf," Materials language processing (MLP) is one of the key facilitators of +materials science research, as it enables the extraction of structured +information from massive materials science literature. Prior works suggested +high-performance MLP models for text classification, named entity recognition +(NER), and extractive question answering (QA), which require complex model +architecture, exhaustive fine-tuning and a large number of human-labelled +datasets. In this study, we develop generative pretrained transformer +(GPT)-enabled pipelines where the complex architectures of prior MLP models are +replaced with strategic designs of prompt engineering. First, we develop a +GPT-enabled document classification method for screening relevant documents, +achieving comparable accuracy and reliability compared to prior models, with +only small dataset. Secondly, for NER task, we design an entity-centric +prompts, and learning few-shot of them improved the performance on most of +entities in three open datasets. Finally, we develop an GPT-enabled extractive +QA model, which provides improved performance and shows the possibility of +automatically correcting annotations. While our findings confirm the potential +of GPT-enabled MLP models as well as their value in terms of reliability and +practicability, our scientific methods and systematic approach are applicable +to any materials science domain to accelerate the information extraction of +scientific literature. +" +Data-to-text Generation for Severely Under-Resourced Languages with GPT-3.5: A Bit of Help Needed from Google Translate,Michela Lorandi,http://arxiv.org/pdf/2308.09957v1.pdf,2023-08-19,"['cs.cl', 'cs.ai']",2308.09957v1.pdf," LLMs like GPT are great at tasks involving English which dominates in their +training data. In this paper, we look at how they cope with tasks involving +languages that are severely under-represented in their training data, in the +context of data-to-text generation for Irish, Maltese, Welsh and Breton. During +the prompt-engineering phase we tested a range of prompt types and formats on +GPT-3.5 and~4 with a small sample of example input/output pairs. We then fully +evaluated the two most promising prompts in two scenarios: (i) direct +generation into the under-resourced language, and (ii) generation into English +followed by translation into the under-resourced language. We find that +few-shot prompting works better for direct generation into under-resourced +languages, but that the difference disappears when pivoting via English. The +few-shot + translation system variants were submitted to the WebNLG 2023 shared +task where they outperformed competitor systems by substantial margins in all +languages on all metrics. We conclude that good performance on under-resourced +languages can be achieved out-of-the box with state-of-the-art LLMs. However, +our best results (for Welsh) remain well below the lowest ranked English system +at WebNLG'20. +" +Activation Addition: Steering Language Models Without Optimization,Alexander Matt Turner,http://arxiv.org/pdf/2308.10248v2.pdf,2023-08-20,"['cs.cl', 'cs.lg']",2308.10248v2.pdf," Reliably controlling the behavior of large language models is a pressing open +problem. Existing methods include supervised finetuning, reinforcement learning +from human feedback, prompt engineering, and guided decoding. We instead +investigate activation engineering: modifying activations at inference time to +predictably alter model behavior. In particular, we bias the forward pass with +an added 'steering vector' implicitly specified through natural language. + Unlike past work which learned these steering vectors, our Activation +Addition (ActAdd) method computes them by taking the activation differences +that result from pairs of prompts. We demonstrate ActAdd on GPT-2 on +OpenWebText and ConceptNet. Our inference-time approach yields control over +high-level properties of output and preserves off-target model performance. It +involves far less compute and implementation effort than finetuning, allows +users to provide natural language specifications, and its overhead scales +naturally with model size. +" +Situated Natural Language Explanations,Zining Zhu,http://arxiv.org/pdf/2308.14115v1.pdf,2023-08-27,['cs.cl'],2308.14115v1.pdf," Natural language is among the most accessible tools for explaining decisions +to humans, and large pretrained language models (PLMs) have demonstrated +impressive abilities to generate coherent natural language explanations (NLE). +The existing NLE research perspectives do not take the audience into account. +An NLE can have high textual quality, but it might not accommodate audiences' +needs and preference. To address this limitation, we propose an alternative +perspective, situated NLE, including a situated generation framework and a +situated evaluation framework. On the generation side, we propose simple prompt +engineering methods that adapt the NLEs to situations. In human studies, the +annotators preferred the situated NLEs. On the evaluation side, we set up +automated evaluation scores in lexical, semantic, and pragmatic categories. The +scores can be used to select the most suitable prompts to generate NLEs. +Situated NLE provides a perspective to conduct further research on automatic +NLE generations. +" +"FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions",Neeraj Cherakara,http://arxiv.org/pdf/2308.15214v2.pdf,2023-08-29,"['cs.cl', 'cs.ai', 'cs.hc', 'cs.ro']",2308.15214v2.pdf," We demonstrate an embodied conversational agent that can function as a +receptionist and generate a mixture of open and closed-domain dialogue along +with facial expressions, by using a large language model (LLM) to develop an +engaging conversation. We deployed the system onto a Furhat robot, which is +highly expressive and capable of using both verbal and nonverbal cues during +interaction. The system was designed specifically for the National Robotarium +to interact with visitors through natural conversations, providing them with +information about the facilities, research, news, upcoming events, etc. The +system utilises the state-of-the-art GPT-3.5 model to generate such information +along with domain-general conversations and facial expressions based on prompt +engineering. +" +Can Prompt Learning Benefit Radiology Report Generation?,Jun Wang,http://arxiv.org/pdf/2308.16269v1.pdf,2023-08-30,['cs.cv'],2308.16269v1.pdf," Radiology report generation aims to automatically provide clinically +meaningful descriptions of radiology images such as MRI and X-ray. Although +great success has been achieved in natural scene image captioning tasks, +radiology report generation remains challenging and requires prior medical +knowledge. In this paper, we propose PromptRRG, a method that utilizes prompt +learning to activate a pretrained model and incorporate prior knowledge. Since +prompt learning for radiology report generation has not been explored before, +we begin with investigating prompt designs and categorise them based on varying +levels of knowledge: common, domain-specific and disease-enriched prompts. +Additionally, we propose an automatic prompt learning mechanism to alleviate +the burden of manual prompt engineering. This is the first work to +systematically examine the effectiveness of prompt learning for radiology +report generation. Experimental results on the largest radiology report +generation benchmark, MIMIC-CXR, demonstrate that our proposed method achieves +state-of-the-art performance. Code will be available upon the acceptance. +" +Large Language Models as Data Preprocessors,Haochen Zhang,http://arxiv.org/pdf/2308.16361v1.pdf,2023-08-30,"['cs.ai', 'cs.db']",2308.16361v1.pdf," Large Language Models (LLMs), typified by OpenAI's GPT series and Meta's +LLaMA variants, have marked a significant advancement in artificial +intelligence. Trained on vast amounts of text data, LLMs are capable of +understanding and generating human-like text across a diverse range of topics. +This study expands on the applications of LLMs, exploring their potential in +data preprocessing, a critical stage in data mining and analytics applications. +We delve into the applicability of state-of-the-art LLMs such as GPT-3.5, +GPT-4, and Vicuna-13B for error detection, data imputation, schema matching, +and entity matching tasks. Alongside showcasing the inherent capabilities of +LLMs, we highlight their limitations, particularly in terms of computational +expense and inefficiency. We propose an LLM-based framework for data +preprocessing, which integrates cutting-edge prompt engineering techniques, +coupled with traditional methods like contextualization and feature selection, +to improve the performance and efficiency of these models. The effectiveness of +LLMs in data preprocessing is evaluated through an experimental study spanning +12 datasets. GPT-4 emerged as a standout, achieving 100\% accuracy or F1 score +on 4 datasets, suggesting LLMs' immense potential in these tasks. Despite +certain limitations, our study underscores the promise of LLMs in this domain +and anticipates future developments to overcome current hurdles. +" +Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering,Lars-Peter Meyer,http://arxiv.org/pdf/2308.16622v1.pdf,2023-08-31,"['cs.ai', 'cs.cl', 'cs.db']",2308.16622v1.pdf," As the field of Large Language Models (LLMs) evolves at an accelerated pace, +the critical need to assess and monitor their performance emerges. We introduce +a benchmarking framework focused on knowledge graph engineering (KGE) +accompanied by three challenges addressing syntax and error correction, facts +extraction and dataset generation. We show that while being a useful tool, LLMs +are yet unfit to assist in knowledge graph generation with zero-shot prompting. +Consequently, our LLM-KG-Bench framework provides automatic evaluation and +storage of LLM responses as well as statistical data and visualization tools to +support tracking of prompt engineering and model performance. +" +Linking microblogging sentiments to stock price movement: An application of GPT-4,Rick Steinert,http://arxiv.org/pdf/2308.16771v1.pdf,2023-08-31,"['q-fin.st', 'q-fin.cp']",2308.16771v1.pdf," This paper investigates the potential improvement of the GPT-4 Language +Learning Model (LLM) in comparison to BERT for modeling same-day daily stock +price movements of Apple and Tesla in 2017, based on sentiment analysis of +microblogging messages. We recorded daily adjusted closing prices and +translated them into up-down movements. Sentiment for each day was extracted +from messages on the Stocktwits platform using both LLMs. We develop a novel +method to engineer a comprehensive prompt for contextual sentiment analysis +which unlocks the true capabilities of modern LLM. This enables us to carefully +retrieve sentiments, perceived advantages or disadvantages, and the relevance +towards the analyzed company. Logistic regression is used to evaluate whether +the extracted message contents reflect stock price movements. As a result, +GPT-4 exhibited substantial accuracy, outperforming BERT in five out of six +months and substantially exceeding a naive buy-and-hold strategy, reaching a +peak accuracy of 71.47 % in May. The study also highlights the importance of +prompt engineering in obtaining desired outputs from GPT-4's contextual +abilities. However, the costs of deploying GPT-4 and the need for fine-tuning +prompts highlight some practical considerations for its use. +" +LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models,Cheng Shi,http://arxiv.org/pdf/2309.01155v2.pdf,2023-09-03,['cs.cv'],2309.01155v2.pdf," Prompt engineering is a powerful tool used to enhance the performance of +pre-trained models on downstream tasks. For example, providing the prompt +""Let's think step by step"" improved GPT-3's reasoning accuracy to 63% on +MutiArith while prompting ""a photo of"" filled with a class name enables CLIP to +achieve $80$\% zero-shot accuracy on ImageNet. While previous research has +explored prompt learning for the visual modality, analyzing what constitutes a +good visual prompt specifically for image recognition is limited. In addition, +existing visual prompt tuning methods' generalization ability is worse than +text-only prompting tuning. This paper explores our key insight: synthetic text +images are good visual prompts for vision-language models! To achieve that, we +propose our LoGoPrompt, which reformulates the classification objective to the +visual prompt selection and addresses the chicken-and-egg challenge of first +adding synthetic text images as class-wise visual prompts or predicting the +class first. Without any trainable visual prompt parameters, experimental +results on 16 datasets demonstrate that our method consistently outperforms +state-of-the-art methods in few-shot learning, base-to-new generalization, and +domain generalization. +" +FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning,Xinyi Wang,http://arxiv.org/pdf/2309.04663v2.pdf,2023-09-09,"['cs.cl', 'cs.ai']",2309.04663v2.pdf," Learning paradigms for large language models (LLMs) currently tend to fall +within either in-context learning (ICL) or full fine-tuning. Each of these +comes with their own trade-offs based on available data, model size, compute +cost, ease-of-use, and final quality with neither solution performing well +across-the-board. In this article, we first describe ICL and fine-tuning +paradigms in a way that highlights their natural connections. Based on these +connections, we propose a new learning paradigm called FIAT that fuses the best +of these paradigms together, enabling prompt-engineered instructions and +chain-of-thought reasoning with the very largest models while also using +similar methods to perform parameter updates on a modestly-sized LLM with +parameter-efficient tuning. We evaluate FIAT's effectiveness on a variety of +multilingual tasks and observe that FIAT performs better than both ICL and +fine-tuning at scales ranging from 100-10,000 training examples. We hope that +FIAT provides a practical way of harnessing the full potential of LLMs without +needing to make a hard choice between learning paradigms. +" +Toward Reproducing Network Research Results Using Large Language Models,Qiao Xiang,http://arxiv.org/pdf/2309.04716v1.pdf,2023-09-09,"['cs.lg', 'cs.ai', 'cs.cl']",2309.04716v1.pdf," Reproducing research results in the networking community is important for +both academia and industry. The current best practice typically resorts to +three approaches: (1) looking for publicly available prototypes; (2) contacting +the authors to get a private prototype; and (3) manually implementing a +prototype following the description of the publication. However, most published +network research does not have public prototypes and private prototypes are +hard to get. As such, most reproducing efforts are spent on manual +implementation based on the publications, which is both time and labor +consuming and error-prone. In this paper, we boldly propose reproducing network +research results using the emerging large language models (LLMs). In +particular, we first prove its feasibility with a small-scale experiment, in +which four students with essential networking knowledge each reproduces a +different networking system published in prominent conferences and journals by +prompt engineering ChatGPT. We report the experiment's observations and lessons +and discuss future open research questions of this proposal. This work raises +no ethical issue. +" +Detecting Natural Language Biases with Prompt-based Learning,Md Abdul Aowal,http://arxiv.org/pdf/2309.05227v1.pdf,2023-09-11,"['cs.cl', 'cs.ai']",2309.05227v1.pdf," In this project, we want to explore the newly emerging field of prompt +engineering and apply it to the downstream task of detecting LM biases. More +concretely, we explore how to design prompts that can indicate 4 different +types of biases: (1) gender, (2) race, (3) sexual orientation, and (4) +religion-based. Within our project, we experiment with different manually +crafted prompts that can draw out the subtle biases that may be present in the +language model. We apply these prompts to multiple variations of popular and +well-recognized models: BERT, RoBERTa, and T5 to evaluate their biases. We +provide a comparative analysis of these models and assess them using a two-fold +method: use human judgment to decide whether model predictions are biased and +utilize model-level judgment (through further prompts) to understand if a model +can self-diagnose the biases of its own prediction. +" +Two Timin': Repairing Smart Contracts With A Two-Layered Approach,Abhinav Jain,http://arxiv.org/pdf/2309.07841v1.pdf,2023-09-14,"['cs.cr', 'cs.ai']",2309.07841v1.pdf," Due to the modern relevance of blockchain technology, smart contracts present +both substantial risks and benefits. Vulnerabilities within them can trigger a +cascade of consequences, resulting in significant losses. Many current papers +primarily focus on classifying smart contracts for malicious intent, often +relying on limited contract characteristics, such as bytecode or opcode. This +paper proposes a novel, two-layered framework: 1) classifying and 2) directly +repairing malicious contracts. Slither's vulnerability report is combined with +source code and passed through a pre-trained RandomForestClassifier (RFC) and +Large Language Models (LLMs), classifying and repairing each suggested +vulnerability. Experiments demonstrate the effectiveness of fine-tuned and +prompt-engineered LLMs. The smart contract repair models, built from +pre-trained GPT-3.5-Turbo and fine-tuned Llama-2-7B models, reduced the overall +vulnerability count by 97.5% and 96.7% respectively. A manual inspection of +repaired contracts shows that all retain functionality, indicating that the +proposed method is appropriate for automatic batch classification and repair of +vulnerabilities in smart contracts. +" +Large Language Models for Failure Mode Classification: An Investigation,Michael Stewart,http://arxiv.org/pdf/2309.08181v1.pdf,2023-09-15,['cs.cl'],2309.08181v1.pdf," In this paper we present the first investigation into the effectiveness of +Large Language Models (LLMs) for Failure Mode Classification (FMC). FMC, the +task of automatically labelling an observation with a corresponding failure +mode code, is a critical task in the maintenance domain as it reduces the need +for reliability engineers to spend their time manually analysing work orders. +We detail our approach to prompt engineering to enable an LLM to predict the +failure mode of a given observation using a restricted code list. We +demonstrate that the performance of a GPT-3.5 model (F1=0.80) fine-tuned on +annotated data is a significant improvement over a currently available text +classification model (F1=0.60) trained on the same annotated data set. The +fine-tuned model also outperforms the out-of-the box GPT-3.5 (F1=0.46). This +investigation reinforces the need for high quality fine-tuning data sets for +domain-specific tasks using LLMs. +" +Safurai 001: New Qualitative Approach for Code LLM Evaluation,Davide Cifarelli,http://arxiv.org/pdf/2309.11385v1.pdf,2023-09-20,['cs.cl'],2309.11385v1.pdf," This paper presents Safurai-001, a new Large Language Model (LLM) with +significant potential in the domain of coding assistance. Driven by recent +advancements in coding LLMs, Safurai-001 competes in performance with the +latest models like WizardCoder [Xu et al., 2023], PanguCoder [Shen et al., +2023] and Phi-1 [Gunasekar et al., 2023] but aims to deliver a more +conversational interaction. By capitalizing on the progress in data engineering +(including latest techniques of data transformation and prompt engineering) and +instruction tuning, this new model promises to stand toe-to-toe with recent +closed and open source developments. Recognizing the need for an efficacious +evaluation metric for coding LLMs, this paper also introduces GPT4-based +MultiParameters, an evaluation benchmark that harnesses varied parameters to +present a comprehensive insight into the models functioning and performance. +Our assessment shows that Safurai-001 can outperform GPT-3.5 by 1.58% and +WizardCoder by 18.78% in the Code Readability parameter and more. +" +A Practical Survey on Zero-shot Prompt Design for In-context Learning,Yinheng Li,http://arxiv.org/pdf/2309.13205v1.pdf,2023-09-22,"['cs.cl', 'cs.ai', 'cs.et', 'cs.lg']",2309.13205v1.pdf," The remarkable advancements in large language models (LLMs) have brought +about significant improvements in Natural Language Processing(NLP) tasks. This +paper presents a comprehensive review of in-context learning techniques, +focusing on different types of prompts, including discrete, continuous, +few-shot, and zero-shot, and their impact on LLM performance. We explore +various approaches to prompt design, such as manual design, optimization +algorithms, and evaluation methods, to optimize LLM performance across diverse +tasks. Our review covers key research studies in prompt engineering, discussing +their methodologies and contributions to the field. We also delve into the +challenges faced in evaluating prompt performance, given the absence of a +single ""best"" prompt and the importance of considering multiple metrics. In +conclusion, the paper highlights the critical role of prompt design in +harnessing the full potential of LLMs and provides insights into the +combination of manual design, optimization techniques, and rigorous evaluation +for more effective and efficient use of LLMs in various NLP tasks. +" +A Chat About Boring Problems: Studying GPT-based text normalization,Yang Zhang,http://arxiv.org/pdf/2309.13426v1.pdf,2023-09-23,"['cs.cl', 'cs.ai']",2309.13426v1.pdf," Text normalization - the conversion of text from written to spoken form - is +traditionally assumed to be an ill-formed task for language models. In this +work, we argue otherwise. We empirically show the capacity of Large-Language +Models (LLM) for text normalization in few-shot scenarios. Combining +self-consistency reasoning with linguistic-informed prompt engineering, we find +LLM based text normalization to achieve error rates around 40\% lower than top +normalization systems. Further, upon error analysis, we note key limitations in +the conventional design of text normalization tasks. We create a new taxonomy +of text normalization errors and apply it to results from GPT-3.5-Turbo and +GPT-4.0. Through this new framework, we can identify strengths and weaknesses +of GPT-based TN, opening opportunities for future work. +" +DynaCon: Dynamic Robot Planner with Contextual Awareness via LLMs,Gyeongmin Kim,http://arxiv.org/pdf/2309.16031v1.pdf,2023-09-27,['cs.ro'],2309.16031v1.pdf," Mobile robots often rely on pre-existing maps for effective path planning and +navigation. However, when these maps are unavailable, particularly in +unfamiliar environments, a different approach become essential. This paper +introduces DynaCon, a novel system designed to provide mobile robots with +contextual awareness and dynamic adaptability during navigation, eliminating +the reliance of traditional maps. DynaCon integrates real-time feedback with an +object server, prompt engineering, and navigation modules. By harnessing the +capabilities of Large Language Models (LLMs), DynaCon not only understands +patterns within given numeric series but also excels at categorizing objects +into matched spaces. This facilitates dynamic path planner imbued with +contextual awareness. We validated the effectiveness of DynaCon through an +experiment where a robot successfully navigated to its goal using reasoning. +Source code and experiment videos for this work can be found at: +https://sites.google.com/view/dynacon. +" +Cyber Sentinel: Exploring Conversational Agents in Streamlining Security Tasks with GPT-4,Mehrdad Kaheh,http://arxiv.org/pdf/2309.16422v1.pdf,2023-09-28,['cs.cr'],2309.16422v1.pdf," In an era where cyberspace is both a battleground and a backbone of modern +society, the urgency of safeguarding digital assets against ever-evolving +threats is paramount. This paper introduces Cyber Sentinel, an innovative +task-oriented cybersecurity dialogue system that is effectively capable of +managing two core functions: explaining potential cyber threats within an +organization to the user, and taking proactive/reactive security actions when +instructed by the user. Cyber Sentinel embodies the fusion of artificial +intelligence, cybersecurity domain expertise, and real-time data analysis to +combat the multifaceted challenges posed by cyber adversaries. This article +delves into the process of creating such a system and how it can interact with +other components typically found in cybersecurity organizations. Our work is a +novel approach to task-oriented dialogue systems, leveraging the power of +chaining GPT-4 models combined with prompt engineering across all sub-tasks. We +also highlight its pivotal role in enhancing cybersecurity communication and +interaction, concluding that not only does this framework enhance the system's +transparency (Explainable AI) but also streamlines the decision-making process +and responding to threats (Actionable AI), therefore marking a significant +advancement in the realm of cybersecurity communication. +" +"A Sign Language Recognition System with Pepper, Lightweight-Transformer, and LLM",JongYoon Lim,http://arxiv.org/pdf/2309.16898v1.pdf,2023-09-28,"['cs.ro', 'cs.cl', 'cs.cv', 'cs.hc']",2309.16898v1.pdf," This research explores using lightweight deep neural network architectures to +enable the humanoid robot Pepper to understand American Sign Language (ASL) and +facilitate non-verbal human-robot interaction. First, we introduce a +lightweight and efficient model for ASL understanding optimized for embedded +systems, ensuring rapid sign recognition while conserving computational +resources. Building upon this, we employ large language models (LLMs) for +intelligent robot interactions. Through intricate prompt engineering, we tailor +interactions to allow the Pepper Robot to generate natural Co-Speech Gesture +responses, laying the foundation for more organic and intuitive humanoid-robot +dialogues. Finally, we present an integrated software pipeline, embodying +advancements in a socially aware AI interaction model. Leveraging the Pepper +Robot's capabilities, we demonstrate the practicality and effectiveness of our +approach in real-world scenarios. The results highlight a profound potential +for enhancing human-robot interaction through non-verbal interactions, bridging +communication gaps, and making technology more accessible and understandable. +" +SPELL: Semantic Prompt Evolution based on a LLM,Yujian Betterest Li,http://arxiv.org/pdf/2310.01260v1.pdf,2023-10-02,"['cs.cl', 'cs.ai']",2310.01260v1.pdf," Prompt engineering is a new paradigm for enhancing the performance of trained +neural network models. For optimizing text-style prompts, existing methods +usually individually operate small portions of a text step by step, which +either breaks the fluency or could not globally adjust a prompt. Since large +language models (LLMs) have powerful ability of generating coherent texts token +by token, can we utilize LLMs for improving prompts? Based on this motivation, +in this paper, considering a trained LLM as a text generator, we attempt to +design a black-box evolution algorithm for automatically optimizing texts, +namely SPELL (Semantic Prompt Evolution based on a LLM). The proposed method is +evaluated with different LLMs and evolution parameters in different text tasks. +Experimental results show that SPELL could rapidly improve the prompts indeed. +We further explore the evolution process and discuss on the limitations, +potential possibilities and future work. +" +Co-audit: tools to help humans double-check AI-generated content,Andrew D. Gordon,http://arxiv.org/pdf/2310.01297v1.pdf,2023-10-02,"['cs.hc', 'cs.ai', 'cs.cl', 'cs.pl']",2310.01297v1.pdf," Users are increasingly being warned to check AI-generated content for +correctness. Still, as LLMs (and other generative models) generate more complex +output, such as summaries, tables, or code, it becomes harder for the user to +audit or evaluate the output for quality or correctness. Hence, we are seeing +the emergence of tool-assisted experiences to help the user double-check a +piece of AI-generated content. We refer to these as co-audit tools. Co-audit +tools complement prompt engineering techniques: one helps the user construct +the input prompt, while the other helps them check the output response. As a +specific example, this paper describes recent research on co-audit tools for +spreadsheet computations powered by generative models. We explain why co-audit +experiences are essential for any application of generative AI where quality is +important and errors are consequential (as is common in spreadsheet +computations). We propose a preliminary list of principles for co-audit, and +outline research challenges. +" +Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations,Deren Lei,http://arxiv.org/pdf/2310.03951v2.pdf,2023-10-06,"['cs.cl', 'cs.ai']",2310.03951v2.pdf," Large language models (LLMs) can generate fluent natural language texts when +given relevant documents as background context. This ability has attracted +considerable interest in developing industry applications of LLMs. However, +LLMs are prone to generate hallucinations that are not supported by the +provided sources. In this paper, we propose a hierarchical framework to detect +and mitigate such ungrounded hallucination. Our framework uses Chain of Natural +Language Inference (CoNLI) for hallucination detection and hallucination +reduction via post-editing. Our approach achieves state-of-the-art performance +on hallucination detection and enhances text quality through rewrite, using +LLMs without any fine-tuning or domain-specific prompt engineering. We show +that this simple plug-and-play framework can serve as an effective choice for +hallucination detection and reduction, achieving competitive performance across +various contexts. +" +LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation,Christian Munley,http://arxiv.org/pdf/2310.04963v2.pdf,2023-10-08,['cs.ai'],2310.04963v2.pdf," Large language models (LLMs) are a new and powerful tool for a wide span of +applications involving natural language and demonstrate impressive code +generation abilities. In this paper, we explore the capabilitity of +state-of-the-art LLMs, including closed-source options like OpenAI GPT-4 and +open-source alternatives like Meta AI Codellama, to automatically generate +tests and use these tests to validate and verify compiler implementations of a +directive-based programming paradigm, OpenACC. Our approach entails exploring +various prompt engineering techniques including a code template, +retrieval-augmented generation (RAG) with code template, expressive prompt +using RAG with code template, one-shot example, and RAG with one-shot example. +This paper focuses on (a) exploring the capabilities of the latest LLMs for +code generation, (b) investigating prompt and fine tuning methods, and (c) +analyzing the outcome of LLMs generated tests +" +Large Language Models for Propaganda Detection,Kilian Sprenkamp,http://arxiv.org/pdf/2310.06422v1.pdf,2023-10-10,"['cs.cl', 'cs.ai']",2310.06422v1.pdf," The prevalence of propaganda in our digital society poses a challenge to +societal harmony and the dissemination of truth. Detecting propaganda through +NLP in text is challenging due to subtle manipulation techniques and contextual +dependencies. To address this issue, we investigate the effectiveness of modern +Large Language Models (LLMs) such as GPT-3 and GPT-4 for propaganda detection. +We conduct experiments using the SemEval-2020 task 11 dataset, which features +news articles labeled with 14 propaganda techniques as a multi-label +classification problem. Five variations of GPT-3 and GPT-4 are employed, +incorporating various prompt engineering and fine-tuning strategies across the +different models. We evaluate the models' performance by assessing metrics such +as $F1$ score, $Precision$, and $Recall$, comparing the results with the +current state-of-the-art approach using RoBERTa. Our findings demonstrate that +GPT-4 achieves comparable results to the current state-of-the-art. Further, +this study analyzes the potential and challenges of LLMs in complex tasks like +propaganda detection. +" +Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming,Juo-Tung Chen,http://arxiv.org/pdf/2310.06646v1.pdf,2023-10-10,['cs.ro'],2310.06646v1.pdf," Large language models offer new ways of empowering people to program robot +applications-namely, code generation via prompting. However, the code generated +by LLMs is susceptible to errors. This work reports a preliminary exploration +that empirically characterizes common errors produced by LLMs in robot +programming. We categorize these errors into two phases: interpretation and +execution. In this work, we focus on errors in execution and observe that they +are caused by LLMs being ""forgetful"" of key information provided in user +prompts. Based on this observation, we propose prompt engineering tactics +designed to reduce errors in execution. We then demonstrate the effectiveness +of these tactics with three language models: ChatGPT, Bard, and LLaMA-2. +Finally, we discuss lessons learned from using LLMs in robot programming and +call for the benchmarking of LLM-powered end-user development of robot +applications. +" +LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing,Stephen Moskal,http://arxiv.org/pdf/2310.06936v1.pdf,2023-10-10,"['cs.cr', 'cs.lg']",2310.06936v1.pdf," In this paper, we explore the potential of Large Language Models (LLMs) to +reason about threats, generate information about tools, and automate cyber +campaigns. We begin with a manual exploration of LLMs in supporting specific +threat-related actions and decisions. We proceed by automating the decision +process in a cyber campaign. We present prompt engineering approaches for a +plan-act-report loop for one action of a threat campaign and and a prompt +chaining design that directs the sequential decision process of a multi-action +campaign. We assess the extent of LLM's cyber-specific knowledge w.r.t the +short campaign we demonstrate and provide insights into prompt design for +eliciting actionable responses. We discuss the potential impact of LLMs on the +threat landscape and the ethical considerations of using LLMs for accelerating +threat actor capabilities. We report a promising, yet concerning, application +of generative AI to cyber threats. However, the LLM's capabilities to deal with +more complex networks, sophisticated vulnerabilities, and the sensitivity of +prompts are open questions. This research should spur deliberations over the +inevitable advancements in LLM-supported cyber adversarial landscape. +" +Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators,Liang Chen,http://arxiv.org/pdf/2310.07289v1.pdf,2023-10-11,['cs.cl'],2310.07289v1.pdf," Large language models (LLMs) outperform information retrieval techniques for +downstream knowledge-intensive tasks when being prompted to generate world +knowledge. However, community concerns abound regarding the factuality and +potential implications of using this uncensored knowledge. In light of this, we +introduce CONNER, a COmpreheNsive kNowledge Evaluation fRamework, designed to +systematically and automatically evaluate generated knowledge from six +important perspectives -- Factuality, Relevance, Coherence, Informativeness, +Helpfulness and Validity. We conduct an extensive empirical analysis of the +generated knowledge from three different types of LLMs on two widely studied +knowledge-intensive tasks, i.e., open-domain question answering and +knowledge-grounded dialogue. Surprisingly, our study reveals that the +factuality of generated knowledge, even if lower, does not significantly hinder +downstream tasks. Instead, the relevance and coherence of the outputs are more +important than small factual mistakes. Further, we show how to use CONNER to +improve knowledge-intensive tasks by designing two strategies: Prompt +Engineering and Knowledge Selection. Our evaluation code and LLM-generated +knowledge with human annotations will be released to facilitate future +research. +" +Multimodal Large Language Model for Visual Navigation,Yao-Hung Hubert Tsai,http://arxiv.org/pdf/2310.08669v2.pdf,2023-10-12,"['cs.cv', 'cs.ro']",2310.08669v2.pdf," Recent efforts to enable visual navigation using large language models have +mainly focused on developing complex prompt systems. These systems incorporate +instructions, observations, and history into massive text prompts, which are +then combined with pre-trained large language models to facilitate visual +navigation. In contrast, our approach aims to fine-tune large language models +for visual navigation without extensive prompt engineering. Our design involves +a simple text prompt, current observations, and a history collector model that +gathers information from previous observations as input. For output, our design +provides a probability distribution of possible actions that the agent can take +during navigation. We train our model using human demonstrations and collision +signals from the Habitat-Matterport 3D Dataset (HM3D). Experimental results +demonstrate that our method outperforms state-of-the-art behavior cloning +methods and effectively reduces collision rates. +" +GPTutor: an open-source AI pair programming tool alternative to Copilot,Eason Chen,http://arxiv.org/pdf/2310.13896v3.pdf,2023-10-21,['cs.hc'],2310.13896v3.pdf," This paper presents the latest progress of GPTutor: a ChatGPT-powered +programming tool extension in Visual Studio Code. The emergence of Large +Language Models (LLMs) has improved software development efficiency, but their +performance can be hindered by training data limitations and prompt design +issues. Existing LLM development tools often operate as black boxes, with users +unable to view the prompts used and unable to improve performance by correcting +prompts when errors occur. To address the aforementioned issues, GPTutor was +introduced as an open-source AI pair programming tool, offering an alternative +to Copilot. GPTutor empowers users to customize prompts for various programming +languages and scenarios, with support for 120+ human languages and 50+ +programming languages. Users can fine-tune prompts to correct the errors from +LLM for precision and efficient code generation. At the end of the paper, we +underscore GPTutor's potential through examples, including demonstrating its +proficiency in interpreting and generating Sui-Move, a newly introduced smart +contract language, using prompt engineering. +" +Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models,Gabriel Sarch,http://arxiv.org/pdf/2310.15127v1.pdf,2023-10-23,"['cs.ai', 'cs.cl', 'cs.lg', 'cs.ro']",2310.15127v1.pdf," Pre-trained and frozen LLMs can effectively map simple scene re-arrangement +instructions to programs over a robot's visuomotor functions through +appropriate few-shot example prompting. To parse open-domain natural language +and adapt to a user's idiosyncratic procedures, not known during prompt +engineering time, fixed prompts fall short. In this paper, we introduce HELPER, +an embodied agent equipped with an external memory of language-program pairs +that parses free-form human-robot dialogue into action programs through +retrieval-augmented LLM prompting: relevant memories are retrieved based on the +current dialogue, instruction, correction or VLM description, and used as +in-context prompt examples for LLM querying. The memory is expanded during +deployment to include pairs of user's language and action plans, to assist +future inferences and personalize them to the user's language and routines. +HELPER sets a new state-of-the-art in the TEACh benchmark in both Execution +from Dialog History (EDH) and Trajectory from Dialogue (TfD), with 1.7x +improvement over the previous SOTA for TfD. Our models, code and video results +can be found in our project's website: https://helper-agent-llm.github.io. +" +TaskDiff: A Similarity Metric for Task-Oriented Conversations,Ankita Bhaumik,http://arxiv.org/pdf/2310.15298v2.pdf,2023-10-23,"['cs.cl', 'cs.ai']",2310.15298v2.pdf," The popularity of conversational digital assistants has resulted in the +availability of large amounts of conversational data which can be utilized for +improved user experience and personalized response generation. Building these +assistants using popular large language models like ChatGPT also require +additional emphasis on prompt engineering and evaluation methods. Textual +similarity metrics are a key ingredient for such analysis and evaluations. +While many similarity metrics have been proposed in the literature, they have +not proven effective for task-oriented conversations as they do not take +advantage of unique conversational features. To address this gap, we present +TaskDiff, a novel conversational similarity metric that utilizes different +dialogue components (utterances, intents, and slots) and their distributions to +compute similarity. Extensive experimental evaluation of TaskDiff on a +benchmark dataset demonstrates its superior performance and improved robustness +over other related approaches. +" +Large language models for aspect-based sentiment analysis,Paul F. Simmering,http://arxiv.org/pdf/2310.18025v1.pdf,2023-10-27,"['cs.cl', 'cs.ai']",2310.18025v1.pdf," Large language models (LLMs) offer unprecedented text completion +capabilities. As general models, they can fulfill a wide range of roles, +including those of more specialized models. We assess the performance of GPT-4 +and GPT-3.5 in zero shot, few shot and fine-tuned settings on the aspect-based +sentiment analysis (ABSA) task. Fine-tuned GPT-3.5 achieves a state-of-the-art +F1 score of 83.8 on the joint aspect term extraction and polarity +classification task of the SemEval-2014 Task 4, improving upon InstructABSA +[@scaria_instructabsa_2023] by 5.7%. However, this comes at the price of 1000 +times more model parameters and thus increased inference cost. We discuss the +the cost-performance trade-offs of different models, and analyze the typical +errors that they make. Our results also indicate that detailed prompts improve +performance in zero-shot and few-shot settings but are not necessary for +fine-tuned models. This evidence is relevant for practioners that are faced +with the choice of prompt engineering versus fine-tuning when using LLMs for +ABSA. +" +Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias,S. Lee,http://arxiv.org/pdf/2311.00217v1.pdf,2023-11-01,"['cs.ai', 'cs.cy']",2311.00217v1.pdf," Large language models (LLMs) have demonstrated their potential in social +science research by emulating human perceptions and behaviors, a concept +referred to as algorithmic fidelity. This study assesses the algorithmic +fidelity and bias of LLMs by utilizing two nationally representative climate +change surveys. The LLMs were conditioned on demographics and/or psychological +covariates to simulate survey responses. The findings indicate that LLMs can +effectively capture presidential voting behaviors but encounter challenges in +accurately representing global warming perspectives when relevant covariates +are not included. GPT-4 exhibits improved performance when conditioned on both +demographics and covariates. However, disparities emerge in LLM estimations of +the views of certain groups, with LLMs tending to underestimate worry about +global warming among Black Americans. While highlighting the potential of LLMs +to aid social science research, these results underscore the importance of +meticulous conditioning, model selection, survey question format, and bias +assessment when employing LLMs for survey simulation. Further investigation +into prompt engineering and algorithm auditing is essential to harness the +power of LLMs while addressing their inherent limitations. +" +Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral Analysis,Hongyi Zheng,http://arxiv.org/pdf/2311.00258v1.pdf,2023-11-01,"['cs.cl', 'cs.lg']",2311.00258v1.pdf," Recent advances in prompt engineering enable large language models (LLMs) to +solve multi-hop logical reasoning problems with impressive accuracy. However, +there is little existing work investigating the robustness of LLMs with +few-shot prompting techniques. Therefore, we introduce a systematic approach to +test the robustness of LLMs in multi-hop reasoning tasks via domain-agnostic +perturbations. We include perturbations at multiple levels of abstractions +(e.g. lexical perturbations such as typos, and semantic perturbations such as +the inclusion of intermediate reasoning steps in the questions) to conduct +behavioral analysis on the LLMs. Throughout our experiments, we find that +models are more sensitive to certain perturbations such as replacing words with +their synonyms. We also demonstrate that increasing the proportion of perturbed +exemplars in the prompts improves the robustness of few-shot prompting methods. +" +Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers,Weiwei Sun,http://arxiv.org/pdf/2311.01555v1.pdf,2023-11-02,"['cs.ir', 'cs.cl']",2311.01555v1.pdf," Recent studies have demonstrated the great potential of Large Language Models +(LLMs) serving as zero-shot relevance rankers. The typical approach involves +making comparisons between pairs or lists of documents. Although effective, +these listwise and pairwise methods are not efficient and also heavily rely on +intricate prompt engineering. To tackle this problem, we introduce a novel +instruction distillation method. The key idea is to distill the pairwise +ranking ability of open-sourced LLMs to a simpler but more efficient pointwise +ranking. Specifically, given the same LLM, we first rank documents using the +effective pairwise approach with complex instructions, and then distill the +teacher predictions to the pointwise approach with simpler instructions. +Evaluation results on the BEIR, TREC, and ReDial datasets demonstrate that +instruction distillation can improve efficiency by 10 to 100x and also enhance +the ranking performance of LLMs. Furthermore, our approach surpasses the +performance of existing supervised methods like monoT5 and is on par with the +state-of-the-art zero-shot methods. The code to reproduce our results is +available at www.github.com/sunnweiwei/RankGPT. +" +Indicative Summarization of Long Discussions,Shahbaz Syed,http://arxiv.org/pdf/2311.01882v1.pdf,2023-11-03,['cs.cl'],2311.01882v1.pdf," Online forums encourage the exchange and discussion of different stances on +many topics. Not only do they provide an opportunity to present one's own +arguments, but may also gather a broad cross-section of others' arguments. +However, the resulting long discussions are difficult to overview. This paper +presents a novel unsupervised approach using large language models (LLMs) to +generating indicative summaries for long discussions that basically serve as +tables of contents. Our approach first clusters argument sentences, generates +cluster labels as abstractive summaries, and classifies the generated cluster +labels into argumentation frames resulting in a two-level summary. Based on an +extensively optimized prompt engineering approach, we evaluate 19~LLMs for +generative cluster labeling and frame classification. To evaluate the +usefulness of our indicative summaries, we conduct a purpose-driven user study +via a new visual interface called Discussion Explorer: It shows that our +proposed indicative summaries serve as a convenient navigation tool to explore +long discussions. +" +Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models,Jake Chanenson,http://arxiv.org/pdf/2311.02192v1.pdf,2023-11-03,"['cs.cy', 'cs.cl', 'cs.lg']",2311.02192v1.pdf," Identifying contextual integrity (CI) and governing knowledge commons (GKC) +parameters in privacy policy texts can facilitate normative privacy analysis. +However, GKC-CI annotation has heretofore required manual or crowdsourced +effort. This paper demonstrates that high-accuracy GKC-CI parameter annotation +of privacy policies can be performed automatically using large language models. +We fine-tune 18 open-source and proprietary models on 21,588 GKC-CI annotations +from 16 ground truth privacy policies. Our best-performing model (fine-tuned +GPT-3.5 Turbo with prompt engineering) has an accuracy of 86%, exceeding the +performance of prior crowdsourcing approaches despite the complexity of privacy +policy texts and the nuance of the GKC-CI annotation task. We apply our +best-performing model to privacy policies from 164 popular online services, +demonstrating the effectiveness of scaling GKC-CI annotation for data +exploration. We make all annotated policies as well as the training data and +scripts needed to fine-tune our best-performing model publicly available for +future research. +" +Requirements Engineering using Generative AI: Prompts and Prompting Patterns,Krishna Ronanki,http://arxiv.org/pdf/2311.03832v1.pdf,2023-11-07,['cs.se'],2311.03832v1.pdf," [Context]: Companies are increasingly recognizing the importance of +automating Requirements Engineering (RE) tasks due to their resource-intensive +nature. The advent of GenAI has made these tasks more amenable to automation, +thanks to its ability to understand and interpret context effectively. +[Problem]: However, in the context of GenAI, prompt engineering is a critical +factor for success. Despite this, we currently lack tools and methods to +systematically assess and determine the most effective prompt patterns to +employ for a particular RE task. [Method]: Two tasks related to requirements, +specifically requirement classification and tracing, were automated using the +GPT-3.5 turbo API. The performance evaluation involved assessing various +prompts created using 5 prompt patterns and implemented programmatically to +perform the selected RE tasks, focusing on metrics such as precision, recall, +accuracy, and F-Score. [Results]: This paper evaluates the effectiveness of the +5 prompt patterns' ability to make GPT-3.5 turbo perform the selected RE tasks +and offers recommendations on which prompt pattern to use for a specific RE +task. Additionally, it also provides an evaluation framework as a reference for +researchers and practitioners who want to evaluate different prompt patterns +for different RE tasks. +" +Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners,Ningyu Zhang,http://arxiv.org/pdf/2108.13161v7.pdf,2021-08-30,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.ir', 'cs.lg']",2108.13161v7.pdf," Large-scale pre-trained language models have contributed significantly to +natural language processing by demonstrating remarkable abilities as few-shot +learners. However, their effectiveness depends mainly on scaling the model +parameters and prompt design, hindering their implementation in most real-world +applications. This study proposes a novel pluggable, extensible, and efficient +approach named DifferentiAble pRompT (DART), which can convert small language +models into better few-shot learners without any prompt engineering. The main +principle behind this approach involves reformulating potential natural +language processing tasks into the task of a pre-trained language model and +differentially optimizing the prompt template as well as the target label with +backpropagation. Furthermore, the proposed approach can be: (i) Plugged to any +pre-trained language models; (ii) Extended to widespread classification tasks. +A comprehensive evaluation of standard NLP tasks demonstrates that the proposed +approach achieves a better few-shot performance. Code is available in +https://github.com/zjunlp/DART. +" +ActionCLIP: A New Paradigm for Video Action Recognition,Mengmeng Wang,http://arxiv.org/pdf/2109.08472v1.pdf,2021-09-17,['cs.cv'],2109.08472v1.pdf," The canonical approach to video action recognition dictates a neural model to +do a classic and standard 1-of-N majority vote task. They are trained to +predict a fixed set of predefined categories, limiting their transferable +ability on new datasets with unseen concepts. In this paper, we provide a new +perspective on action recognition by attaching importance to the semantic +information of label texts rather than simply mapping them into numbers. +Specifically, we model this task as a video-text matching problem within a +multimodal learning framework, which strengthens the video representation with +more semantic language supervision and enables our model to do zero-shot action +recognition without any further labeled data or parameters requirements. +Moreover, to handle the deficiency of label texts and make use of tremendous +web data, we propose a new paradigm based on this multimodal learning framework +for action recognition, which we dub ""pre-train, prompt and fine-tune"". This +paradigm first learns powerful representations from pre-training on a large +amount of web image-text or video-text data. Then it makes the action +recognition task to act more like pre-training problems via prompt engineering. +Finally, it end-to-end fine-tunes on target datasets to obtain strong +performance. We give an instantiation of the new paradigm, ActionCLIP, which +not only has superior and flexible zero-shot/few-shot transfer ability but also +reaches a top performance on general action recognition task, achieving 83.8% +top-1 accuracy on Kinetics-400 with a ViT-B/16 as the backbone. Code is +available at https://github.com/sallymmx/ActionCLIP.git +" +CLIP-Adapter: Better Vision-Language Models with Feature Adapters,Peng Gao,http://arxiv.org/pdf/2110.04544v1.pdf,2021-10-09,"['cs.cv', 'cs.cl']",2110.04544v1.pdf," Large-scale contrastive vision-language pre-training has shown significant +progress in visual representation learning. Unlike traditional visual systems +trained by a fixed set of discrete labels, a new paradigm was introduced in +\cite{radford2021learning} to directly learn to align images with raw texts in +an open-vocabulary setting. On downstream tasks, a carefully chosen text prompt +is employed to make zero-shot predictions.~To avoid non-trivial prompt +engineering, context optimization \cite{zhou2021coop} has been proposed to +learn continuous vectors as task-specific prompts with few-shot training +examples.~In this paper, we show that there is an alternative path to achieve +better vision-language models other than prompt tuning.~While prompt tuning is +for the textual inputs, we propose CLIP-Adapter to conduct fine-tuning with +feature adapters on either visual or language branch. Specifically, +CLIP-Adapter adopts an additional bottleneck layer to learn new features and +performs residual-style feature blending with the original pre-trained +features.~As a consequence, CLIP-Adapter is able to outperform context +optimization while maintains a simple design. Experiments and extensive +ablation studies on various visual classification tasks demonstrate the +effectiveness of our approach. +" +Symbolic Knowledge Distillation: from General Language Models to Commonsense Models,Peter West,http://arxiv.org/pdf/2110.07178v2.pdf,2021-10-14,['cs.cl'],2110.07178v2.pdf," The common practice for training commonsense models has gone +from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in +order to train commonsense models. In this work, we investigate an alternative, +from-machine-to-corpus-to-machine: general language models author these +commonsense knowledge graphs to train commonsense models. Our study leads to a +new framework, Symbolic Knowledge Distillation. As with prior art in Knowledge +Distillation (Hinton et al., 2015), our approach uses larger models to teach +smaller models. A key difference is that we distill knowledge symbolically-as +text-in addition to the neural model. We also distill only one aspect-the +commonsense of a general language model teacher, allowing the student to be a +different type, a commonsense model. Altogether, we show that careful prompt +engineering and a separately trained critic model allow us to selectively +distill high-quality causal commonsense from GPT-3, a general language model. +Empirical results demonstrate that, for the first time, a human-authored +commonsense knowledge graph is surpassed by our automatically distilled variant +in all three criteria: quantity, quality, and diversity. In addition, it +results in a neural commonsense model that surpasses the teacher model's +commonsense capabilities despite its 100x smaller size. We apply this to the +ATOMIC resource, and share our new symbolic knowledge graph and commonsense +models. +" +Red Teaming Language Models with Language Models,Ethan Perez,http://arxiv.org/pdf/2202.03286v1.pdf,2022-02-07,"['cs.cl', 'cs.ai', 'cs.cr', 'cs.lg']",2202.03286v1.pdf," Language Models (LMs) often cannot be deployed because of their potential to +harm users in hard-to-predict ways. Prior work identifies harmful behaviors +before deployment by using human annotators to hand-write test cases. However, +human annotation is expensive, limiting the number and diversity of test cases. +In this work, we automatically find cases where a target LM behaves in a +harmful way, by generating test cases (""red teaming"") using another LM. We +evaluate the target LM's replies to generated test questions using a classifier +trained to detect offensive content, uncovering tens of thousands of offensive +replies in a 280B parameter LM chatbot. We explore several methods, from +zero-shot generation to reinforcement learning, for generating test cases with +varying levels of diversity and difficulty. Furthermore, we use prompt +engineering to control LM-generated test cases to uncover a variety of other +harms, automatically finding groups of people that the chatbot discusses in +offensive ways, personal and hospital phone numbers generated as the chatbot's +own contact info, leakage of private training data in generated text, and harms +that occur over the course of a conversation. Overall, LM-based red teaming is +one promising tool (among many needed) for finding and fixing diverse, +undesirable LM behaviors before impacting users. +" +Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model,Yu Du,http://arxiv.org/pdf/2203.14940v1.pdf,2022-03-28,['cs.cv'],2203.14940v1.pdf," Recently, vision-language pre-training shows great potential in +open-vocabulary object detection, where detectors trained on base classes are +devised for detecting new classes. The class text embedding is firstly +generated by feeding prompts to the text encoder of a pre-trained +vision-language model. It is then used as the region classifier to supervise +the training of a detector. The key element that leads to the success of this +model is the proper prompt, which requires careful words tuning and ingenious +design. To avoid laborious prompt engineering, there are some prompt +representation learning methods being proposed for the image classification +task, which however can only be sub-optimal solutions when applied to the +detection task. In this paper, we introduce a novel method, detection prompt +(DetPro), to learn continuous prompt representations for open-vocabulary object +detection based on the pre-trained vision-language model. Different from the +previous classification-oriented methods, DetPro has two highlights: 1) a +background interpretation scheme to include the proposals in image background +into the prompt training; 2) a context grading scheme to separate proposals in +image foreground for tailored prompt training. We assemble DetPro with ViLD, a +recent state-of-the-art open-world object detector, and conduct experiments on +the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365 +datasets. Experimental results show that our DetPro outperforms the baseline +ViLD in all settings, e.g., +3.4 APbox and +3.0 APmask improvements on the +novel classes of LVIS. Code and models are available at +https://github.com/dyabel/detpro. +" +No Token Left Behind: Explainability-Aided Image Classification and Generation,Roni Paiss,http://arxiv.org/pdf/2204.04908v2.pdf,2022-04-11,['cs.cv'],2204.04908v2.pdf," The application of zero-shot learning in computer vision has been +revolutionized by the use of image-text matching models. The most notable +example, CLIP, has been widely used for both zero-shot classification and +guiding generative models with a text prompt. However, the zero-shot use of +CLIP is unstable with respect to the phrasing of the input text, making it +necessary to carefully engineer the prompts used. We find that this instability +stems from a selective similarity score, which is based only on a subset of the +semantically meaningful input tokens. To mitigate it, we present a novel +explainability-based approach, which adds a loss term to ensure that CLIP +focuses on all relevant semantic parts of the input, in addition to employing +the CLIP similarity loss used in previous works. When applied to one-shot +classification through prompt engineering, our method yields an improvement in +the recognition rate, without additional training or fine-tuning. Additionally, +we show that CLIP guidance of generative models using our method significantly +improves the generated images. Finally, we demonstrate a novel use of CLIP +guidance for text-based image generation with spatial conditioning on object +location, by requiring the image explainability heatmap for each object to be +confined to a pre-determined bounding box. +" +On Measuring Social Biases in Prompt-Based Multi-Task Learning,Afra Feyza Akyürek,http://arxiv.org/pdf/2205.11605v1.pdf,2022-05-23,"['cs.cl', 'cs.cy']",2205.11605v1.pdf," Large language models trained on a mixture of NLP tasks that are converted +into a text-to-text format using prompts, can generalize into novel forms of +language and handle novel tasks. A large body of work within prompt engineering +attempts to understand the effects of input forms and prompts in achieving +superior performance. We consider an alternative measure and inquire whether +the way in which an input is encoded affects social biases promoted in outputs. +In this paper, we study T0, a large-scale multi-task text-to-text language +model trained using prompt-based learning. We consider two different forms of +semantically equivalent inputs: question-answer format and premise-hypothesis +format. We use an existing bias benchmark for the former BBQ and create the +first bias benchmark in natural language inference BBNLI with hand-written +hypotheses while also converting each benchmark into the other form. The +results on two benchmarks suggest that given two different formulations of +essentially the same input, T0 conspicuously acts more biased in question +answering form, which is seen during training, compared to premise-hypothesis +form which is unlike its training examples. Code and data are released under +https://github.com/feyzaakyurek/bbnli. +" +OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression,Wanhua Li,http://arxiv.org/pdf/2206.02338v2.pdf,2022-06-06,['cs.cv'],2206.02338v2.pdf," This paper presents a language-powered paradigm for ordinal regression. +Existing methods usually treat each rank as a category and employ a set of +weights to learn these concepts. These methods are easy to overfit and usually +attain unsatisfactory performance as the learned concepts are mainly derived +from the training set. Recent large pre-trained vision-language models like +CLIP have shown impressive performance on various visual tasks. In this paper, +we propose to learn the rank concepts from the rich semantic CLIP latent space. +Specifically, we reformulate this task as an image-language matching problem +with a contrastive objective, which regards labels as text and obtains a +language prototype from a text encoder for each rank. While prompt engineering +for CLIP is extremely time-consuming, we propose OrdinalCLIP, a differentiable +prompting method for adapting CLIP for ordinal regression. OrdinalCLIP consists +of learnable context tokens and learnable rank embeddings; The learnable rank +embeddings are constructed by explicitly modeling numerical continuity, +resulting in well-ordered, compact language prototypes in the CLIP space. Once +learned, we can only save the language prototypes and discard the huge language +model, resulting in zero additional computational overhead compared with the +linear head counterpart. Experimental results show that our paradigm achieves +competitive performance in general ordinal regression tasks, and gains +improvements in few-shot and distribution shift settings for age estimation. +The code is available at https://github.com/xk-huang/OrdinalCLIP. +" +P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting,Ziyi Wang,http://arxiv.org/pdf/2208.02812v2.pdf,2022-08-04,"['cs.cv', 'cs.ai', 'cs.lg']",2208.02812v2.pdf," Nowadays, pre-training big models on large-scale datasets has become a +crucial topic in deep learning. The pre-trained models with high representation +ability and transferability achieve a great success and dominate many +downstream tasks in natural language processing and 2D vision. However, it is +non-trivial to promote such a pretraining-tuning paradigm to the 3D vision, +given the limited training data that are relatively inconvenient to collect. In +this paper, we provide a new perspective of leveraging pre-trained 2D knowledge +in 3D domain to tackle this problem, tuning pre-trained image models with the +novel Point-to-Pixel prompting for point cloud analysis at a minor parameter +cost. Following the principle of prompting engineering, we transform point +clouds into colorful images with geometry-preserved projection and +geometry-aware coloring to adapt to pre-trained image models, whose weights are +kept frozen during the end-to-end optimization of point cloud analysis tasks. +We conduct extensive experiments to demonstrate that cooperating with our +proposed Point-to-Pixel Prompting, better pre-trained image model will lead to +consistently better performance in 3D vision. Enjoying prosperous development +from image pre-training field, our method attains 89.3% accuracy on the hardest +setting of ScanObjectNN, surpassing conventional point cloud models with much +fewer trainable parameters. Our framework also exhibits very competitive +performance on ModelNet classification and ShapeNet Part Segmentation. Code is +available at https://github.com/wangzy22/P2P. +" +Unsupervised Hashing with Semantic Concept Mining,Rong-Cheng Tu,http://arxiv.org/pdf/2209.11475v1.pdf,2022-09-23,"['cs.cv', 'cs.ir']",2209.11475v1.pdf," Recently, to improve the unsupervised image retrieval performance, plenty of +unsupervised hashing methods have been proposed by designing a semantic +similarity matrix, which is based on the similarities between image features +extracted by a pre-trained CNN model. However, most of these methods tend to +ignore high-level abstract semantic concepts contained in images. Intuitively, +concepts play an important role in calculating the similarity among images. In +real-world scenarios, each image is associated with some concepts, and the +similarity between two images will be larger if they share more identical +concepts. Inspired by the above intuition, in this work, we propose a novel +Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which +leverages a VLP model to construct a high-quality similarity matrix. +Specifically, a set of randomly chosen concepts is first collected. Then, by +employing a vision-language pretraining (VLP) model with the prompt engineering +which has shown strong power in visual representation learning, the set of +concepts is denoised according to the training images. Next, the proposed +method UHSCM applies the VLP model with prompting again to mine the concept +distribution of each image and construct a high-quality semantic similarity +matrix based on the mined concept distributions. Finally, with the semantic +similarity matrix as guiding information, a novel hashing loss with a modified +contrastive loss based regularization item is proposed to optimize the hashing +network. Extensive experiments on three benchmark datasets show that the +proposed method outperforms the state-of-the-art baselines in the image +retrieval task. +" +Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning,Louis Castricato,http://arxiv.org/pdf/2210.07792v2.pdf,2022-10-14,['cs.cl'],2210.07792v2.pdf," Controlled automated story generation seeks to generate natural language +stories satisfying constraints from natural language critiques or preferences. +Existing methods to control for story preference utilize prompt engineering +which is labor intensive and often inconsistent. They may also use +logit-manipulation methods which require annotated datasets to exist for the +desired attributes. To address these issues, we first train a contrastive +bi-encoder model to align stories with corresponding human critiques, named +CARP, building a general purpose preference model. This is subsequently used as +a reward function to fine-tune a generative language model via reinforcement +learning. However, simply fine-tuning a generative language model with a +contrastive reward model does not always reliably result in a story generation +system capable of generating stories that meet user preferences. To increase +story generation robustness we further fine-tune the contrastive reward model +using a prompt-learning technique. A human participant study is then conducted +comparing generations from our full system, ablations, and two baselines. We +show that the full fine-tuning pipeline results in a story generator preferred +over a LLM 20x as large as well as logit-based methods. This motivates the use +of contrastive learning for general purpose human preference modeling. +" +Towards Equitable Representation in Text-to-Image Synthesis Models with the Cross-Cultural Understanding Benchmark (CCUB) Dataset,Zhixuan Liu,http://arxiv.org/pdf/2301.12073v2.pdf,2023-01-28,['cs.cv'],2301.12073v2.pdf," It has been shown that accurate representation in media improves the +well-being of the people who consume it. By contrast, inaccurate +representations can negatively affect viewers and lead to harmful perceptions +of other cultures. To achieve inclusive representation in generated images, we +propose a culturally-aware priming approach for text-to-image synthesis using a +small but culturally curated dataset that we collected, known here as +Cross-Cultural Understanding Benchmark (CCUB) Dataset, to fight the bias +prevalent in giant datasets. Our proposed approach is comprised of two +fine-tuning techniques: (1) Adding visual context via fine-tuning a pre-trained +text-to-image synthesis model, Stable Diffusion, on the CCUB text-image pairs, +and (2) Adding semantic context via automated prompt engineering using the +fine-tuned large language model, GPT-3, trained on our CCUB culturally-aware +text data. CCUB dataset is curated and our approach is evaluated by people who +have a personal relationship with that particular culture. Our experiments +indicate that priming using both text and image is effective in improving the +cultural relevance and decreasing the offensiveness of generated images while +maintaining quality. +" +Trash to Treasure: Using text-to-image models to inform the design of physical artefacts,Amy Smith,http://arxiv.org/pdf/2302.00561v1.pdf,2023-02-01,['cs.ai'],2302.00561v1.pdf," Text-to-image generative models have recently exploded in popularity and +accessibility. Yet so far, use of these models in creative tasks that bridge +the 2D digital world and the creation of physical artefacts has been +understudied. We conduct a pilot study to investigate if and how text-to-image +models can be used to assist in upstream tasks within the creative process, +such as ideation and visualization, prior to a sculpture-making activity. +Thirty participants selected sculpture-making materials and generated three +images using the Stable Diffusion text-to-image generator, each with text +prompts of their choice, with the aim of informing and then creating a physical +sculpture. The majority of participants (23/30) reported that the generated +images informed their sculptures, and 28/30 reported interest in using +text-to-image models to help them in a creative task in the future. We identify +several prompt engineering strategies and find that a participant's prompting +strategy relates to their stage in the creative process. We discuss how our +findings can inform support for users at different stages of the design process +and for using text-to-image models for physical artefact design. +" +"Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models",Paula Maddigan,http://arxiv.org/pdf/2302.02094v2.pdf,2023-02-04,['cs.hc'],2302.02094v2.pdf," The field of data visualisation has long aimed to devise solutions for +generating visualisations directly from natural language text. Research in +Natural Language Interfaces (NLIs) has contributed towards the development of +such techniques. However, the implementation of workable NLIs has always been +challenging due to the inherent ambiguity of natural language, as well as in +consequence of unclear and poorly written user queries which pose problems for +existing language models in discerning user intent. Instead of pursuing the +usual path of developing new iterations of language models, this study uniquely +proposes leveraging the advancements in pre-trained large language models +(LLMs) such as ChatGPT and GPT-3 to convert free-form natural language directly +into code for appropriate visualisations. This paper presents a novel system, +Chat2VIS, which takes advantage of the capabilities of LLMs and demonstrates +how, with effective prompt engineering, the complex problem of language +understanding can be solved more efficiently, resulting in simpler and more +accurate end-to-end solutions than prior approaches. Chat2VIS shows that LLMs +together with the proposed prompts offer a reliable approach to rendering +visualisations from natural language queries, even when queries are highly +misspecified and underspecified. This solution also presents a significant +reduction in costs for the development of NLI systems, while attaining greater +visualisation inference abilities compared to traditional NLP approaches that +use hand-crafted grammar rules and tailored models. This study also presents +how LLM prompts can be constructed in a way that preserves data security and +privacy while being generalisable to different datasets. This work compares the +performance of GPT-3, Codex and ChatGPT across a number of case studies and +contrasts the performances with prior studies. +" +CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets,Zachary Novack,http://arxiv.org/pdf/2302.02551v3.pdf,2023-02-06,"['cs.cv', 'cs.lg']",2302.02551v3.pdf," Open vocabulary models (e.g. CLIP) have shown strong performance on zero-shot +classification through their ability generate embeddings for each class based +on their (natural language) names. Prior work has focused on improving the +accuracy of these models through prompt engineering or by incorporating a small +amount of labeled downstream data (via finetuning). However, there has been +little focus on improving the richness of the class names themselves, which can +pose issues when class labels are coarsely-defined and are uninformative. We +propose Classification with Hierarchical Label Sets (or CHiLS), an alternative +strategy for zero-shot classification specifically designed for datasets with +implicit semantic hierarchies. CHiLS proceeds in three steps: (i) for each +class, produce a set of subclasses, using either existing label hierarchies or +by querying GPT-3; (ii) perform the standard zero-shot CLIP procedure as though +these subclasses were the labels of interest; (iii) map the predicted subclass +back to its parent to produce the final prediction. Across numerous datasets +with underlying hierarchical structure, CHiLS leads to improved accuracy in +situations both with and without ground-truth hierarchical information. CHiLS +is simple to implement within existing zero-shot pipelines and requires no +additional training cost. Code is available at: +https://github.com/acmi-lab/CHILS. +" +"A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity",Yejin Bang,http://arxiv.org/pdf/2302.04023v2.pdf,2023-02-08,"['cs.cl', 'cs.ai']",2302.04023v2.pdf," This paper proposes a framework for quantitatively evaluating interactive +LLMs such as ChatGPT using publicly available data sets. We carry out an +extensive technical evaluation of ChatGPT using 23 data sets covering 8 +different common NLP application tasks. We evaluate the multitask, multilingual +and multi-modal aspects of ChatGPT based on these data sets and a newly +designed multimodal dataset. We find that ChatGPT outperforms LLMs with +zero-shot learning on most tasks and even outperforms fine-tuned models on some +tasks. We find that it is better at understanding non-Latin script languages +than generating them. It is able to generate multimodal content from textual +prompts, via an intermediate code generation step. Moreover, we find that +ChatGPT is 63.41% accurate on average in 10 different reasoning categories +under logical reasoning, non-textual reasoning, and commonsense reasoning, +hence making it an unreliable reasoner. It is, for example, better at deductive +than inductive reasoning. ChatGPT suffers from hallucination problems like +other LLMs and it generates more extrinsic hallucinations from its parametric +memory as it does not have access to an external knowledge base. Finally, the +interactive feature of ChatGPT enables human collaboration with the underlying +LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ +on machine translation, in a multi-turn ""prompt engineering"" fashion. We also +release codebase for evaluation set extraction. +" +Prompt Stealing Attacks Against Text-to-Image Generation Models,Xinyue Shen,http://arxiv.org/pdf/2302.09923v1.pdf,2023-02-20,"['cs.cr', 'cs.lg']",2302.09923v1.pdf," Text-to-Image generation models have revolutionized the artwork design +process and enabled anyone to create high-quality images by entering text +descriptions called prompts. Creating a high-quality prompt that consists of a +subject and several modifiers can be time-consuming and costly. In consequence, +a trend of trading high-quality prompts on specialized marketplaces has +emerged. In this paper, we propose a novel attack, namely prompt stealing +attack, which aims to steal prompts from generated images by text-to-image +generation models. Successful prompt stealing attacks direct violate the +intellectual property and privacy of prompt engineers and also jeopardize the +business model of prompt trading marketplaces. We first perform a large-scale +analysis on a dataset collected by ourselves and show that a successful prompt +stealing attack should consider a prompt's subject as well as its modifiers. We +then propose the first learning-based prompt stealing attack, PromptStealer, +and demonstrate its superiority over two baseline methods quantitatively and +qualitatively. We also make some initial attempts to defend PromptStealer. In +general, our study uncovers a new attack surface in the ecosystem created by +the popular text-to-image generation models. We hope our results can help to +mitigate the threat. To facilitate research in this field, we will share our +dataset and code with the community. +" +Controlled and Conditional Text to Image Generation with Diffusion Prior,Pranav Aggarwal,http://arxiv.org/pdf/2302.11710v2.pdf,2023-02-23,['cs.cv'],2302.11710v2.pdf," Denoising Diffusion models have shown remarkable performance in generating +diverse, high quality images from text. Numerous techniques have been proposed +on top of or in alignment with models like Stable Diffusion and Imagen that +generate images directly from text. A lesser explored approach is DALLE-2's two +step process comprising a Diffusion Prior that generates a CLIP image embedding +from text and a Diffusion Decoder that generates an image from a CLIP image +embedding. We explore the capabilities of the Diffusion Prior and the +advantages of an intermediate CLIP representation. We observe that Diffusion +Prior can be used in a memory and compute efficient way to constrain the +generation to a specific domain without altering the larger Diffusion Decoder. +Moreover, we show that the Diffusion Prior can be trained with additional +conditional information such as color histogram to further control the +generation. We show quantitatively and qualitatively that the proposed +approaches perform better than prompt engineering for domain specific +generation and existing baselines for color conditioned generation. We believe +that our observations and results will instigate further research into the +diffusion prior and uncover more of its capabilities. +" +EvoPrompting: Language Models for Code-Level Neural Architecture Search,Angelica Chen,http://arxiv.org/pdf/2302.14838v2.pdf,2023-02-28,"['cs.ne', 'cs.ai', 'cs.cl', 'cs.lg']",2302.14838v2.pdf," Given the recent impressive accomplishments of language models (LMs) for code +generation, we explore the use of LMs as adaptive mutation and crossover +operators for an evolutionary neural architecture search (NAS) algorithm. While +NAS still proves too difficult a task for LMs to succeed at solely through +prompting, we find that the combination of evolutionary prompt engineering with +soft prompt-tuning, a method we term EvoPrompting, consistently finds diverse +and high performing models. We first demonstrate that EvoPrompting is effective +on the computationally efficient MNIST-1D dataset, where EvoPrompting produces +convolutional architecture variants that outperform both those designed by +human experts and naive few-shot prompting in terms of accuracy and model size. +We then apply our method to searching for graph neural networks on the CLRS +Algorithmic Reasoning Benchmark, where EvoPrompting is able to design novel +architectures that outperform current state-of-the-art models on 21 out of 30 +algorithmic reasoning tasks while maintaining similar model size. EvoPrompting +is successful at designing accurate and efficient neural network architectures +across a variety of machine learning tasks, while also being general enough for +easy adaptation to other tasks beyond neural network design. +" +Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering,Maciej P. Polak,http://arxiv.org/pdf/2303.05352v2.pdf,2023-03-07,"['cs.cl', 'cond-mat.mtrl-sci']",2303.05352v2.pdf," There has been a growing effort to replace hand extraction of data from +research papers with automated data extraction based on natural language +processing, language models, and recently, large language models (LLMs). +Although these methods enable efficient extraction of data from large sets of +research papers, they require a significant amount of up-front effort, +expertise, and coding. In this work we propose the ChatExtract method that can +fully automate very accurate data extraction with minimal initial effort and +background, using an advanced conversational LLM. ChatExtract consists of a set +of engineered prompts applied to a conversational LLM that both identify +sentences with data, extract that data, and assure the data's correctness +through a series of follow-up questions. These follow-up questions largely +overcome known issues with LLMs providing factually inaccurate responses. +ChatExtract can be applied with any conversational LLMs and yields very high +quality data extraction. In tests on materials data we find precision and +recall both close to 90% from the best conversational LLMs, like ChatGPT-4. We +demonstrate that the exceptional performance is enabled by the information +retention in a conversational model combined with purposeful redundancy and +introducing uncertainty through follow-up prompts. These results suggest that +approaches similar to ChatExtract, due to their simplicity, transferability, +and accuracy are likely to become powerful tools for data extraction in the +near future. Finally, databases for critical cooling rates of metallic glasses +and yield strengths of high entropy alloys are developed using ChatExtract. +" +On Codex Prompt Engineering for OCL Generation: An Empirical Study,Seif Abukhalaf,http://arxiv.org/pdf/2303.16244v1.pdf,2023-03-28,"['cs.se', 'cs.ai']",2303.16244v1.pdf," The Object Constraint Language (OCL) is a declarative language that adds +constraints and object query expressions to MOF models. Despite its potential +to provide precision and conciseness to UML models, the unfamiliar syntax of +OCL has hindered its adoption. Recent advancements in LLMs, such as GPT-3, have +shown their capability in many NLP tasks, including semantic parsing and text +generation. Codex, a GPT-3 descendant, has been fine-tuned on publicly +available code from GitHub and can generate code in many programming languages. +We investigate the reliability of OCL constraints generated by Codex from +natural language specifications. To achieve this, we compiled a dataset of 15 +UML models and 168 specifications and crafted a prompt template with slots to +populate with UML information and the target task, using both zero- and +few-shot learning methods. By measuring the syntactic validity and execution +accuracy metrics of the generated OCL constraints, we found that enriching the +prompts with UML information and enabling few-shot learning increases the +reliability of the generated OCL constraints. Furthermore, the results reveal a +close similarity based on sentence embedding between the generated OCL +constraints and the human-written ones in the ground truth, implying a level of +clarity and understandability in the generated OCL constraints by Codex. +" +Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in Computational Biology,Tiago Lubiana,http://arxiv.org/pdf/2303.16429v1.pdf,2023-03-29,"['q-bio.ot', '92-04']",2303.16429v1.pdf," The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the +scientific community. ChatGPT is a general-purpose chatbot powered by large +language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous +fields, including computational biology. In this article, we offer ten tips +based on our experience with ChatGPT to assist computational biologists in +optimizing their workflows. We have collected relevant prompts and reviewed the +nascent literature in the field, compiling tips we project to remain pertinent +for future ChatGPT and LLM iterations, ranging from code refactoring to +scientific writing to prompt engineering. We hope our work will help +bioinformaticians to complement their workflows while staying aware of the +various implications of using this technology. Additionally, to track new and +creative applications for bioinformatics tools such as ChatGPT, we have +established a GitHub repository at +https://github.com/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical +adherence to ChatGPT and other LLMs will increase the efficiency of +computational biologists, ultimately advancing the pace of scientific discovery +in the life sciences. +" +Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure,Philipp Koralus,http://arxiv.org/pdf/2303.17276v1.pdf,2023-03-30,"['cs.ai', 'cs.cl', 'cs.hc', 'cs.lg', '00, 68', 'i.2.0; i.2.6']",2303.17276v1.pdf," Increase in computational scale and fine-tuning has seen a dramatic +improvement in the quality of outputs of large language models (LLMs) like GPT. +Given that both GPT-3 and GPT-4 were trained on large quantities of +human-generated text, we might ask to what extent their outputs reflect +patterns of human thinking, both for correct and incorrect cases. The Erotetic +Theory of Reason (ETR) provides a symbolic generative model of both human +success and failure in thinking, across propositional, quantified, and +probabilistic reasoning, as well as decision-making. We presented GPT-3, +GPT-3.5, and GPT-4 with 61 central inference and judgment problems from a +recent book-length presentation of ETR, consisting of experimentally verified +data-points on human judgment and extrapolated data-points predicted by ETR, +with correct inference patterns as well as fallacies and framing effects (the +ETR61 benchmark). ETR61 includes classics like Wason's card task, illusory +inferences, the decoy effect, and opportunity-cost neglect, among others. GPT-3 +showed evidence of ETR-predicted outputs for 59% of these examples, rising to +77% in GPT-3.5 and 75% in GPT-4. Remarkably, the production of human-like +fallacious judgments increased from 18% in GPT-3 to 33% in GPT-3.5 and 34% in +GPT-4. This suggests that larger and more advanced LLMs may develop a tendency +toward more human-like mistakes, as relevant thought patterns are inherent in +human-produced training data. According to ETR, the same fundamental patterns +are involved both in successful and unsuccessful ordinary reasoning, so that +the ""bad"" cases could paradoxically be learned from the ""good"" cases. We +further present preliminary evidence that ETR-inspired prompt engineering could +reduce instances of these mistakes. +" +Pair Programming with Large Language Models for Sampling and Estimation of Copulas,Jan Górecki,http://arxiv.org/pdf/2303.18116v1.pdf,2023-03-31,"['cs.cl', 'stat.co', '65c60, 68n19, 68t50']",2303.18116v1.pdf," Without writing a single line of code by a human, an example Monte Carlo +simulation based application for stochastic dependence modeling with copulas is +developed using a state-of-the-art large language model (LLM) fine-tuned for +conversations. This includes interaction with ChatGPT in natural language and +using mathematical formalism, which, under careful supervision by a +human-expert, led to producing a working code in MATLAB, Python and R for +sampling from a given copula model, evaluation of the model's density, +performing maximum likelihood estimation, optimizing the code for parallel +computing for CPUs as well as for GPUs, and visualization of the computed +results. In contrast to other emerging studies that assess the accuracy of LLMs +like ChatGPT on tasks from a selected area, this work rather investigates ways +how to achieve a successful solution of a standard statistical task in a +collaboration of a human-expert and artificial intelligence (AI). Particularly, +through careful prompt engineering, we separate successful solutions generated +by ChatGPT from unsuccessful ones, resulting in a comprehensive list of related +pros and cons. It is demonstrated that if the typical pitfalls are avoided, we +can substantially benefit from collaborating with an AI partner. For example, +we show that if ChatGPT is not able to provide a correct solution due to a lack +of or incorrect knowledge, the human-expert can feed it with the correct +knowledge, e.g., in the form of mathematical theorems and formulas, and make it +to apply the gained knowledge in order to provide a solution that is correct. +Such ability presents an attractive opportunity to achieve a programmed +solution even for users with rather limited knowledge of programming +techniques. +" +"Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing",Walid Hariri,http://arxiv.org/pdf/2304.02017v5.pdf,2023-03-27,['cs.cl'],2304.02017v5.pdf," Large language models have revolutionized the field of artificial +intelligence and have been used in various applications. Among these models, +ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI, +it stands out as a powerful tool that has been widely adopted. ChatGPT has been +successfully applied in numerous areas, including chatbots, content generation, +language translation, personalized recommendations, and even medical diagnosis +and treatment. Its success in these applications can be attributed to its +ability to generate human-like responses, understand natural language, and +adapt to different contexts. Its versatility and accuracy make it a powerful +tool for natural language processing (NLP). However, there are also limitations +to ChatGPT, such as its tendency to produce biased responses and its potential +to perpetuate harmful language patterns. This article provides a comprehensive +overview of ChatGPT, its applications, advantages, and limitations. +Additionally, the paper emphasizes the importance of ethical considerations +when using this robust tool in real-world scenarios. Finally, This paper +contributes to ongoing discussions surrounding artificial intelligence and its +impact on vision and NLP domains by providing insights into prompt engineering +techniques. +" +TagGPT: Large Language Models are Zero-shot Multimodal Taggers,Chen Li,http://arxiv.org/pdf/2304.03022v1.pdf,2023-04-06,['cs.ir'],2304.03022v1.pdf," Tags are pivotal in facilitating the effective distribution of multimedia +content in various applications in the contemporary Internet era, such as +search engines and recommendation systems. Recently, large language models +(LLMs) have demonstrated impressive capabilities across a wide range of tasks. +In this work, we propose TagGPT, a fully automated system capable of tag +extraction and multimodal tagging in a completely zero-shot fashion. Our core +insight is that, through elaborate prompt engineering, LLMs are able to extract +and reason about proper tags given textual clues of multimodal data, e.g., OCR, +ASR, title, etc. Specifically, to automatically build a high-quality tag set +that reflects user intent and interests for a specific application, TagGPT +predicts large-scale candidate tags from a series of raw data via prompting +LLMs, filtered with frequency and semantics. Given a new entity that needs +tagging for distribution, TagGPT introduces two alternative options for +zero-shot tagging, i.e., a generative method with late semantic matching with +the tag set, and another selective method with early matching in prompts. It is +well noticed that TagGPT provides a system-level solution based on a modular +framework equipped with a pre-trained LLM (GPT-3.5 used here) and a sentence +embedding model (SimCSE used here), which can be seamlessly replaced with any +more advanced one you want. TagGPT is applicable for various modalities of data +in modern social media and showcases strong generalization ability to a wide +range of applications. We evaluate TagGPT on publicly available datasets, i.e., +Kuaishou and Food.com, and demonstrate the effectiveness of TagGPT compared to +existing hashtags and off-the-shelf taggers. Project page: +https://github.com/TencentARC/TagGPT. +" +Towards Interpretable Mental Health Analysis with Large Language Models,Kailai Yang,http://arxiv.org/pdf/2304.03347v4.pdf,2023-04-06,['cs.cl'],2304.03347v4.pdf," The latest large language models (LLMs) such as ChatGPT, exhibit strong +capabilities in automated mental health analysis. However, existing relevant +studies bear several limitations, including inadequate evaluations, lack of +prompting strategies, and ignorance of exploring LLMs for explainability. To +bridge these gaps, we comprehensively evaluate the mental health analysis and +emotional reasoning ability of LLMs on 11 datasets across 5 tasks. We explore +the effects of different prompting strategies with unsupervised and distantly +supervised emotional information. Based on these prompts, we explore LLMs for +interpretable mental health analysis by instructing them to generate +explanations for each of their decisions. We convey strict human evaluations to +assess the quality of the generated explanations, leading to a novel dataset +with 163 human-assessed explanations. We benchmark existing automatic +evaluation metrics on this dataset to guide future related works. According to +the results, ChatGPT shows strong in-context learning ability but still has a +significant gap with advanced task-specific methods. Careful prompt engineering +with emotional cues and expert-written few-shot examples can also effectively +improve performance on mental health analysis. In addition, ChatGPT generates +explanations that approach human performance, showing its great potential in +explainable mental health analysis. +" +Low-code LLM: Visual Programming over LLMs,Yuzhe Cai,http://arxiv.org/pdf/2304.08103v2.pdf,2023-04-17,"['cs.cl', 'cs.hc']",2304.08103v2.pdf," Effectively utilizing LLMs for complex tasks is challenging, often involving +a time-consuming and uncontrollable prompt engineering process. This paper +introduces a novel human-LLM interaction framework, Low-code LLM. It +incorporates six types of simple low-code visual programming interactions, all +supported by clicking, dragging, or text editing, to achieve more controllable +and stable responses. Through visual interaction with a graphical user +interface, users can incorporate their ideas into the workflow without writing +trivial prompts. The proposed Low-code LLM framework consists of a Planning LLM +that designs a structured planning workflow for complex tasks, which can be +correspondingly edited and confirmed by users through low-code visual +programming operations, and an Executing LLM that generates responses following +the user-confirmed workflow. We highlight three advantages of the low-code LLM: +controllable generation results, user-friendly human-LLM interaction, and +broadly applicable scenarios. We demonstrate its benefits using four typical +applications. By introducing this approach, we aim to bridge the gap between +humans and LLMs, enabling more effective and efficient utilization of LLMs for +complex tasks. Our system will be soon publicly available at LowCodeLLM. +" +Inducing anxiety in large language models increases exploration and bias,Julian Coda-Forno,http://arxiv.org/pdf/2304.11111v1.pdf,2023-04-21,"['cs.cl', 'cs.ai', 'cs.lg']",2304.11111v1.pdf," Large language models are transforming research on machine learning while +galvanizing public debates. Understanding not only when these models work well +and succeed but also why they fail and misbehave is of great societal +relevance. We propose to turn the lens of computational psychiatry, a framework +used to computationally describe and modify aberrant behavior, to the outputs +produced by these models. We focus on the Generative Pre-Trained Transformer +3.5 and subject it to tasks commonly studied in psychiatry. Our results show +that GPT-3.5 responds robustly to a common anxiety questionnaire, producing +higher anxiety scores than human subjects. Moreover, GPT-3.5's responses can be +predictably changed by using emotion-inducing prompts. Emotion-induction not +only influences GPT-3.5's behavior in a cognitive task measuring exploratory +decision-making but also influences its behavior in a previously-established +task measuring biases such as racism and ableism. Crucially, GPT-3.5 shows a +strong increase in biases when prompted with anxiety-inducing text. Thus, it is +likely that how prompts are communicated to large language models has a strong +influence on their behavior in applied settings. These results progress our +understanding of prompt engineering and demonstrate the usefulness of methods +taken from computational psychiatry for studying the capable algorithms to +which we increasingly delegate authority and autonomy. +" +Is ChatGPT the Ultimate Programming Assistant -- How far is it?,Haoye Tian,http://arxiv.org/pdf/2304.11938v2.pdf,2023-04-24,"['cs.se', 'cs.ai']",2304.11938v2.pdf," Recently, the ChatGPT LLM has received great attention: it can be used as a +bot for discussing source code, prompting it to suggest changes, provide +descriptions or even generate code. Typical demonstrations generally focus on +existing benchmarks, which may have been used in model training (i.e., data +leakage). To assess the feasibility of using an LLM as a useful assistant bot +for programmers, we must assess its realistic capabilities on unseen problems +as well as its capabilities on various tasks. In this paper, we present an +empirical study of ChatGPT's potential as a fully automated programming +assistant, focusing on the tasks of code generation, program repair, and code +summariziation. The study investigates ChatGPT's performance on common +programming problems and compares it with state-of-the-art approaches on two +benchmarks. Among several findings, our study shows that ChatGPT is effective +in dealing with common programming problems. However, our experiments also +reveal limitations in terms of its attention span: detailed descriptions will +constrain the focus of ChatGPT and prevent it from leveraging its vast +knowledge to solve the actual problem. Surprisingly, we have identified the +ability of ChatGPT to reason the original intention of the code. We expect +future work to build on this insight for dealing with the open question of the +oracle problem. Our findings contribute interesting insights to the development +of LLMs for programming assistance, notably by demonstrating the importance of +prompt engineering, and providing a better understanding of ChatGPT's practical +applications for software engineering. +" +Framing the News:From Human Perception to Large Language Model Inferences,David Alonso del Barrio,http://arxiv.org/pdf/2304.14456v1.pdf,2023-04-27,"['cs.cl', 'cs.hc']",2304.14456v1.pdf," Identifying the frames of news is important to understand the articles' +vision, intention, message to be conveyed, and which aspects of the news are +emphasized. Framing is a widely studied concept in journalism, and has emerged +as a new topic in computing, with the potential to automate processes and +facilitate the work of journalism professionals. In this paper, we study this +issue with articles related to the Covid-19 anti-vaccine movement. First, to +understand the perspectives used to treat this theme, we developed a protocol +for human labeling of frames for 1786 headlines of No-Vax movement articles of +European newspapers from 5 countries. Headlines are key units in the written +press, and worth of analysis as many people only read headlines (or use them to +guide their decision for further reading.) Second, considering advances in +Natural Language Processing (NLP) with large language models, we investigated +two approaches for frame inference of news headlines: first with a GPT-3.5 +fine-tuning approach, and second with GPT-3.5 prompt-engineering. Our work +contributes to the study and analysis of the performance that these models have +to facilitate journalistic tasks like classification of frames, while +understanding whether the models are able to replicate human perception in the +identification of these frames. +" +"ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations",Chunkit Chan,http://arxiv.org/pdf/2304.14827v2.pdf,2023-04-28,['cs.cl'],2304.14827v2.pdf," This paper aims to quantitatively evaluate the performance of ChatGPT, an +interactive large language model, on inter-sentential relations such as +temporal relations, causal relations, and discourse relations. Given ChatGPT's +promising performance across various tasks, we conduct extensive evaluations on +the whole test sets of 13 datasets, including temporal and causal relations, +PDTB2.0-based and dialogue-based discourse relations, and downstream +applications on discourse understanding. To achieve reliable results, we adopt +three tailored prompt templates for each task, including the zero-shot prompt +template, zero-shot prompt engineering (PE) template, and in-context learning +(ICL) prompt template, to establish the initial baseline scores for all popular +sentence-pair relation classification tasks for the first time. We find that +ChatGPT exhibits strong performance in detecting and reasoning about causal +relations, while it may not be proficient in identifying the temporal order +between two events. It can recognize most discourse relations with existing +explicit discourse connectives, but the implicit discourse relation still +remains a challenging task. Meanwhile, ChatGPT performs poorly in the dialogue +discourse parsing task that requires structural understanding in a dialogue +before being aware of the discourse relation. +" +Large Language Models Can Be Used To Effectively Scale Spear Phishing Campaigns,Julian Hazell,http://arxiv.org/pdf/2305.06972v2.pdf,2023-05-11,"['cs.cy', 'cs.ai', 'cs.cr']",2305.06972v2.pdf," Recent progress in artificial intelligence (AI), particularly in the domain +of large language models (LLMs), has resulted in powerful and versatile +dual-use systems. Indeed, cognition can be put towards a wide variety of tasks, +some of which can result in harm. This study investigates how LLMs can be used +for spear phishing, a form of cybercrime that involves manipulating targets +into divulging sensitive information. I first explore LLMs' ability to assist +with the reconnaissance and message generation stages of a successful spear +phishing attack, where I find that advanced LLMs are capable of improving +cybercriminals' efficiency during these stages. To explore how LLMs can be used +to scale spear phishing campaigns, I then create unique spear phishing messages +for over 600 British Members of Parliament using OpenAI's GPT-3.5 and GPT-4 +models. My findings reveal that these messages are not only realistic but also +cost-effective, with each email costing only a fraction of a cent to generate. +Next, I demonstrate how basic prompt engineering can circumvent safeguards +installed in LLMs by the reinforcement learning from human feedback fine-tuning +process, highlighting the need for more robust governance interventions aimed +at preventing misuse. To address these evolving risks, I propose two potential +solutions: structured access schemes, such as application programming +interfaces, and LLM-based defensive systems. +" +Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural Language Cohort Discovery,Pranav Kulkarni,http://arxiv.org/pdf/2305.07637v2.pdf,2023-05-12,"['cs.lg', 'cs.cl', 'cs.hc', 'cs.ir']",2305.07637v2.pdf," The Imaging Data Commons (IDC) is a cloud-based database that provides +researchers with open access to cancer imaging data, with the goal of +facilitating collaboration in medical imaging research. However, querying the +IDC database for cohort discovery and access to imaging data has a significant +learning curve for researchers due to its complex nature. We developed +Text2Cohort, a large language model (LLM) based toolkit to facilitate +user-friendly and intuitive natural language cohort discovery in the IDC. +Text2Cohorts translates user input into IDC database queries using prompt +engineering and autocorrection and returns the query's response to the user. +Autocorrection resolves errors in queries by passing the errors back to the +model for interpretation and correction. We evaluate Text2Cohort on 50 natural +language user inputs ranging from information extraction to cohort discovery. +The resulting queries and outputs were verified by two computer scientists to +measure Text2Cohort's accuracy and F1 score. Text2Cohort successfully generated +queries and their responses with an 88% accuracy and F1 score of 0.94. However, +it failed to generate queries for 6/50 (12%) user inputs due to syntax and +semantic errors. Our results indicate that Text2Cohort succeeded at generating +queries with correct responses, but occasionally failed due to a lack of +understanding of the data schema. Despite these shortcomings, Text2Cohort +demonstrates the utility of LLMs to enable researchers to discover and curate +cohorts using data hosted on IDC with high levels of accuracy using natural +language in a more intuitive and user-friendly way. +" +Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks,Chengguang Gan,http://arxiv.org/pdf/2305.08714v2.pdf,2023-05-15,"['cs.cl', 'cs.ai']",2305.08714v2.pdf," Prompt engineering relevance research has seen a notable surge in recent +years, primarily driven by advancements in pre-trained language models and +large language models. However, a critical issue has been identified within +this domain: the inadequate of sensitivity and robustness of these models +towards Prompt Templates, particularly in lesser-studied languages such as +Japanese. This paper explores this issue through a comprehensive evaluation of +several representative Large Language Models (LLMs) and a widely-utilized +pre-trained model(PLM). These models are scrutinized using a benchmark dataset +in Japanese, with the aim to assess and analyze the performance of the current +multilingual models in this context. Our experimental results reveal startling +discrepancies. A simple modification in the sentence structure of the Prompt +Template led to a drastic drop in the accuracy of GPT-4 from 49.21 to 25.44. +This observation underscores the fact that even the highly performance GPT-4 +model encounters significant stability issues when dealing with diverse +Japanese prompt templates, rendering the consistency of the model's output +results questionable. In light of these findings, we conclude by proposing +potential research trajectories to further enhance the development and +performance of Large Language Models in their current stage. +" +Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs,Jiao Chen,http://arxiv.org/pdf/2305.09858v1.pdf,2023-05-17,"['cs.ir', 'cs.ai', 'cs.cl', 'cs.lg']",2305.09858v1.pdf," Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system +performance by providing structured information about entities and their +relationships, such as complementary or substitutable relations between +products or product types, which can be utilized in recommender systems. +However, relation labeling in KGs remains a challenging task due to the dynamic +nature of e-commerce domains and the associated cost of human labor. Recently, +breakthroughs in Large Language Models (LLMs) have shown surprising results in +numerous natural language processing tasks. In this paper, we conduct an +empirical study of LLMs for relation labeling in e-commerce KGs, investigating +their powerful learning capabilities in natural language and effectiveness in +predicting relations between product types with limited labeled data. We +evaluate various LLMs, including PaLM and GPT-3.5, on benchmark datasets, +demonstrating their ability to achieve competitive performance compared to +humans on relation labeling tasks using just 1 to 5 labeled examples per +relation. Additionally, we experiment with different prompt engineering +techniques to examine their impact on model performance. Our results show that +LLMs significantly outperform existing KG completion models in relation +labeling for e-commerce KGs and exhibit performance strong enough to replace +human labeling. +" +VisorGPT: Learning Visual Prior via Generative Pre-Training,Jinheng Xie,http://arxiv.org/pdf/2305.13777v4.pdf,2023-05-23,['cs.cv'],2305.13777v4.pdf," Various stuff and things in visual data possess specific traits, which can be +learned by deep neural networks and are implicitly represented as the visual +prior, e.g., object location and shape, in the model. Such prior potentially +impacts many vision tasks. For example, in conditional image synthesis, spatial +conditions failing to adhere to the prior can result in visually inaccurate +synthetic results. This work aims to explicitly learn the visual prior and +enable the customization of sampling. Inspired by advances in language +modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed +VisorGPT. By discretizing visual locations of objects, e.g., bounding boxes, +human pose, and instance masks, into sequences, VisorGPT can model visual prior +through likelihood maximization. Besides, prompt engineering is investigated to +unify various visual locations and enable customized sampling of sequential +outputs from the learned prior. Experimental results demonstrate that VisorGPT +can effectively model the visual prior, which can be employed for many vision +tasks, such as customizing accurate human pose for conditional image synthesis +models like ControlNet. Code will be released at +https://github.com/Sierkinhane/VisorGPT. +" +Game of Tones: Faculty detection of GPT-4 generated content in university assessments,Mike Perkins,http://arxiv.org/pdf/2305.18081v1.pdf,2023-05-29,"['cs.cy', 'cs.ai', 'k.4']",2305.18081v1.pdf," This study explores the robustness of university assessments against the use +of Open AI's Generative Pre-Trained Transformer 4 (GPT-4) generated content and +evaluates the ability of academic staff to detect its use when supported by the +Turnitin Artificial Intelligence (AI) detection tool. The research involved +twenty-two GPT-4 generated submissions being created and included in the +assessment process to be marked by fifteen different faculty members. The study +reveals that although the detection tool identified 91% of the experimental +submissions as containing some AI-generated content, the total detected content +was only 54.8%. This suggests that the use of adversarial techniques regarding +prompt engineering is an effective method in evading AI detection tools and +highlights that improvements to AI detection software are needed. Using the +Turnitin AI detect tool, faculty reported 54.5% of the experimental submissions +to the academic misconduct process, suggesting the need for increased awareness +and training into these tools. Genuine submissions received a mean score of +54.4, whereas AI-generated content scored 52.3, indicating the comparable +performance of GPT-4 in real-life situations. Recommendations include adjusting +assessment strategies to make them more resistant to the use of AI tools, using +AI-inclusive assessment where possible, and providing comprehensive training +programs for faculty and students. This research contributes to understanding +the relationship between AI-generated content and academic assessment, urging +further investigation to preserve academic integrity. +" +Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators,Zhizheng Zhang,http://arxiv.org/pdf/2306.01242v1.pdf,2023-06-02,"['cs.ai', 'cs.cl']",2306.01242v1.pdf," The recent success of Large Language Models (LLMs) signifies an impressive +stride towards artificial general intelligence. They have shown a promising +prospect in automatically completing tasks upon user instructions, functioning +as brain-like coordinators. The associated risks will be revealed as we +delegate an increasing number of tasks to machines for automated completion. A +big question emerges: how can we make machines behave responsibly when helping +humans automate tasks as personal copilots? In this paper, we explore this +question in depth from the perspectives of feasibility, completeness and +security. In specific, we present Responsible Task Automation (ResponsibleTA) +as a fundamental framework to facilitate responsible collaboration between +LLM-based coordinators and executors for task automation with three empowered +capabilities: 1) predicting the feasibility of the commands for executors; 2) +verifying the completeness of executors; 3) enhancing the security (e.g., the +protection of users' privacy). We further propose and compare two paradigms for +implementing the first two capabilities. One is to leverage the generic +knowledge of LLMs themselves via prompt engineering while the other is to adopt +domain-specific learnable models. Moreover, we introduce a local memory +mechanism for achieving the third capability. We evaluate our proposed +ResponsibleTA on UI task automation and hope it could bring more attentions to +ensuring LLMs more responsible in diverse scenarios. The research project +homepage is at +https://task-automation-research.github.io/responsible_task_automation. +" +A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering,Chaoning Zhang,http://arxiv.org/pdf/2306.06211v3.pdf,2023-05-12,['cs.cv'],2306.06211v3.pdf," Segment anything model (SAM) developed by Meta AI Research has recently +attracted significant attention. Trained on a large segmentation dataset of +over 1 billion masks, SAM is capable of segmenting any object on a certain +image. In the original SAM work, the authors turned to zero-short transfer +tasks (like edge detection) for evaluating the performance of SAM. Recently, +numerous works have attempted to investigate the performance of SAM in various +scenarios to recognize and segment objects. Moreover, numerous projects have +emerged to show the versatility of SAM as a foundation model by combining it +with other models, like Grounding DINO, Stable Diffusion, ChatGPT, etc. With +the relevant papers and projects increasing exponentially, it is challenging +for the readers to catch up with the development of SAM. To this end, this work +conducts the first yet comprehensive survey on SAM. This is an ongoing project +and we intend to update the manuscript on a regular basis. Therefore, readers +are welcome to contact us if they complete new works related to SAM so that we +can include them in our next version. +" +The economic trade-offs of large language models: A case study,Kristen Howell,http://arxiv.org/pdf/2306.07402v1.pdf,2023-06-08,"['cs.cl', 'cs.ai']",2306.07402v1.pdf," Contacting customer service via chat is a common practice. Because employing +customer service agents is expensive, many companies are turning to NLP that +assists human agents by auto-generating responses that can be used directly or +with modifications. Large Language Models (LLMs) are a natural fit for this use +case; however, their efficacy must be balanced with the cost of training and +serving them. This paper assesses the practical cost and impact of LLMs for the +enterprise as a function of the usefulness of the responses that they generate. +We present a cost framework for evaluating an NLP model's utility for this use +case and apply it to a single brand as a case study in the context of an +existing agent assistance product. We compare three strategies for specializing +an LLM - prompt engineering, fine-tuning, and knowledge distillation - using +feedback from the brand's customer service agents. We find that the usability +of a model's responses can make up for a large difference in inference cost for +our case study brand, and we extrapolate our findings to the broader enterprise +space. +" +TART: A plug-and-play Transformer module for task-agnostic reasoning,Kush Bhatia,http://arxiv.org/pdf/2306.07536v1.pdf,2023-06-13,"['cs.lg', 'cs.ai', 'cs.cl']",2306.07536v1.pdf," Large language models (LLMs) exhibit in-context learning abilities which +enable the same model to perform several tasks without any task-specific +training. In contrast, traditional adaptation approaches, such as fine-tuning, +modify the underlying models for each specific task. In-context learning, +however, consistently underperforms task-specific tuning approaches even when +presented with the same examples. While most existing approaches (e.g., prompt +engineering) focus on the LLM's learned representations to patch this +performance gap, our analysis actually reveal that LLM representations contain +sufficient information to make good predictions. As such, we focus on the LLM's +reasoning abilities and demonstrate that this performance gap exists due to +their inability to perform simple probabilistic reasoning tasks. This raises an +intriguing question: Are LLMs actually capable of learning how to reason in a +task-agnostic manner? We answer this in the affirmative and propose TART which +generically improves an LLM's reasoning abilities using a synthetically trained +Transformer-based reasoning module. TART trains this reasoning module in a +task-agnostic manner using only synthetic logistic regression tasks and +composes it with an arbitrary real-world pre-trained model without any +additional training. With a single inference module, TART improves performance +across different model families (GPT-Neo, Pythia, BLOOM), model sizes (100M - +6B), tasks (14 NLP binary classification tasks), and even across different +modalities (audio and vision). Additionally, on the RAFT Benchmark, TART +improves GPT-Neo (125M)'s performance such that it outperforms BLOOM (176B), +and is within 4% of GPT-3 (175B). Our code and models are available at +https://github.com/HazyResearch/TART . +" +Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards,Alexander van Meekeren,http://arxiv.org/pdf/2306.11763v1.pdf,2023-06-20,['cs.cv'],2306.11763v1.pdf," Deep object detection models have achieved notable successes in recent years, +but one major obstacle remains: the requirement for a large amount of training +data. Obtaining such data is a tedious process and is mainly time consuming, +leading to the exploration of new research avenues like synthetic data +generation techniques. In this study, we explore the usability of Stable +Diffusion 2.1-base for generating synthetic datasets of apple trees for object +detection and compare it to a baseline model trained on real-world data. After +creating a dataset of realistic apple trees with prompt engineering and +utilizing a previously trained Stable Diffusion model, the custom dataset was +annotated and evaluated by training a YOLOv5m object detection model to predict +apples in a real-world apple detection dataset. YOLOv5m was chosen for its +rapid inference time and minimal hardware demands. Results demonstrate that the +model trained on generated data is slightly underperforming compared to a +baseline model trained on real-world images when evaluated on a set of +real-world images. However, these findings remain highly promising, as the +average precision difference is only 0.09 and 0.06, respectively. Qualitative +results indicate that the model can accurately predict the location of apples, +except in cases of heavy shading. These findings illustrate the potential of +synthetic data generation techniques as a viable alternative to the collection +of extensive training data for object detection models. +" +Do you still need a manual smart contract audit?,Isaac David,http://arxiv.org/pdf/2306.12338v2.pdf,2023-06-21,['cs.cr'],2306.12338v2.pdf," We investigate the feasibility of employing large language models (LLMs) for +conducting the security audit of smart contracts, a traditionally +time-consuming and costly process. Our research focuses on the optimization of +prompt engineering for enhanced security analysis, and we evaluate the +performance and accuracy of LLMs using a benchmark dataset comprising 52 +Decentralized Finance (DeFi) smart contracts that have previously been +compromised. + Our findings reveal that, when applied to vulnerable contracts, both GPT-4 +and Claude models correctly identify the vulnerability type in 40% of the +cases. However, these models also demonstrate a high false positive rate, +necessitating continued involvement from manual auditors. The LLMs tested +outperform a random model by 20% in terms of F1-score. + To ensure the integrity of our study, we conduct mutation testing on five +newly developed and ostensibly secure smart contracts, into which we manually +insert two and 15 vulnerabilities each. This testing yielded a remarkable +best-case 78.7% true positive rate for the GPT-4-32k model. We tested both, +asking the models to perform a binary classification on whether a contract is +vulnerable, and a non-binary prompt. We also examined the influence of model +temperature variations and context length on the LLM's performance. + Despite the potential for many further enhancements, this work lays the +groundwork for a more efficient and economical approach to smart contract +security audits. +" +MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models,Chaoyou Fu,http://arxiv.org/pdf/2306.13394v2.pdf,2023-06-23,['cs.cv'],2306.13394v2.pdf," Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform +multimodal tasks, showing amazing emergent abilities in recent studies, such as +writing poems based on an image. However, it is difficult for these case +studies to fully reflect the performance of MLLM, lacking a comprehensive +evaluation. In this paper, we fill in this blank, presenting the first MLLM +Evaluation benchmark MME. It measures both perception and cognition abilities +on a total of 14 subtasks. In order to avoid data leakage that may arise from +direct use of public datasets for evaluation, the annotations of +instruction-answer pairs are all manually designed. The concise instruction +design allows us to fairly compare MLLMs, instead of struggling in prompt +engineering. Besides, with such an instruction, we can also easily carry out +quantitative statistics. A total of 12 advanced MLLMs are comprehensively +evaluated on our MME, which not only suggests that existing MLLMs still have a +large room for improvement, but also reveals the potential directions for the +subsequent model optimization. +" +Zero-shot Nuclei Detection via Visual-Language Pre-trained Models,Yongjian Wu,http://arxiv.org/pdf/2306.17659v1.pdf,2023-06-30,['cs.cv'],2306.17659v1.pdf," Large-scale visual-language pre-trained models (VLPM) have proven their +excellent performance in downstream object detection for natural scenes. +However, zero-shot nuclei detection on H\&E images via VLPMs remains +underexplored. The large gap between medical images and the web-originated +text-image pairs used for pre-training makes it a challenging task. In this +paper, we attempt to explore the potential of the object-level VLPM, Grounded +Language-Image Pre-training (GLIP) model, for zero-shot nuclei detection. +Concretely, an automatic prompts design pipeline is devised based on the +association binding trait of VLPM and the image-to-text VLPM BLIP, avoiding +empirical manual prompts engineering. We further establish a self-training +framework, using the automatically designed prompts to generate the preliminary +results as pseudo labels from GLIP and refine the predicted boxes in an +iterative manner. Our method achieves a remarkable performance for label-free +nuclei detection, surpassing other comparison methods. Foremost, our work +demonstrates that the VLPM pre-trained on natural image-text pairs exhibits +astonishing potential for downstream tasks in the medical field as well. Code +will be released at https://github.com/wuyongjianCODE/VLPMNuD. +" +Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues,Dollaya Hirunyasiri,http://arxiv.org/pdf/2307.02018v1.pdf,2023-07-05,"['cs.cl', 'cs.ai', 'cs.hc']",2307.02018v1.pdf," Research suggests that providing specific and timely feedback to human tutors +enhances their performance. However, it presents challenges due to the +time-consuming nature of assessing tutor performance by human evaluators. Large +language models, such as the AI-chatbot ChatGPT, hold potential for offering +constructive feedback to tutors in practical settings. Nevertheless, the +accuracy of AI-generated feedback remains uncertain, with scant research +investigating the ability of models like ChatGPT to deliver effective feedback. +In this work-in-progress, we evaluate 30 dialogues generated by GPT-4 in a +tutor-student setting. We use two different prompting approaches, the zero-shot +chain of thought and the few-shot chain of thought, to identify specific +components of effective praise based on five criteria. These approaches are +then compared to the results of human graders for accuracy. Our goal is to +assess the extent to which GPT-4 can accurately identify each praise criterion. +We found that both zero-shot and few-shot chain of thought approaches yield +comparable results. GPT-4 performs moderately well in identifying instances +when the tutor offers specific and immediate praise. However, GPT-4 +underperforms in identifying the tutor's ability to deliver sincere praise, +particularly in the zero-shot prompting scenario where examples of sincere +tutor praise statements were not provided. Future work will focus on enhancing +prompt engineering, developing a more general tutoring rubric, and evaluating +our method using real-life tutoring dialogues. +" +"Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions",Dawen Zhang,http://arxiv.org/pdf/2307.03941v3.pdf,2023-07-08,"['cs.cy', 'cs.ai', 'cs.cl']",2307.03941v3.pdf," The Right to be Forgotten (RTBF) was first established as the result of the +ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja Gonz\'alez, and +was later included as the Right to Erasure under the General Data Protection +Regulation (GDPR) of European Union to allow individuals the right to request +personal data be deleted by organizations. Specifically for search engines, +individuals can send requests to organizations to exclude their information +from the query results. It was a significant emergent right as the result of +the evolution of technology. With the recent development of Large Language +Models (LLMs) and their use in chatbots, LLM-enabled software systems have +become popular. But they are not excluded from the RTBF. Compared with the +indexing approach used by search engines, LLMs store, and process information +in a completely different way. This poses new challenges for compliance with +the RTBF. In this paper, we explore these challenges and provide our insights +on how to implement technical solutions for the RTBF, including the use of +differential privacy, machine unlearning, model editing, and prompt +engineering. With the rapid advancement of AI and the increasing need of +regulating this powerful technology, learning from the case of RTBF can provide +valuable lessons for technical practitioners, legal experts, organizations, and +authorities. +" +"Software Testing with Large Language Model: Survey, Landscape, and Vision",Junjie Wang,http://arxiv.org/pdf/2307.07221v1.pdf,2023-07-14,['cs.se'],2307.07221v1.pdf," Pre-trained large language models (LLMs) have recently emerged as a +breakthrough technology in natural language processing and artificial +intelligence, with the ability to handle large-scale datasets and exhibit +remarkable performance across a wide range of tasks. Meanwhile, software +testing is a crucial undertaking that serves as a cornerstone for ensuring the +quality and reliability of software products. As the scope and complexity of +software systems continue to grow, the need for more effective software testing +techniques becomes increasingly urgent, and making it an area ripe for +innovative approaches such as the use of LLMs. This paper provides a +comprehensive review of the utilization of LLMs in software testing. It +analyzes 52 relevant studies that have used LLMs for software testing, from +both the software testing and LLMs perspectives. The paper presents a detailed +discussion of the software testing tasks for which LLMs are commonly used, +among which test case preparation and program repair are the most +representative ones. It also analyzes the commonly used LLMs, the types of +prompt engineering that are employed, as well as the accompanied techniques +with these LLMs. It also summarizes the key challenges and potential +opportunities in this direction. This work can serve as a roadmap for future +research in this area, highlighting potential avenues for exploration, and +identifying gaps in our current understanding of the use of LLMs in software +testing. +" +The Potential and Pitfalls of using a Large Language Model such as ChatGPT or GPT-4 as a Clinical Assistant,Jingqing Zhang,http://arxiv.org/pdf/2307.08152v1.pdf,2023-07-16,['cs.cl'],2307.08152v1.pdf," Recent studies have demonstrated promising performance of ChatGPT and GPT-4 +on several medical domain tasks. However, none have assessed its performance +using a large-scale real-world electronic health record database, nor have +evaluated its utility in providing clinical diagnostic assistance for patients +across a full range of disease presentation. We performed two analyses using +ChatGPT and GPT-4, one to identify patients with specific medical diagnoses +using a real-world large electronic health record database and the other, in +providing diagnostic assistance to healthcare workers in the prospective +evaluation of hypothetical patients. Our results show that GPT-4 across disease +classification tasks with chain of thought and few-shot prompting can achieve +performance as high as 96% F1 scores. For patient assessment, GPT-4 can +accurately diagnose three out of four times. However, there were mentions of +factually incorrect statements, overlooking crucial medical findings, +recommendations for unnecessary investigations and overtreatment. These issues +coupled with privacy concerns, make these models currently inadequate for real +world clinical use. However, limited data and time needed for prompt +engineering in comparison to configuration of conventional machine learning +workflows highlight their potential for scalability across healthcare +applications. +" +A Lightweight Framework for High-Quality Code Generation,Mohammed Latif Siddiq,http://arxiv.org/pdf/2307.08220v1.pdf,2023-07-17,"['cs.se', 'cs.lg']",2307.08220v1.pdf," In recent years, the use of automated source code generation utilizing +transformer-based generative models has expanded, and these models can generate +functional code according to the requirements of the developers. However, +recent research revealed that these automatically generated source codes can +contain vulnerabilities and other quality issues. Despite researchers' and +practitioners' attempts to enhance code generation models, retraining and +fine-tuning large language models is time-consuming and resource-intensive. +Thus, we describe FRANC, a lightweight framework for recommending more secure +and high-quality source code derived from transformer-based code generation +models. FRANC includes a static filter to make the generated code compilable +with heuristics and a quality-aware ranker to sort the code snippets based on a +quality score. Moreover, the framework uses prompt engineering to fix +persistent quality issues. We evaluated the framework with five Python and Java +code generation models and six prompt datasets, including a newly created one +in this work (SOEval). The static filter improves 9% to 46% Java suggestions +and 10% to 43% Python suggestions regarding compilability. The average +improvement over the NDCG@10 score for the ranking system is 0.0763, and the +repairing techniques repair the highest 80% of prompts. FRANC takes, on +average, 1.98 seconds for Java; for Python, it takes 0.08 seconds. +" +"Multi-Method Self-Training: Improving Code Generation With Text, And Vice Versa",Shriyash K. Upadhyay,http://arxiv.org/pdf/2307.10633v1.pdf,2023-07-20,"['cs.cl', 'cs.lg']",2307.10633v1.pdf," Large Language Models have many methods for solving the same problem. This +introduces novel strengths (different methods may work well for different +problems) and weaknesses (it may be difficult for users to know which method to +use). In this paper, we introduce Multi-Method Self-Training (MMST), where one +method is trained on the filtered outputs of another, allowing us to augment +the strengths and ameliorate the weaknesses of each method. Using a 176B +parameter model trained on both language and code, we show that MMST can 1) +improve the less performant method (up to 30%) making the model easier to use, +2) improve the more performant method (up to 32.2%) making the model more +performant, and 3) improve the performance of related but distinct tasks (up to +10.3%) by improving the ability of the model to generate rationales. We then +conduct ablation analyses to explore why MMST works. We show that MMST +generates more data than traditional self-training, but the improvement in +performance is driven by the use of multiple methods. We also analyze +prompt-engineering and anti-correlated performance between methods as means of +making MMST more effective. We hope the evidence from our paper motivates +machine learning researchers to explore ways in which advances in language +models allow for new forms of training. +" +Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts,Mayug Maniparambil,http://arxiv.org/pdf/2307.11661v2.pdf,2023-07-21,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2307.11661v2.pdf," Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have +revolutionized visual representation learning by providing good performance on +downstream datasets. VLMs are 0-shot adapted to a downstream dataset by +designing prompts that are relevant to the dataset. Such prompt engineering +makes use of domain expertise and a validation dataset. Meanwhile, recent +developments in generative pretrained models like GPT-4 mean they can be used +as advanced internet search tools. They can also be manipulated to provide +visual information in any structure. In this work, we show that GPT-4 can be +used to generate text that is visually descriptive and how this can be used to +adapt CLIP to downstream tasks. We show considerable improvements in 0-shot +transfer accuracy on specialized fine-grained datasets like EuroSAT (~7%), DTD +(~7%), SUN397 (~4.6%), and CUB (~3.3%) when compared to CLIP's default prompt. +We also design a simple few-shot adapter that learns to choose the best +possible sentences to construct generalizable classifiers that outperform the +recently proposed CoCoOP by ~2% on average and by over 4% on 4 specialized +fine-grained datasets. The code, prompts, and auxiliary text dataset is +available at https://github.com/mayug/VDT-Adapter. +" +GPT-3 Models are Few-Shot Financial Reasoners,Raul Salles de Padua,http://arxiv.org/pdf/2307.13617v2.pdf,2023-07-25,"['cs.cl', 'cs.ai']",2307.13617v2.pdf," Financial analysis is an important tool for evaluating company performance. +Practitioners work to answer financial questions to make profitable investment +decisions, and use advanced quantitative analyses to do so. As a result, +Financial Question Answering (QA) is a question answering task that requires +deep reasoning about numbers. Furthermore, it is unknown how well pre-trained +language models can reason in the financial domain. The current +state-of-the-art requires a retriever to collect relevant facts about the +financial question from the text and a generator to produce a valid financial +program and a final answer. However, recently large language models like GPT-3 +have achieved state-of-the-art performance on wide variety of tasks with just a +few shot examples. We run several experiments with GPT-3 and find that a +separate retrieval model and logic engine continue to be essential components +to achieving SOTA performance in this task, particularly due to the precise +nature of financial questions and the complex information stored in financial +documents. With this understanding, our refined prompt-engineering approach on +GPT-3 achieves near SOTA accuracy without any fine-tuning. +" +S3: Social-network Simulation System with Large Language Model-Empowered Agents,Chen Gao,http://arxiv.org/pdf/2307.14984v2.pdf,2023-07-27,['cs.si'],2307.14984v2.pdf," Social network simulation plays a crucial role in addressing various +challenges within social science. It offers extensive applications such as +state prediction, phenomena explanation, and policy-making support, among +others. In this work, we harness the formidable human-like capabilities +exhibited by large language models (LLMs) in sensing, reasoning, and behaving, +and utilize these qualities to construct the S$^3$ system (short for +$\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$ystem). Adhering to +the widely employed agent-based simulation paradigm, we employ prompt +engineering and prompt tuning techniques to ensure that the agent's behavior +closely emulates that of a genuine human within the social network. +Specifically, we simulate three pivotal aspects: emotion, attitude, and +interaction behaviors. By endowing the agent in the system with the ability to +perceive the informational environment and emulate human actions, we observe +the emergence of population-level phenomena, including the propagation of +information, attitudes, and emotions. We conduct an evaluation encompassing two +levels of simulation, employing real-world social network data. Encouragingly, +the results demonstrate promising accuracy. This work represents an initial +step in the realm of social network simulation empowered by LLM-based agents. +We anticipate that our endeavors will serve as a source of inspiration for the +development of simulation systems within, but not limited to, social science. +" +Flows: Building Blocks of Reasoning and Collaborating AI,Martin Josifoski,http://arxiv.org/pdf/2308.01285v1.pdf,2023-08-02,"['cs.ai', 'cs.hc']",2308.01285v1.pdf," Recent advances in artificial intelligence (AI) have produced highly capable +and controllable systems. This creates unprecedented opportunities for +structured reasoning as well as collaboration among multiple AI systems and +humans. To fully realize this potential, it is essential to develop a +principled way of designing and studying such structured interactions. For this +purpose, we introduce the conceptual framework of Flows: a systematic approach +to modeling complex interactions. Flows are self-contained building blocks of +computation, with an isolated state, communicating through a standardized +message-based interface. This modular design allows Flows to be recursively +composed into arbitrarily nested interactions, with a substantial reduction of +complexity. Crucially, any interaction can be implemented using this framework, +including prior work on AI--AI and human--AI interactions, prompt engineering +schemes, and tool augmentation. We demonstrate the potential of Flows on the +task of competitive coding, a challenging task on which even GPT-4 struggles. +Our results suggest that structured reasoning and collaboration substantially +improve generalization, with AI-only Flows adding +$21$ and human--AI Flows +adding +$54$ absolute points in terms of solve rate. To support rapid and +rigorous research, we introduce the aiFlows library. The library comes with a +repository of Flows that can be easily used, extended, and composed into novel, +more complex Flows. + The aiFlows library is available at https://github.com/epfl-dlab/aiflows. +Data and Flows for reproducing our experiments are available at +https://github.com/epfl-dlab/cc_flows. +" +Evaluating ChatGPT text-mining of clinical records for obesity monitoring,Ivo S. Fins,http://arxiv.org/pdf/2308.01666v1.pdf,2023-08-03,"['cs.ir', 'cs.cl']",2308.01666v1.pdf," Background: Veterinary clinical narratives remain a largely untapped resource +for addressing complex diseases. Here we compare the ability of a large +language model (ChatGPT) and a previously developed regular expression (RegexT) +to identify overweight body condition scores (BCS) in veterinary narratives. +Methods: BCS values were extracted from 4,415 anonymised clinical narratives +using either RegexT or by appending the narrative to a prompt sent to ChatGPT +coercing the model to return the BCS information. Data were manually reviewed +for comparison. Results: The precision of RegexT was higher (100%, 95% CI +94.81-100%) than the ChatGPT (89.3%; 95% CI82.75-93.64%). However, the recall +of ChatGPT (100%. 95% CI 96.18-100%) was considerably higher than that of +RegexT (72.6%, 95% CI 63.92-79.94%). Limitations: Subtle prompt engineering is +needed to improve ChatGPT output. Conclusions: Large language models create +diverse opportunities and, whilst complex, present an intuitive interface to +information but require careful implementation to avoid unpredictable errors. +" +ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP,Lu Yan,http://arxiv.org/pdf/2308.02122v2.pdf,2023-08-04,"['cs.cr', 'cs.cl']",2308.02122v2.pdf," Backdoor attacks have emerged as a prominent threat to natural language +processing (NLP) models, where the presence of specific triggers in the input +can lead poisoned models to misclassify these inputs to predetermined target +classes. Current detection mechanisms are limited by their inability to address +more covert backdoor strategies, such as style-based attacks. In this work, we +propose an innovative test-time poisoned sample detection framework that hinges +on the interpretability of model predictions, grounded in the semantic meaning +of inputs. We contend that triggers (e.g., infrequent words) are not supposed +to fundamentally alter the underlying semantic meanings of poisoned samples as +they want to stay stealthy. Based on this observation, we hypothesize that +while the model's predictions for paraphrased clean samples should remain +stable, predictions for poisoned samples should revert to their true labels +upon the mutations applied to triggers during the paraphrasing process. We +employ ChatGPT, a state-of-the-art large language model, as our paraphraser and +formulate the trigger-removal task as a prompt engineering problem. We adopt +fuzzing, a technique commonly used for unearthing software vulnerabilities, to +discover optimal paraphrase prompts that can effectively eliminate triggers +while concurrently maintaining input semantics. Experiments on 4 types of +backdoor attacks, including the subtle style backdoors, and 4 distinct datasets +demonstrate that our approach surpasses baseline methods, including STRIP, RAP, +and ONION, in precision and recall. +" +IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models,Hu Ye,http://arxiv.org/pdf/2308.06721v1.pdf,2023-08-13,"['cs.cv', 'cs.ai']",2308.06721v1.pdf," Recent years have witnessed the strong power of large text-to-image diffusion +models for the impressive generative capability to create high-fidelity images. +However, it is very tricky to generate desired images using only text prompt as +it often involves complex prompt engineering. An alternative to text prompt is +image prompt, as the saying goes: ""an image is worth a thousand words"". +Although existing methods of direct fine-tuning from pretrained models are +effective, they require large computing resources and are not compatible with +other base models, text prompt, and structural controls. In this paper, we +present IP-Adapter, an effective and lightweight adapter to achieve image +prompt capability for the pretrained text-to-image diffusion models. The key +design of our IP-Adapter is decoupled cross-attention mechanism that separates +cross-attention layers for text features and image features. Despite the +simplicity of our method, an IP-Adapter with only 22M parameters can achieve +comparable or even better performance to a fully fine-tuned image prompt model. +As we freeze the pretrained diffusion model, the proposed IP-Adapter can be +generalized not only to other custom models fine-tuned from the same base +model, but also to controllable generation using existing controllable tools. +With the benefit of the decoupled cross-attention strategy, the image prompt +can also work well with the text prompt to achieve multimodal image generation. +The project page is available at \url{https://ip-adapter.github.io}. +" +LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis,Yilun Liu,http://arxiv.org/pdf/2308.07610v1.pdf,2023-08-15,"['cs.se', 'cs.cl']",2308.07610v1.pdf," Automated log analysis is crucial in modern software-intensive systems for +ensuring reliability and resilience throughout software maintenance and +engineering life cycles. Existing methods perform tasks such as log parsing and +log anomaly detection by providing a single prediction value without +interpretation. However, given the increasing volume of system events, the +limited interpretability of analysis results hinders analysts' trust and their +ability to take appropriate actions. Moreover, these methods require +substantial in-domain training data, and their performance declines sharply (by +up to 62.5%) in online scenarios involving unseen logs from new domains, a +common occurrence due to rapid software updates. In this paper, we propose +LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt +employs large language models (LLMs) to perform zero-shot log analysis tasks +via a suite of advanced prompt strategies tailored for log tasks, which +enhances LLMs' performance by up to 107.5% compared with simple prompts. +Experiments on nine publicly available evaluation datasets across two tasks +demonstrate that LogPrompt, despite using no training data, outperforms +existing approaches trained on thousands of logs by up to around 50%. We also +conduct a human evaluation of LogPrompt's interpretability, with six +practitioners possessing over 10 years of experience, who highly rated the +generated content in terms of usefulness and readability (averagely 4.42/5). +LogPrompt also exhibits remarkable compatibility with open-source and +smaller-scale LLMs, making it flexible for practical deployment. +" +Transforming Sentiment Analysis in the Financial Domain with ChatGPT,Georgios Fatouros,http://arxiv.org/pdf/2308.07935v1.pdf,2023-08-13,"['cs.cl', 'cs.ai', 'cs.ce', 'cs.ir', '68t01, 68t50, 91b28, 91b30']",2308.07935v1.pdf," Financial sentiment analysis plays a crucial role in decoding market trends +and guiding strategic trading decisions. Despite the deployment of advanced +deep learning techniques and language models to refine sentiment analysis in +finance, this study breaks new ground by investigating the potential of large +language models, particularly ChatGPT 3.5, in financial sentiment analysis, +with a strong emphasis on the foreign exchange market (forex). Employing a +zero-shot prompting approach, we examine multiple ChatGPT prompts on a +meticulously curated dataset of forex-related news headlines, measuring +performance using metrics such as precision, recall, f1-score, and Mean +Absolute Error (MAE) of the sentiment class. Additionally, we probe the +correlation between predicted sentiment and market returns as an additional +evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment +analysis model for financial texts, exhibited approximately 35\% enhanced +performance in sentiment classification and a 36\% higher correlation with +market returns. By underlining the significance of prompt engineering, +particularly in zero-shot contexts, this study spotlights ChatGPT's potential +to substantially boost sentiment analysis in financial applications. By sharing +the utilized dataset, our intention is to stimulate further research and +advancements in the field of financial services. +" +ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT,Fatemeh Nazary,http://arxiv.org/pdf/2308.09731v1.pdf,2023-08-17,"['cs.ai', 'cs.cl', 'cs.lg']",2308.09731v1.pdf," This study presents an innovative approach to the application of large +language models (LLMs) in clinical decision-making, focusing on OpenAI's +ChatGPT. Our approach introduces the use of contextual prompts-strategically +designed to include task description, feature description, and crucially, +integration of domain knowledge-for high-quality binary classification tasks +even in data-scarce scenarios. The novelty of our work lies in the utilization +of domain knowledge, obtained from high-performing interpretable ML models, and +its seamless incorporation into prompt design. By viewing these ML models as +medical experts, we extract key insights on feature importance to aid in +decision-making processes. This interplay of domain knowledge and AI holds +significant promise in creating a more insightful diagnostic tool. + Additionally, our research explores the dynamics of zero-shot and few-shot +prompt learning based on LLMs. By comparing the performance of OpenAI's ChatGPT +with traditional supervised ML models in different data conditions, we aim to +provide insights into the effectiveness of prompt engineering strategies under +varied data availability. In essence, this paper bridges the gap between AI and +healthcare, proposing a novel methodology for LLMs application in clinical +decision support systems. It highlights the transformative potential of +effective prompt design, domain knowledge integration, and flexible learning +approaches in enhancing automated decision-making. +" +Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis,Oscar J. Romero,http://arxiv.org/pdf/2308.09830v3.pdf,2023-08-18,['cs.ai'],2308.09830v3.pdf," This paper explores the integration of two AI subdisciplines employed in the +development of artificial agents that exhibit intelligent behavior: Large +Language Models (LLMs) and Cognitive Architectures (CAs). We present three +integration approaches, each grounded in theoretical models and supported by +preliminary empirical evidence. The modular approach, which introduces four +models with varying degrees of integration, makes use of chain-of-thought +prompting, and draws inspiration from augmented LLMs, the Common Model of +Cognition, and the simulation theory of cognition. The agency approach, +motivated by the Society of Mind theory and the LIDA cognitive architecture, +proposes the formation of agent collections that interact at micro and macro +cognitive levels, driven by either LLMs or symbolic components. The +neuro-symbolic approach, which takes inspiration from the CLARION cognitive +architecture, proposes a model where bottom-up learning extracts symbolic +representations from an LLM layer and top-down guidance utilizes symbolic +representations to direct prompt engineering in the LLM layer. These approaches +aim to harness the strengths of both LLMs and CAs, while mitigating their +weaknesses, thereby advancing the development of more robust AI systems. We +discuss the tradeoffs and challenges associated with each approach. +" +Manipulating Embeddings of Stable Diffusion Prompts,Niklas Deckers,http://arxiv.org/pdf/2308.12059v1.pdf,2023-08-23,"['cs.cv', 'cs.lg']",2308.12059v1.pdf," Generative text-to-image models such as Stable Diffusion allow users to +generate images based on a textual description, the prompt. Changing the prompt +is still the primary means for the user to change a generated image as desired. +However, changing the image by reformulating the prompt remains a difficult +process of trial and error, which has led to the emergence of prompt +engineering as a new field of research. We propose and analyze methods to +change the embedding of a prompt directly instead of the prompt text. It allows +for more fine-grained and targeted control that takes into account user +intentions. Our approach treats the generative text-to-image model as a +continuous function and passes gradients between the image space and the prompt +embedding space. By addressing different user interaction problems, we can +apply this idea in three scenarios: (1) Optimization of a metric defined in +image space that could measure, for example, image style. (2) Assistance of +users in creative tasks by enabling them to navigate the image space along a +selection of directions of ""near"" prompt embeddings. (3) Changing the embedding +of the prompt to include information that the user has seen in a particular +seed but finds difficult to describe in the prompt. Our experiments demonstrate +the feasibility of the described methods. +" +Large Language Models in Fault Localisation,Yonghao Wu,http://arxiv.org/pdf/2308.15276v3.pdf,2023-08-29,['cs.se'],2308.15276v3.pdf," Large Language Models (LLMs) have shown promise in multiple software +engineering tasks including code generation, program repair, code +summarisation, and test generation. Fault localisation is instrumental in +enabling automated debugging and repair of programs and was prominently +featured as a highlight during the launch event of ChatGPT-4. Nevertheless, the +performance of LLMs compared to state-of-the-art methods, as well as the impact +of prompt design and context length on their efficacy, remains unclear. To fill +this gap, this paper presents an in-depth investigation into the capability of +ChatGPT-3.5 and ChatGPT-4, the two state-of-the-art LLMs, on fault +localisation. Using the widely-adopted large-scale Defects4J dataset, we +compare the two LLMs with the existing fault localisation techniques. We also +investigate the consistency of LLMs in fault localisation, as well as how +prompt engineering and the length of code context affect the fault localisation +effectiveness. + Our findings demonstrate that within function-level context, ChatGPT-4 +outperforms all the existing fault localisation methods. Additional error logs +can further improve ChatGPT models' localisation accuracy and consistency, with +an average 46.9% higher accuracy over the state-of-the-art baseline SmartFL on +the Defects4J dataset in terms of TOP-1 metric. However, when the code context +of the Defects4J dataset expands to the class-level, ChatGPT-4's performance +suffers a significant drop, with 49.9% lower accuracy than SmartFL under TOP-1 +metric. These observations indicate that although ChatGPT can effectively +localise faults under specific conditions, limitations are evident. Further +research is needed to fully harness the potential of LLMs like ChatGPT for +practical fault localisation applications. +" +Leveraging Large Language Models for Exploiting ASR Uncertainty,Pranay Dighe,http://arxiv.org/pdf/2309.04842v2.pdf,2023-09-09,"['cs.cl', 'cs.hc', 'cs.sd', 'eess.as']",2309.04842v2.pdf," While large language models excel in a variety of natural language processing +(NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they +must either rely on off-the-shelf automatic speech recognition (ASR) systems +for transcription, or be equipped with an in-built speech modality. This work +focuses on the former scenario, where LLM's accuracy on SLU tasks is +constrained by the accuracy of a fixed ASR system on the spoken input. +Specifically, we tackle speech-intent classification task, where a high +word-error-rate can limit the LLM's ability to understand the spoken intent. +Instead of chasing a high accuracy by designing complex or specialized +architectures regardless of deployment costs, we seek to answer how far we can +go without substantially changing the underlying ASR and LLM, which can +potentially be shared by multiple unrelated tasks. To this end, we propose +prompting the LLM with an n-best list of ASR hypotheses instead of only the +error-prone 1-best hypothesis. We explore prompt-engineering to explain the +concept of n-best lists to the LLM; followed by the finetuning of Low-Rank +Adapters on the downstream tasks. Our approach using n-best lists proves to be +effective on a device-directed speech detection task as well as on a keyword +spotting task, where systems using n-best list prompts outperform those using +1-best ASR hypothesis; thus paving the way for an efficient method to exploit +ASR uncertainty via LLMs for speech-based applications. +" +Unveiling the potential of large language models in generating semantic and cross-language clones,Palash R. Roy,http://arxiv.org/pdf/2309.06424v1.pdf,2023-09-12,"['cs.se', 'cs.ai', 'cs.lg']",2309.06424v1.pdf," Semantic and Cross-language code clone generation may be useful for code +reuse, code comprehension, refactoring and benchmarking. OpenAI's GPT model has +potential in such clone generation as GPT is used for text generation. When +developers copy/paste codes from Stack Overflow (SO) or within a system, there +might be inconsistent changes leading to unexpected behaviours. Similarly, if +someone possesses a code snippet in a particular programming language but seeks +equivalent functionality in a different language, a semantic cross-language +code clone generation approach could provide valuable assistance. In this +study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3 +model could help generate semantic and cross-language clone variants for a +given fragment.We have comprised a diverse set of code fragments and assessed +GPT-3s performance in generating code variants.Through extensive +experimentation and analysis, where 9 judges spent 158 hours to validate, we +investigate the model's ability to produce accurate and semantically correct +variants. Our findings shed light on GPT-3's strengths in code generation, +offering insights into the potential applications and challenges of using +advanced language models in software development. Our quantitative analysis +yields compelling results. In the realm of semantic clones, GPT-3 attains an +impressive accuracy of 62.14% and 0.55 BLEU score, achieved through few-shot +prompt engineering. Furthermore, the model shines in transcending linguistic +confines, boasting an exceptional 91.25% accuracy in generating cross-language +clones +" +Is GPT4 a Good Trader?,Bingzhe Wu,http://arxiv.org/pdf/2309.10982v1.pdf,2023-09-20,['cs.ai'],2309.10982v1.pdf," Recently, large language models (LLMs), particularly GPT-4, have demonstrated +significant capabilities in various planning and reasoning tasks +\cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there +has been a surge of interest among researchers to harness the capabilities of +GPT-4 for the automated design of quantitative factors that do not overlap with +existing factor libraries, with an aspiration to achieve alpha returns +\cite{webpagequant}. In contrast to these work, this study aims to examine the +fidelity of GPT-4's comprehension of classic trading theories and its +proficiency in applying its code interpreter abilities to real-world trading +data analysis. Such an exploration is instrumental in discerning whether the +underlying logic GPT-4 employs for trading is intrinsically reliable. +Furthermore, given the acknowledged interpretative latitude inherent in most +trading theories, we seek to distill more precise methodologies of deploying +these theories from GPT-4's analytical process, potentially offering invaluable +insights to human traders. + To achieve this objective, we selected daily candlestick (K-line) data from +specific periods for certain assets, such as the Shanghai Stock Index. Through +meticulous prompt engineering, we guided GPT-4 to analyze the technical +structures embedded within this data, based on specific theories like the +Elliott Wave Theory. We then subjected its analytical output to manual +evaluation, assessing its interpretative depth and accuracy vis-\`a-vis these +trading theories from multiple dimensions. The results and findings from this +study could pave the way for a synergistic amalgamation of human expertise and +AI-driven insights in the realm of trading. +" +AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling,Pivithuru Thejan Amarasinghe,http://arxiv.org/pdf/2309.13218v3.pdf,2023-09-22,['cs.ai'],2309.13218v3.pdf," Business optimisation refers to the process of finding and implementing +efficient and cost-effective means of operation to bring a competitive +advantage for businesses. Synthesizing problem formulations is an integral part +of business optimisation, which relies on human expertise to construct problem +formulations using optimisation languages. Interestingly, with advancements in +Large Language Models (LLMs), the human expertise needed in problem formulation +can be minimized. However, developing an LLM for problem formulation is +challenging, due to training data, token limitations, and lack of appropriate +performance metrics. For the requirement of training data, recent attention has +been directed towards fine-tuning pre-trained LLMs for downstream tasks rather +than training an LLM from scratch for a specific task. In this paper, we adopt +an LLM fine-tuning approach and propose an AI-Copilot for business optimisation +problem formulation. For token limitations, we introduce modularization and +prompt engineering techniques to synthesize complex problem formulations as +modules that fit into the token limits of LLMs. Additionally, we design +performance evaluation metrics that are better suited for assessing the +accuracy and quality of problem formulations. The experiment results +demonstrate that with this approach we can synthesize complex and large problem +formulations for a typical business optimisation problem in production +scheduling. +" +An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems,Andreas Metzger,http://arxiv.org/pdf/2309.14391v1.pdf,2023-09-25,"['cs.lg', 'cs.ai', 'cs.cl']",2309.14391v1.pdf," Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the +open-world assumption in service-oriented systems. Deep RL was successfully +applied to problems such as dynamic service composition, job scheduling, and +offloading, as well as service adaptation. While Deep RL offers many benefits, +understanding the decision-making of Deep RL is challenging because its learned +decision-making policy essentially appears as a black box. Yet, understanding +the decision-making of Deep RL is key to help service developers perform +debugging, support service providers to comply with relevant legal frameworks, +and facilitate service users to build trust. We introduce Chat4XAI to +facilitate the understanding of the decision-making of Deep RL by providing +natural-language explanations. Compared with visual explanations, the reported +benefits of natural-language explanations include better understandability for +non-technical users, increased user acceptance and trust, as well as more +efficient explanations. Chat4XAI leverages modern AI chatbot technology and +dedicated prompt engineering. Compared to earlier work on natural-language +explanations using classical software-based dialogue systems, using an AI +chatbot eliminates the need for eliciting and defining potential questions and +answers up-front. We prototypically realize Chat4XAI using OpenAI's ChatGPT API +and evaluate the fidelity and stability of its explanations using an adaptive +service exemplar. +" +Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering,Han Zhou,http://arxiv.org/pdf/2309.17249v1.pdf,2023-09-29,"['cs.cl', 'cs.ai', 'cs.lg']",2309.17249v1.pdf," Prompting and in-context learning (ICL) have become efficient learning +paradigms for large language models (LLMs). However, LLMs suffer from prompt +brittleness and various bias factors in the prompt, including but not limited +to the formatting, the choice verbalizers, and the ICL examples. To address +this problem that results in unexpected performance degradation, calibration +methods have been developed to mitigate the effects of these biases while +recovering LLM performance. In this work, we first conduct a systematic +analysis of the existing calibration methods, where we both provide a unified +view and reveal the failure cases. Inspired by these analyses, we propose Batch +Calibration (BC), a simple yet intuitive method that controls the contextual +bias from the batched input, unifies various prior approaches, and effectively +addresses the aforementioned issues. BC is zero-shot, inference-only, and +incurs negligible additional costs. In the few-shot setup, we further extend BC +to allow it to learn the contextual bias from labeled data. We validate the +effectiveness of BC with PaLM 2-(S, M, L) and CLIP models and demonstrate +state-of-the-art performance over previous calibration baselines across more +than 10 natural language understanding and image classification tasks. +" +Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4,Jiaxian Guo,http://arxiv.org/pdf/2309.17277v2.pdf,2023-09-29,['cs.ai'],2309.17277v2.pdf," Unlike perfect information games, where all elements are known to every +player, imperfect information games emulate the real-world complexities of +decision-making under uncertain or incomplete information. GPT-4, the recent +breakthrough in large language models (LLMs) trained on massive passive data, +is notable for its knowledge retrieval and reasoning abilities. This paper +delves into the applicability of GPT-4's learned knowledge for imperfect +information games. To achieve this, we introduce \textbf{Suspicion-Agent}, an +innovative agent that leverages GPT-4's capabilities for performing in +imperfect information games. With proper prompt engineering to achieve +different functions, Suspicion-Agent based on GPT-4 demonstrates remarkable +adaptability across a range of imperfect information card games. Importantly, +GPT-4 displays a strong high-order theory of mind (ToM) capacity, meaning it +can understand others and intentionally impact others' behavior. Leveraging +this, we design a planning strategy that enables GPT-4 to competently play +against different opponents, adapting its gameplay style as needed, while +requiring only the game rules and descriptions of observations as input. In the +experiments, we qualitatively showcase the capabilities of Suspicion-Agent +across three different imperfect information games and then quantitatively +evaluate it in Leduc Hold'em. The results show that Suspicion-Agent can +potentially outperform traditional algorithms designed for imperfect +information games, without any specialized training or examples. In order to +encourage and foster deeper insights within the community, we make our +game-related data publicly available. +" +Investigating the Limitation of CLIP Models: The Worst-Performing Categories,Jie-Jing Shao,http://arxiv.org/pdf/2310.03324v1.pdf,2023-10-05,"['cs.cv', 'cs.lg']",2310.03324v1.pdf," Contrastive Language-Image Pre-training (CLIP) provides a foundation model by +integrating natural language into visual concepts, enabling zero-shot +recognition on downstream tasks. It is usually expected that satisfactory +overall accuracy can be achieved across numerous domains through well-designed +textual prompts. However, we found that their performance in the worst +categories is significantly inferior to the overall performance. For example, +on ImageNet, there are a total of 10 categories with class-wise accuracy as low +as 0\%, even though the overall performance has achieved 64.1\%. This +phenomenon reveals the potential risks associated with using CLIP models, +particularly in risk-sensitive applications where specific categories hold +significant importance. To address this issue, we investigate the alignment +between the two modalities in the CLIP model and propose the Class-wise +Matching Margin (\cmm) to measure the inference confusion. \cmm\ can +effectively identify the worst-performing categories and estimate the potential +performance of the candidate prompts. We further query large language models to +enrich descriptions of worst-performing categories and build a weighted +ensemble to highlight the efficient prompts. Experimental results clearly +verify the effectiveness of our proposal, where the accuracy on the worst-10 +categories on ImageNet is boosted to 5.2\%, without manual prompt engineering, +laborious optimization, or access to labeled validation data. +" +Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models,Junchi Yu,http://arxiv.org/pdf/2310.03965v2.pdf,2023-10-06,"['cs.ai', 'cs.cl']",2310.03965v2.pdf," Large Language Models (LLMs) have achieved remarkable success in reasoning +tasks with the development of prompting methods. However, existing prompting +approaches cannot reuse insights of solving similar problems and suffer from +accumulated errors in multi-step reasoning, since they prompt LLMs to reason +\textit{from scratch}. To address these issues, we propose +\textbf{\textit{Thought Propagation} (TP)}, which explores the analogous +problems and leverages their solutions to enhance the complex reasoning ability +of LLMs. These analogous problems are related to the input one, with reusable +solutions and problem-solving strategies. Thus, it is promising to propagate +insights of solving previous analogous problems to inspire new problem-solving. +To achieve this, TP first prompts LLMs to propose and solve a set of analogous +problems that are related to the input one. Then, TP reuses the results of +analogous problems to directly yield a new solution or derive a +knowledge-intensive plan for execution to amend the initial solution obtained +from scratch. TP is compatible with existing prompting approaches, allowing +plug-and-play generalization and enhancement in a wide range of tasks without +much labor in task-specific prompt engineering. Experiments across three +challenging tasks demonstrate TP enjoys a substantial improvement over the +baselines by an average of 12\% absolute increase in finding the optimal +solutions in Shortest-path Reasoning, 13\% improvement of human preference in +Creative Writing, and 15\% enhancement in the task completion rate of LLM-Agent +Planning. +" +JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressions,Detai Xin,http://arxiv.org/pdf/2310.06072v1.pdf,2023-10-09,"['cs.sd', 'eess.as']",2310.06072v1.pdf," We present the JVNV, a Japanese emotional speech corpus with verbal content +and nonverbal vocalizations whose scripts are generated by a large-scale +language model. Existing emotional speech corpora lack not only proper +emotional scripts but also nonverbal vocalizations (NVs) that are essential +expressions in spoken language to express emotions. We propose an automatic +script generation method to produce emotional scripts by providing seed words +with sentiment polarity and phrases of nonverbal vocalizations to ChatGPT using +prompt engineering. We select 514 scripts with balanced phoneme coverage from +the generated candidate scripts with the assistance of emotion confidence +scores and language fluency scores. We demonstrate the effectiveness of JVNV by +showing that JVNV has better phoneme coverage and emotion recognizability than +previous Japanese emotional speech corpora. We then benchmark JVNV on emotional +text-to-speech synthesis using discrete codes to represent NVs. We show that +there still exists a gap between the performance of synthesizing read-aloud +speech and emotional speech, and adding NVs in the speech makes the task even +harder, which brings new challenges for this task and makes JVNV a valuable +resource for relevant works in the future. To our best knowledge, JVNV is the +first speech corpus that generates scripts automatically using large language +models. +" +Large Language Model-Empowered Agents for Simulating Macroeconomic Activities,Nian Li,http://arxiv.org/pdf/2310.10436v1.pdf,2023-10-16,['cs.ai'],2310.10436v1.pdf," The advent of the Web has brought about a paradigm shift in traditional +economics, particularly in the digital economy era, enabling the precise +recording and analysis of individual economic behavior. This has led to a +growing emphasis on data-driven modeling in macroeconomics. In macroeconomic +research, Agent-based modeling (ABM) emerged as an alternative, evolving +through rule-based agents, machine learning-enhanced decision-making, and, more +recently, advanced AI agents. However, the existing works are suffering from +three main challenges when endowing agents with human-like decision-making, +including agent heterogeneity, the influence of macroeconomic trends, and +multifaceted economic factors. Large language models (LLMs) have recently +gained prominence in offering autonomous human-like characteristics. Therefore, +leveraging LLMs in macroeconomic simulation presents an opportunity to overcome +traditional limitations. In this work, we take an early step in introducing a +novel approach that leverages LLMs in macroeconomic simulation. We design +prompt-engineering-driven LLM agents to exhibit human-like decision-making and +adaptability in the economic environment, with the abilities of perception, +reflection, and decision-making to address the abovementioned challenges. +Simulation experiments on macroeconomic activities show that LLM-empowered +agents can make realistic work and consumption decisions and emerge more +reasonable macroeconomic phenomena than existing rule-based or AI agents. Our +work demonstrates the promising potential to simulate macroeconomics based on +LLM and its human-like characteristics. +" +Large Language Model for Multi-objective Evolutionary Optimization,Fei Liu,http://arxiv.org/pdf/2310.12541v2.pdf,2023-10-19,"['cs.ne', 'cs.ai', 'cs.cl', 'cs.et']",2310.12541v2.pdf," Multiobjective evolutionary algorithms (MOEAs) are major methods for solving +multiobjective optimization problems (MOPs). Many MOEAs have been proposed in +the past decades, of which the search operators need a carefully handcrafted +design with domain knowledge. Recently, some attempts have been made to replace +the manually designed operators in MOEAs with learning-based operators (e.g., +neural network models). However, much effort is still required for designing +and training such models, and the learned operators might not generalize well +on new problems. To tackle the above challenges, this work investigates a novel +approach that leverages the powerful large language model (LLM) to design MOEA +operators. With proper prompt engineering, we successfully let a general LLM +serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a +zero-shot manner. In addition, by learning from the LLM behavior, we further +design an explicit white-box operator with randomness and propose a new version +of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on +different test benchmarks show that our proposed method can achieve competitive +performance with widely used MOEAs. It is also promising to see the operator +only learned from a few instances can have robust generalization performance on +unseen problems with quite different patterns and settings. The results reveal +the potential benefits of using pre-trained LLMs in the design of MOEAs. +" +Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning,Juan Rocamonde,http://arxiv.org/pdf/2310.12921v1.pdf,2023-10-19,"['cs.lg', 'cs.ai']",2310.12921v1.pdf," Reinforcement learning (RL) requires either manually specifying a reward +function, which is often infeasible, or learning a reward model from a large +amount of human feedback, which is often very expensive. We study a more +sample-efficient alternative: using pretrained vision-language models (VLMs) as +zero-shot reward models (RMs) to specify tasks via natural language. We propose +a natural and general approach to using VLMs as reward models, which we call +VLM-RMs. We use VLM-RMs based on CLIP to train a MuJoCo humanoid to learn +complex tasks without a manually specified reward function, such as kneeling, +doing the splits, and sitting in a lotus position. For each of these tasks, we +only provide a single sentence text prompt describing the desired task with +minimal prompt engineering. We provide videos of the trained agents at: +https://sites.google.com/view/vlm-rm. We can improve performance by providing a +second ``baseline'' prompt and projecting out parts of the CLIP embedding space +irrelevant to distinguish between goal and baseline. Further, we find a strong +scaling effect for VLM-RMs: larger VLMs trained with more compute and data are +better reward models. The failure modes of VLM-RMs we encountered are all +related to known capability limitations of current VLMs, such as limited +spatial reasoning ability or visually unrealistic environments that are far +off-distribution for the VLM. We find that VLM-RMs are remarkably robust as +long as the VLM is large enough. This suggests that future VLMs will become +more and more useful reward models for a wide range of RL applications. +" +Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering,Rahman S M Wahidur,http://arxiv.org/pdf/2310.13226v1.pdf,2023-10-20,['cs.cl'],2310.13226v1.pdf," Blockchain technology has revolutionized the financial landscape, with +cryptocurrencies gaining widespread adoption for their decentralized and +transparent nature. As the sentiment expressed on social media platforms can +significantly influence cryptocurrency discussions and market movements, +sentiment analysis has emerged as a crucial tool for understanding public +opinion and predicting market trends. Motivated by the aim to enhance sentiment +analysis accuracy in the cryptocurrency domain, this paper investigates +fine-tuning techniques on large language models. This paper also investigates +the efficacy of supervised fine-tuning and instruction-based fine-tuning on +large language models for unseen tasks. Experimental results demonstrate a +significant average zero-shot performance gain of 40% after fine-tuning, +highlighting the potential of this technique in optimizing pre-trained language +model efficiency. Additionally, the impact of instruction tuning on models of +varying scales is examined, revealing that larger models benefit from +instruction tuning, achieving the highest average accuracy score of 75.16%. In +contrast, smaller-scale models may experience reduced generalization due to the +complete utilization of model capacity. To gain deeper insight about how +instruction works with these language models, this paper presents an +experimental investigation into the response of an instruction-based model +under different instruction tuning setups. The investigation demonstrates that +the model achieves an average accuracy score of 72.38% for short and simple +instructions. This performance significantly outperforms its accuracy under +long and complex instructions by over 12%, thereby effectively highlighting the +profound significance of instruction characteristics in maximizing model +performance. +" +Can LLMs Grade Short-answer Reading Comprehension Questions : Foundational Literacy Assessment in LMICs,Owen Henkel,http://arxiv.org/pdf/2310.18373v1.pdf,2023-10-26,"['cs.cl', 'cs.ai']",2310.18373v1.pdf," This paper presents emerging evidence of using generative large language +models (i.e., GPT-4) to reliably evaluate short-answer reading comprehension +questions. Specifically, we explore how various configurations of generative +(LLMs) are able to evaluate student responses from a new dataset, drawn from a +battery of reading assessments conducted with over 150 students in Ghana. As +this dataset is novel and hence not used in training runs of GPT, it offers an +opportunity to test for domain shift and evaluate the generalizability of +generative LLMs, which are predominantly designed and trained on data from +high-income North American countries. We found that GPT-4, with minimal prompt +engineering performed extremely well on evaluating the novel dataset (Quadratic +Weighted Kappa 0.923, F1 0.88), substantially outperforming transfer-learning +based approaches, and even exceeding expert human raters (Quadratic Weighted +Kappa 0.915, F1 0.87). To the best of our knowledge, our work is the first to +empirically evaluate the performance of generative LLMs on short-answer reading +comprehension questions, using real student data, and suggests that generative +LLMs have the potential to reliably evaluate foundational literacy. Currently +the assessment of formative literacy and numeracy is infrequent in many low and +middle-income countries (LMICs) due to the cost and operational complexities of +conducting them at scale. Automating the grading process for reading assessment +could enable wider usage, and in turn improve decision-making regarding +curricula, school management, and teaching practice at the classroom level. +Importantly, in contrast transfer learning based approaches, generative LLMs +generalize well and the technical barriers to their use are low, making them +more feasible to implement and scale in lower resource educational contexts. +" +Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models,Hao Li,http://arxiv.org/pdf/2310.19721v2.pdf,2023-10-30,"['eess.iv', 'cs.cv']",2310.19721v2.pdf," To address prevalent issues in medical imaging, such as data acquisition +challenges and label availability, transfer learning from natural to medical +image domains serves as a viable strategy to produce reliable segmentation +results. However, several existing barriers between domains need to be broken +down, including addressing contrast discrepancies, managing anatomical +variability, and adapting 2D pretrained models for 3D segmentation tasks. In +this paper, we propose ProMISe,a prompt-driven 3D medical image segmentation +model using only a single point prompt to leverage knowledge from a pretrained +2D image foundation model. In particular, we use the pretrained vision +transformer from the Segment Anything Model (SAM) and integrate lightweight +adapters to extract depth-related (3D) spatial context without updating the +pretrained weights. For robust results, a hybrid network with complementary +encoders is designed, and a boundary-aware loss is proposed to achieve precise +boundaries. We evaluate our model on two public datasets for colon and pancreas +tumor segmentations, respectively. Compared to the state-of-the-art +segmentation methods with and without prompt engineering, our proposed method +achieves superior performance. The code is publicly available at +https://github.com/MedICL-VU/ProMISe. +" +Making Large Language Models Better Data Creators,Dong-Ho Lee,http://arxiv.org/pdf/2310.20111v1.pdf,2023-10-31,['cs.cl'],2310.20111v1.pdf," Although large language models (LLMs) have advanced the state-of-the-art in +NLP significantly, deploying them for downstream applications is still +challenging due to cost, responsiveness, control, or concerns around privacy +and security. As such, trainable models are still the preferred option in some +cases. However, these models still require human-labeled data for optimal +performance, which is expensive and time-consuming to obtain. In order to +address this issue, several techniques to reduce human effort involve labeling +or generating data using LLMs. Although these methods are effective for certain +applications, in practice they encounter difficulties in real-world scenarios. +Labeling data requires careful data selection, while generating data +necessitates task-specific prompt engineering. In this paper, we propose a +unified data creation pipeline that requires only a single formatting example, +and which is applicable to a broad range of tasks, including traditionally +problematic ones with semantically devoid label spaces. In our experiments we +demonstrate that instruction-following LLMs are highly cost-effective data +creators, and that models trained with these data exhibit performance better +than those trained with human-labeled data (by up to 17.5%) on +out-of-distribution evaluation, while maintaining comparable performance on +in-distribution tasks. These results have important implications for the +robustness of NLP systems deployed in the real-world. +" +VisPercep: A Vision-Language Approach to Enhance Visual Perception for People with Blindness and Low Vision,Yu Hao,http://arxiv.org/pdf/2310.20225v1.pdf,2023-10-31,"['cs.cv', 'cs.ai']",2310.20225v1.pdf," People with blindness and low vision (pBLV) encounter substantial challenges +when it comes to comprehensive scene recognition and precise object +identification in unfamiliar environments. Additionally, due to the vision +loss, pBLV have difficulty in accessing and identifying potential tripping +hazards on their own. In this paper, we present a pioneering approach that +leverages a large vision-language model to enhance visual perception for pBLV, +offering detailed and comprehensive descriptions of the surrounding +environments and providing warnings about the potential risks. Our method +begins by leveraging a large image tagging model (i.e., Recognize Anything +(RAM)) to identify all common objects present in the captured images. The +recognition results and user query are then integrated into a prompt, tailored +specifically for pBLV using prompt engineering. By combining the prompt and +input image, a large vision-language model (i.e., InstructBLIP) generates +detailed and comprehensive descriptions of the environment and identifies +potential risks in the environment by analyzing the environmental objects and +scenes, relevant to the prompt. We evaluate our approach through experiments +conducted on both indoor and outdoor datasets. Our results demonstrate that our +method is able to recognize objects accurately and provide insightful +descriptions and analysis of the environment for pBLV. +" +BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing,Jason Alan Fries,http://arxiv.org/pdf/2206.15076v1.pdf,2022-06-30,['cs.cl'],2206.15076v1.pdf," Training and evaluating language models increasingly requires the +construction of meta-datasets --diverse collections of curated data with clear +provenance. Natural language prompting has recently lead to improved zero-shot +generalization by transforming existing, supervised datasets into a diversity +of novel pretraining tasks, highlighting the benefits of meta-dataset curation. +While successful in general-domain text, translating these data-centric +approaches to biomedical language modeling remains challenging, as labeled +biomedical datasets are significantly underrepresented in popular data hubs. To +address this challenge, we introduce BigBIO a community library of 126+ +biomedical NLP datasets, currently covering 12 task categories and 10+ +languages. BigBIO facilitates reproducible meta-dataset curation via +programmatic access to datasets and their metadata, and is compatible with +current platforms for prompt engineering and end-to-end few/zero shot language +model evaluation. We discuss our process for task schema harmonization, data +auditing, contribution guidelines, and outline two illustrative use cases: +zero-shot evaluation of biomedical prompts and large-scale, multi-task +learning. BigBIO is an ongoing community effort and is available at +https://github.com/bigscience-workshop/biomedical +" +GPT Takes the Bar Exam,Michael Bommarito II,http://arxiv.org/pdf/2212.14402v1.pdf,2022-12-29,"['cs.cl', 'cs.ai', 'cs.lg']",2212.14402v1.pdf," Nearly all jurisdictions in the United States require a professional license +exam, commonly referred to as ""the Bar Exam,"" as a precondition for law +practice. To even sit for the exam, most jurisdictions require that an +applicant completes at least seven years of post-secondary education, including +three years at an accredited law school. In addition, most test-takers also +undergo weeks to months of further, exam-specific preparation. Despite this +significant investment of time and capital, approximately one in five +test-takers still score under the rate required to pass the exam on their first +try. In the face of a complex task that requires such depth of knowledge, what, +then, should we expect of the state of the art in ""AI?"" In this research, we +document our experimental evaluation of the performance of OpenAI's +`text-davinci-003` model, often-referred to as GPT-3.5, on the multistate +multiple choice (MBE) section of the exam. While we find no benefit in +fine-tuning over GPT-3.5's zero-shot performance at the scale of our training +data, we do find that hyperparameter optimization and prompt engineering +positively impacted GPT-3.5's zero-shot performance. For best prompt and +parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete +NCBE MBE practice exam, significantly in excess of the 25% baseline guessing +rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's +ranking of responses is also highly-correlated with correctness; its top two +and top three choices are correct 71% and 88% of the time, respectively, +indicating very strong non-entailment performance. While our ability to +interpret these results is limited by nascent scientific understanding of LLMs +and the proprietary nature of GPT, we believe that these results strongly +suggest that an LLM will pass the MBE component of the Bar Exam in the near +future. +" +Few-shot Multimodal Multitask Multilingual Learning,Aman Chadha,http://arxiv.org/pdf/2303.12489v1.pdf,2023-02-19,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.mm']",2303.12489v1.pdf," While few-shot learning as a transfer learning paradigm has gained +significant traction for scenarios with limited data, it has primarily been +explored in the context of building unimodal and unilingual models. +Furthermore, a significant part of the existing literature in the domain of +few-shot multitask learning perform in-context learning which requires manually +generated prompts as the input, yielding varying outcomes depending on the +level of manual prompt-engineering. In addition, in-context learning suffers +from substantial computational, memory, and storage costs which eventually +leads to high inference latency because it involves running all of the prompt's +examples through the model every time a prediction is made. In contrast, +methods based on the transfer learning via the fine-tuning paradigm avoid the +aforementioned issues at a one-time cost of fine-tuning weights on a per-task +basis. However, such methods lack exposure to few-shot multimodal multitask +learning. In this paper, we propose few-shot learning for a multimodal +multitask multilingual (FM3) setting by adapting pre-trained vision and +language models using task-specific hypernetworks and contrastively fine-tuning +them to enable few-shot learning. FM3's architecture combines the best of both +worlds of in-context and fine-tuning based learning and consists of three major +components: (i) multimodal contrastive fine-tuning to enable few-shot learning, +(ii) hypernetwork task adaptation to perform multitask learning, and (iii) +task-specific output heads to cater to a plethora of diverse tasks. FM3 learns +the most prominent tasks in the vision and language domains along with their +intersections, namely visual entailment (VE), visual question answering (VQA), +and natural language understanding (NLU) tasks such as neural entity +recognition (NER) and the GLUE benchmark including QNLI, MNLI, QQP, and SST-2. +" +Improving Few-Shot Prompts with Relevant Static Analysis Products,Toufique Ahmed,http://arxiv.org/pdf/2304.06815v2.pdf,2023-04-13,"['cs.se', 'cs.lg']",2304.06815v2.pdf," Large Language Models (LLM) are a new class of computation engines, +""programmed"" via prompt engineering. We are still learning how to best +""program"" these LLMs to help developers. We start with the intuition that +developers tend to consciously and unconsciously have a collection of semantics +facts in mind when working on coding tasks. Mostly these are shallow, simple +facts arising from a quick read. For a function, examples of facts might +include parameter and local variable names, return expressions, simple pre- and +post-conditions, and basic control and data flow, etc. + One might assume that the powerful multi-layer architecture of +transformer-style LLMs makes them inherently capable of doing this simple level +of ""code analysis"" and extracting such information, implicitly, while +processing code: but are they, really? If they aren't, could explicitly adding +this information help? Our goal here is to investigate this question, using the +code summarization task and evaluate whether automatically augmenting an LLM's +prompt with semantic facts explicitly, actually helps. + Prior work shows that LLM performance on code summarization benefits from +few-shot samples drawn either from the same-project or from examples found via +information retrieval methods (such as BM25). While summarization performance +has steadily increased since the early days, there is still room for +improvement: LLM performance on code summarization still lags its performance +on natural-language tasks like translation and text summarization. + We find that adding semantic facts actually does help! This approach improves +performance in several different settings suggested by prior work, including +for two different Large Language Models. In most cases, improvement nears or +exceeds 2 BLEU; for the PHP language in the challenging CodeSearchNet dataset, +this augmentation actually yields performance surpassing 30 BLEU. +" +Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery,Debadutta Dash,http://arxiv.org/pdf/2304.13714v3.pdf,2023-04-26,"['cs.ai', 'cs.cl', 'cs.ir']",2304.13714v3.pdf," Despite growing interest in using large language models (LLMs) in healthcare, +current explorations do not assess the real-world utility and safety of LLMs in +clinical settings. Our objective was to determine whether two LLMs can serve +information needs submitted by physicians as questions to an informatics +consultation service in a safe and concordant manner. Sixty six questions from +an informatics consult service were submitted to GPT-3.5 and GPT-4 via simple +prompts. 12 physicians assessed the LLM responses' possibility of patient harm +and concordance with existing reports from an informatics consultation service. +Physician assessments were summarized based on majority vote. For no questions +did a majority of physicians deem either LLM response as harmful. For GPT-3.5, +responses to 8 questions were concordant with the informatics consult report, +20 discordant, and 9 were unable to be assessed. There were 29 responses with +no majority on ""Agree"", ""Disagree"", and ""Unable to assess"". For GPT-4, +responses to 13 questions were concordant, 15 discordant, and 3 were unable to +be assessed. There were 35 responses with no majority. Responses from both LLMs +were largely devoid of overt harm, but less than 20% of the responses agreed +with an answer from an informatics consultation service, responses contained +hallucinated references, and physicians were divided on what constitutes harm. +These results suggest that while general purpose LLMs are able to provide safe +and credible responses, they often do not meet the specific information need of +a given question. A definitive evaluation of the usefulness of LLMs in +healthcare settings will likely require additional research on prompt +engineering, calibration, and custom-tailoring of general purpose models. +" +Zelda: Video Analytics using Vision-Language Models,Francisco Romero,http://arxiv.org/pdf/2305.03785v2.pdf,2023-05-05,['cs.db'],2305.03785v2.pdf," Advances in ML have motivated the design of video analytics systems that +allow for structured queries over video datasets. However, existing systems +limit query expressivity, require users to specify an ML model per predicate, +rely on complex optimizations that trade off accuracy for performance, and +return large amounts of redundant and low-quality results. This paper focuses +on the recently developed Vision-Language Models (VLMs) that allow users to +query images using natural language like ""cars during daytime at traffic +intersections."" Through an in-depth analysis, we show VLMs address three +limitations of current video analytics systems: general expressivity, a single +general purpose model to query many predicates, and are both simple and fast. +However, VLMs still return large numbers of redundant and low-quality results +that can overwhelm and burden users. In addition, VLMs often require manual +prompt engineering to improve result relevance. + We present Zelda: a video analytics system that uses VLMs to return both +relevant and semantically diverse results for top-K queries on large video +datasets. Zelda prompts the VLM with the user's query in natural language. +Zelda then automatically adds discriminator and synonym terms to boost +accuracy, and terms to identify low-quality frames. To improve result +diversity, Zelda uses semantic-rich VLM embeddings in an algorithm that prunes +similar frames while considering their relevance to the query and the number of +top-K results requested. We evaluate Zelda across five datasets and 19 queries +and quantitatively show it achieves higher mean average precision (up to 1.15x) +and improves average pairwise similarity (up to 1.16x) compared to using VLMs +out-of-the-box. We also compare Zelda to a state-of-the-art video analytics +engine and show that Zelda retrieves results 7.5x (up to 10.4x) faster for the +same accuracy and frame diversity. +" +ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models,Huahui Yi,http://arxiv.org/pdf/2305.18993v1.pdf,2023-05-30,['cs.cv'],2305.18993v1.pdf," Large pre-trained vision-language models have shown great prominence in +transferring pre-acquired knowledge to various domains and downstream tasks +with appropriate prompting or tuning. Existing prevalent tuning methods can be +generally categorized into three genres: 1) prompt engineering by creating +suitable prompt texts, which is time-consuming and requires domain expertise; +2) or simply fine-tuning the whole model, which is extremely inefficient; 3) +prompt tuning through parameterized prompt embeddings with the text encoder. +Nevertheless, all methods rely on the text encoder for bridging the modality +gap between vision and language. In this work, we question the necessity of the +cumbersome text encoder for a more lightweight and efficient tuning paradigm as +well as more representative prompt embeddings closer to the image +representations. To achieve this, we propose a Concept Embedding Search (ConES) +approach by optimizing prompt embeddings -- without the need of the text +encoder -- to capture the 'concept' of the image modality through a variety of +task objectives. By dropping the text encoder, we are able to significantly +speed up the learning process, \eg, from about an hour to just ten minutes in +our experiments for personalized text-to-image generation without impairing the +generation quality. Moreover, our proposed approach is orthogonal to current +existing tuning methods since the searched concept embeddings can be further +utilized in the next stage of fine-tuning the pre-trained large models for +boosting performance. Extensive experiments show that our approach can beat the +prompt tuning and textual inversion methods in a variety of downstream tasks +including objection detection, instance segmentation, and image generation. Our +approach also shows better generalization capability for unseen concepts in +specialized domains, such as the medical domain. +" +ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis,Zhiling Zheng,http://arxiv.org/pdf/2306.11296v2.pdf,2023-06-20,"['cs.ir', 'cond-mat.mtrl-sci', 'cs.cl', 'physics.chem-ph']",2306.11296v2.pdf," We use prompt engineering to guide ChatGPT in the automation of text mining +of metal-organic frameworks (MOFs) synthesis conditions from diverse formats +and styles of the scientific literature. This effectively mitigates ChatGPT's +tendency to hallucinate information -- an issue that previously made the use of +Large Language Models (LLMs) in scientific fields challenging. Our approach +involves the development of a workflow implementing three different processes +for text mining, programmed by ChatGPT itself. All of them enable parsing, +searching, filtering, classification, summarization, and data unification with +different tradeoffs between labor, speed, and accuracy. We deploy this system +to extract 26,257 distinct synthesis parameters pertaining to approximately 800 +MOFs sourced from peer-reviewed research articles. This process incorporates +our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, +resulting in impressive precision, recall, and F1 scores of 90-99%. +Furthermore, with the dataset built by text mining, we constructed a +machine-learning model with over 86% accuracy in predicting MOF experimental +crystallization outcomes and preliminarily identifying important factors in MOF +crystallization. We also developed a reliable data-grounded MOF chatbot to +answer questions on chemical reactions and synthesis procedures. Given that the +process of using ChatGPT reliably mines and tabulates diverse MOF synthesis +information in a unified format, while using only narrative language requiring +no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be +very useful across various other chemistry sub-disciplines. +" +Identifying and Extracting Rare Disease Phenotypes with Large Language Models,Cathy Shyr,http://arxiv.org/pdf/2306.12656v1.pdf,2023-06-22,"['cs.cl', 'cs.ai']",2306.12656v1.pdf," Rare diseases (RDs) are collectively common and affect 300 million people +worldwide. Accurate phenotyping is critical for informing diagnosis and +treatment, but RD phenotypes are often embedded in unstructured text and +time-consuming to extract manually. While natural language processing (NLP) +models can perform named entity recognition (NER) to automate extraction, a +major bottleneck is the development of a large, annotated corpus for model +training. Recently, prompt learning emerged as an NLP paradigm that can lead to +more generalizable results without any (zero-shot) or few labeled samples +(few-shot). Despite growing interest in ChatGPT, a revolutionary large language +model capable of following complex human prompts and generating high-quality +responses, none have studied its NER performance for RDs in the zero- and +few-shot settings. To this end, we engineered novel prompts aimed at extracting +RD phenotypes and, to the best of our knowledge, are the first the establish a +benchmark for evaluating ChatGPT's performance in these settings. We compared +its performance to the traditional fine-tuning approach and conducted an +in-depth error analysis. Overall, fine-tuning BioClinicalBERT resulted in +higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.591 in the +zero- and few-shot settings, respectively). Despite this, ChatGPT achieved +similar or higher accuracy for certain entities (i.e., rare diseases and signs) +in the one-shot setting (F1 of 0.776 and 0.725). This suggests that with +appropriate prompt engineering, ChatGPT has the potential to match or +outperform fine-tuned language models for certain entity types with just one +labeled sample. While the proliferation of large language models may provide +opportunities for supporting RD diagnosis and treatment, researchers and +clinicians should critically evaluate model outputs and be well-informed of +their limitations. +" +Demonstrations of the Potential of AI-based Political Issue Polling,Nathan E. Sanders,http://arxiv.org/pdf/2307.04781v2.pdf,2023-07-10,['cs.cy'],2307.04781v2.pdf," Political polling is a multi-billion dollar industry with outsized influence +on the societal trajectory of the United States and nations around the world. +However, it has been challenged by factors that stress its cost, availability, +and accuracy. At the same time, artificial intelligence (AI) chatbots have +become compelling stand-ins for human behavior, powered by increasingly +sophisticated large language models (LLMs). Could AI chatbots be an effective +tool for anticipating public opinion on controversial issues to the extent that +they could be used by campaigns, interest groups, and polling firms? We have +developed a prompt engineering methodology for eliciting human-like survey +responses from ChatGPT, which simulate the response to a policy question of a +person described by a set of demographic factors, and produce both an ordinal +numeric response score and a textual justification. We execute large scale +experiments, querying for thousands of simulated responses at a cost far lower +than human surveys. We compare simulated data to human issue polling data from +the Cooperative Election Study (CES). We find that ChatGPT is effective at +anticipating both the mean level and distribution of public opinion on a +variety of policy issues such as abortion bans and approval of the US Supreme +Court, particularly in their ideological breakdown (correlation typically +>85%). However, it is less successful at anticipating demographic-level +differences. Moreover, ChatGPT tends to overgeneralize to new policy issues +that arose after its training data was collected, such as US support for +involvement in the war in Ukraine. Our work has implications for our +understanding of the strengths and limitations of the current generation of AI +chatbots as virtual publics or online listening platforms, future directions +for LLM development, and applications of AI tools to the political domain. +(Abridged) +" +Go Beyond The Obvious: Probing the gap of INFORMAL reasoning ability between Humanity and LLMs by Detective Reasoning Puzzle Benchmark,Zhouhon Gu,http://arxiv.org/pdf/2307.05113v2.pdf,2023-07-11,['cs.cl'],2307.05113v2.pdf," Informal reasoning ability is the ability to reason based on common sense, +experience, and intuition.Humans use informal reasoning every day to extract +the most influential elements for their decision-making from a large amount of +life-like information.With the rapid development of language models, the +realization of general artificial intelligence has emerged with hope. Given the +outstanding informal reasoning ability of humans, how much informal reasoning +ability language models have has not been well studied by scholars.In order to +explore the gap between humans and language models in informal reasoning +ability, this paper constructs a Detective Reasoning Benchmark, which is an +assembly of 1,200 questions gathered from accessible online resources, aims at +evaluating the model's informal reasoning ability in real-life +context.Considering the improvement of the model's informal reasoning ability +restricted by the lack of benchmark, we further propose a Self-Question Prompt +Framework that mimics human thinking to enhance the model's informal reasoning +ability.The goals of self-question are to find key elements, deeply investigate +the connections between these elements, encourage the relationship between each +element and the problem, and finally, require the model to reasonably answer +the problem.The experimental results show that human performance greatly +outperforms the SoTA Language Models in Detective Reasoning Benchmark.Besides, +Self-Question is proven to be the most effective prompt engineering in +improving GPT-4's informal reasoning ability, but it still does not even +surpass the lowest score made by human participants.Upon acceptance of the +paper, the source code for the benchmark will be made publicly accessible. +" +Benchmarking Causal Study to Interpret Large Language Models for Source Code,Daniel Rodriguez-Cardenas,http://arxiv.org/pdf/2308.12415v1.pdf,2023-08-23,"['cs.se', 'cs.ai']",2308.12415v1.pdf," One of the most common solutions adopted by software researchers to address +code generation is by training Large Language Models (LLMs) on massive amounts +of source code. Although a number of studies have shown that LLMs have been +effectively evaluated on popular accuracy metrics (e.g., BLEU, CodeBleu), +previous research has largely overlooked the role of Causal Inference as a +fundamental component of the interpretability of LLMs' performance. Existing +benchmarks and datasets are meant to highlight the difference between the +expected and the generated outcome, but do not take into account confounding +variables (e.g., lines of code, prompt size) that equally influence the +accuracy metrics. The fact remains that, when dealing with generative software +tasks by LLMs, no benchmark is available to tell researchers how to quantify +neither the causal effect of SE-based treatments nor the correlation of +confounders to the model's performance. In an effort to bring statistical rigor +to the evaluation of LLMs, this paper introduces a benchmarking strategy named +Galeras comprised of curated testbeds for three SE tasks (i.e., code +completion, code summarization, and commit generation) to help aid the +interpretation of LLMs' performance. We illustrate the insights of our +benchmarking strategy by conducting a case study on the performance of ChatGPT +under distinct prompt engineering methods. The results of the case study +demonstrate the positive causal influence of prompt semantics on ChatGPT's +generative performance by an average treatment effect of $\approx 3\%$. +Moreover, it was found that confounders such as prompt size are highly +correlated with accuracy metrics ($\approx 0.412\%$). The end result of our +case study is to showcase causal inference evaluations, in practice, to reduce +confounding bias. By reducing the bias, we offer an interpretable solution for +the accuracy metric under analysis. +" +GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench,Ajmain Inqiad Alam,http://arxiv.org/pdf/2308.13963v2.pdf,2023-08-26,['cs.se'],2308.13963v2.pdf," With the emergence of Machine Learning, there has been a surge in leveraging +its capabilities for problem-solving across various domains. In the code clone +realm, the identification of type-4 or semantic clones has emerged as a crucial +yet challenging task. Researchers aim to utilize Machine Learning to tackle +this challenge, often relying on the BigCloneBench dataset. However, it's worth +noting that BigCloneBench, originally not designed for semantic clone +detection, presents several limitations that hinder its suitability as a +comprehensive training dataset for this specific purpose. Furthermore, CLCDSA +dataset suffers from a lack of reusable examples aligning with real-world +software systems, rendering it inadequate for cross-language clone detection +approaches. In this work, we present a comprehensive semantic clone and +cross-language clone benchmark, GPTCloneBench by exploiting SemanticCloneBench +and OpenAI's GPT-3 model. In particular, using code fragments from +SemanticCloneBench as sample inputs along with appropriate prompt engineering +for GPT-3 model, we generate semantic and cross-language clones for these +specific fragments and then conduct a combination of extensive manual analysis, +tool-assisted filtering, functionality testing and automated validation in +building the benchmark. From 79,928 clone pairs of GPT-3 output, we created a +benchmark with 37,149 true semantic clone pairs, 19,288 false semantic +pairs(Type-1/Type-2), and 20,770 cross-language clones across four languages +(Java, C, C#, and Python). Our benchmark is 15-fold larger than +SemanticCloneBench, has more functional code examples for software systems and +programming language support than CLCDSA, and overcomes BigCloneBench's +qualities, quantification, and language variety limitations. +" +"AI Foundation Models for Weather and Climate: Applications, Design, and Implementation",S. Karthik Mukkavilli,http://arxiv.org/pdf/2309.10808v2.pdf,2023-09-19,"['cs.lg', 'cs.ai', 'physics.ao-ph', '68t07 (primary), 68t01, 86a08', 'i.2.0; i.4.0; j.2.5']",2309.10808v2.pdf," Machine learning and deep learning methods have been widely explored in +understanding the chaotic behavior of the atmosphere and furthering weather +forecasting. There has been increasing interest from technology companies, +government institutions, and meteorological agencies in building digital twins +of the Earth. Recent approaches using transformers, physics-informed machine +learning, and graph neural networks have demonstrated state-of-the-art +performance on relatively narrow spatiotemporal scales and specific tasks. With +the recent success of generative artificial intelligence (AI) using pre-trained +transformers for language modeling and vision with prompt engineering and +fine-tuning, we are now moving towards generalizable AI. In particular, we are +witnessing the rise of AI foundation models that can perform competitively on +multiple domain-specific downstream tasks. Despite this progress, we are still +in the nascent stages of a generalizable AI model for global Earth system +models, regional climate models, and mesoscale weather models. Here, we review +current state-of-the-art AI approaches, primarily from transformer and operator +learning literature in the context of meteorology. We provide our perspective +on criteria for success towards a family of foundation models for nowcasting +and forecasting weather and climate predictions. We also discuss how such +models can perform competitively on downstream tasks such as downscaling +(super-resolution), identifying conditions conducive to the occurrence of +wildfires, and predicting consequential meteorological phenomena across various +spatiotemporal scales such as hurricanes and atmospheric rivers. In particular, +we examine current AI methodologies and contend they have matured enough to +design and implement a weather foundation model. +" +Exploring Small Language Models with Prompt-Learning Paradigm for Efficient Domain-Specific Text Classification,Hengyu Luo,http://arxiv.org/pdf/2309.14779v1.pdf,2023-09-26,"['cs.cl', 'cs.ai', 'cs.lg']",2309.14779v1.pdf," Domain-specific text classification faces the challenge of scarce labeled +data due to the high cost of manual labeling. Prompt-learning, known for its +efficiency in few-shot scenarios, is proposed as an alternative to traditional +fine-tuning methods. And besides, although large language models (LLMs) have +gained prominence, small language models (SLMs, with under 1B parameters) offer +significant customizability, adaptability, and cost-effectiveness for +domain-specific tasks, given industry constraints. In this study, we +investigate the potential of SLMs combined with prompt-learning paradigm for +domain-specific text classification, specifically within customer-agent +interactions in retail. Our evaluations show that, in few-shot settings when +prompt-based model fine-tuning is possible, T5-base, a typical SLM with 220M +parameters, achieve approximately 75% accuracy with limited labeled data (up to +15% of full data), which shows great potentials of SLMs with prompt-learning. +Based on this, We further validate the effectiveness of active few-shot +sampling and the ensemble strategy in the prompt-learning pipeline that +contribute to a remarkable performance gain. Besides, in zero-shot settings +with a fixed model, we underscore a pivotal observation that, although the +GPT-3.5-turbo equipped with around 154B parameters garners an accuracy of +55.16%, the power of well designed prompts becomes evident when the +FLAN-T5-large, a model with a mere 0.5% of GPT-3.5-turbo's parameters, achieves +an accuracy exceeding 31% with the optimized prompt, a leap from its sub-18% +performance with an unoptimized one. Our findings underscore the promise of +prompt-learning in classification tasks with SLMs, emphasizing the benefits of +active few-shot sampling, and ensemble strategies in few-shot settings, and the +importance of prompt engineering in zero-shot settings. +" +Label Supervised LLaMA Finetuning,Zongxi Li,http://arxiv.org/pdf/2310.01208v1.pdf,2023-10-02,['cs.cl'],2310.01208v1.pdf," The recent success of Large Language Models (LLMs) has gained significant +attention in both academia and industry. Substantial efforts have been made to +enhance the zero- and few-shot generalization capabilities of open-source LLMs +through finetuning. Currently, the prevailing approach is instruction-tuning, +which trains LLMs to complete real-world tasks by generating responses guided +by natural language instructions. It is worth noticing that such an approach +may underperform in sequence and token classification tasks. Unlike text +generation tasks, classification tasks have a limited label space, where +precise label prediction is more appreciated than generating diverse and +human-like responses. Prior research has unveiled that instruction-tuned LLMs +cannot outperform BERT, prompting us to explore the potential of leveraging +latent representations from LLMs for supervised label prediction. In this +paper, we introduce a label-supervised adaptation for LLMs, which aims to +finetuning the model with discriminant labels. We evaluate this approach with +Label Supervised LLaMA (LS-LLaMA), based on LLaMA-2-7B, a relatively +small-scale LLM, and can be finetuned on a single GeForce RTX4090 GPU. We +extract latent representations from the final LLaMA layer and project them into +the label space to compute the cross-entropy loss. The model is finetuned by +Low-Rank Adaptation (LoRA) to minimize this loss. Remarkably, without intricate +prompt engineering or external knowledge, LS-LLaMA substantially outperforms +LLMs ten times its size in scale and demonstrates consistent improvements +compared to robust baselines like BERT-Large and RoBERTa-Large in text +classification. Moreover, by removing the causal mask from decoders, LS-unLLaMA +achieves the state-of-the-art performance in named entity recognition (NER). +Our work will shed light on a novel approach to adapting LLMs for various +downstream tasks. +" +Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models,Zeqiang Lai,http://arxiv.org/pdf/2310.07653v2.pdf,2023-10-11,['cs.ai'],2310.07653v2.pdf," The revolution of artificial intelligence content generation has been rapidly +accelerated with the booming text-to-image (T2I) diffusion models. Within just +two years of development, it was unprecedentedly of high-quality, diversity, +and creativity that the state-of-the-art models could generate. However, a +prevalent limitation persists in the effective communication with these popular +T2I models, such as Stable Diffusion, using natural language descriptions. This +typically makes an engaging image hard to obtain without expertise in prompt +engineering with complex word compositions, magic tags, and annotations. +Inspired by the recently released DALLE3 - a T2I model directly built-in +ChatGPT that talks human language, we revisit the existing T2I systems +endeavoring to align human intent and introduce a new task - interactive text +to image (iT2I), where people can interact with LLM for interleaved +high-quality image generation/edit/refinement and question answering with +stronger images and text correspondences using natural language. In addressing +the iT2I problem, we present a simple approach that augments LLMs for iT2I with +prompting techniques and off-the-shelf T2I models. We evaluate our approach for +iT2I in a variety of common-used scenarios under different LLMs, e.g., ChatGPT, +LLAMA, Baichuan, and InternLM. We demonstrate that our approach could be a +convenient and low-cost way to introduce the iT2I ability for any existing LLMs +and any text-to-image models without any training while bringing little +degradation on LLMs' inherent capabilities in, e.g., question answering and +code generation. We hope this work could draw broader attention and provide +inspiration for boosting user experience in human-machine interactions +alongside the image quality of the next-generation T2I systems. +" +Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques,Junxiao Shen,http://arxiv.org/pdf/2310.08101v2.pdf,2023-10-12,"['cs.cl', 'cs.ai']",2310.08101v2.pdf," Text entry is an essential task in our day-to-day digital interactions. +Numerous intelligent features have been developed to streamline this process, +making text entry more effective, efficient, and fluid. These improvements +include sentence prediction and user personalization. However, as deep +learning-based language models become the norm for these advanced features, the +necessity for data collection and model fine-tuning increases. These challenges +can be mitigated by harnessing the in-context learning capability of large +language models such as GPT-3.5. This unique feature allows the language model +to acquire new skills through prompts, eliminating the need for data collection +and fine-tuning. Consequently, large language models can learn various text +prediction techniques. We initially showed that, for a sentence prediction +task, merely prompting GPT-3.5 surpassed a GPT-2 backed system and is +comparable with a fine-tuned GPT-3.5 model, with the latter two methods +requiring costly data collection, fine-tuning and post-processing. However, the +task of prompting large language models to specialize in specific text +prediction tasks can be challenging, particularly for designers without +expertise in prompt engineering. To address this, we introduce Promptor, a +conversational prompt generation agent designed to engage proactively with +designers. Promptor can automatically generate complex prompts tailored to meet +specific needs, thus offering a solution to this challenge. We conducted a user +study involving 24 participants creating prompts for three intelligent text +entry tasks, half of the participants used Promptor while the other half +designed prompts themselves. The results show that Promptor-designed prompts +result in a 35% increase in similarity and 22% in coherence over those by +designers. +" +Human-in-the-loop Machine Translation with Large Language Model,Xinyi Yang,http://arxiv.org/pdf/2310.08908v1.pdf,2023-10-13,['cs.cl'],2310.08908v1.pdf," The large language model (LLM) has garnered significant attention due to its +in-context learning mechanisms and emergent capabilities. The research +community has conducted several pilot studies to apply LLMs to machine +translation tasks and evaluate their performance from diverse perspectives. +However, previous research has primarily focused on the LLM itself and has not +explored human intervention in the inference process of LLM. The +characteristics of LLM, such as in-context learning and prompt engineering, +closely mirror human cognitive abilities in language tasks, offering an +intuitive solution for human-in-the-loop generation. In this study, we propose +a human-in-the-loop pipeline that guides LLMs to produce customized outputs +with revision instructions. The pipeline initiates by prompting the LLM to +produce a draft translation, followed by the utilization of automatic retrieval +or human feedback as supervision signals to enhance the LLM's translation +through in-context learning. The human-machine interactions generated in this +pipeline are also stored in an external database to expand the in-context +retrieval database, enabling us to leverage human supervision in an offline +setting. We evaluate the proposed pipeline using GPT-3.5-turbo API on five +domain-specific benchmarks for German-English translation. The results +demonstrate the effectiveness of the pipeline in tailoring in-domain +translations and improving translation performance compared to direct +translation. Additionally, we discuss the results from the following +perspectives: 1) the effectiveness of different in-context retrieval methods; +2) the construction of a retrieval database under low-resource scenarios; 3) +the observed domains differences; 4) the quantitative analysis of linguistic +statistics; and 5) the qualitative analysis of translation cases. The code and +data are available at https://github.com/NLP2CT/HIL-MT/. +" +ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles,Savvas Petridis,http://arxiv.org/pdf/2310.15428v1.pdf,2023-10-24,"['cs.hc', 'cs.ai']",2310.15428v1.pdf," Large language model (LLM) prompting is a promising new approach for users to +create and customize their own chatbots. However, current methods for steering +a chatbot's outputs, such as prompt engineering and fine-tuning, do not support +users in converting their natural feedback on the model's outputs to changes in +the prompt or model. In this work, we explore how to enable users to +interactively refine model outputs through their feedback, by helping them +convert their feedback into a set of principles (i.e. a constitution) that +dictate the model's behavior. From a formative study, we (1) found that users +needed support converting their feedback into principles for the chatbot and +(2) classified the different principle types desired by users. Inspired by +these findings, we developed ConstitutionMaker, an interactive tool for +converting user feedback into principles, to steer LLM-based chatbots. With +ConstitutionMaker, users can provide either positive or negative feedback in +natural language, select auto-generated feedback, or rewrite the chatbot's +response; each mode of feedback automatically generates a principle that is +inserted into the chatbot's prompt. In a user study with 14 participants, we +compare ConstitutionMaker to an ablated version, where users write their own +principles. With ConstitutionMaker, participants felt that their principles +could better guide the chatbot, that they could more easily convert their +feedback into principles, and that they could write principles more +efficiently, with less mental demand. ConstitutionMaker helped users identify +ways to improve the chatbot, formulate their intuitive responses to the model +into feedback, and convert this feedback into specific and clear principles. +Together, these findings inform future tools that support the interactive +critiquing of LLM outputs. +" +Few-shot learning for sentence pair classification and its applications in software engineering,Robert Kraig Helmeczi,http://arxiv.org/pdf/2306.08058v1.pdf,2023-06-13,['cs.se'],2306.08058v1.pdf," Few-shot learning-the ability to train models with access to limited data-has +become increasingly popular in the natural language processing (NLP) domain, as +large language models such as GPT and T0 have been empirically shown to achieve +high performance in numerous tasks with access to just a handful of labeled +examples. Smaller language models such as BERT and its variants have also been +shown to achieve strong performance with just a handful of labeled examples +when combined with few-shot learning algorithms like pattern-exploiting +training (PET) and SetFit. The focus of this work is to investigate the +performance of alternative few-shot learning approaches with BERT-based models. +Specifically, vanilla fine-tuning, PET and SetFit are compared for numerous +BERT-based checkpoints over an array of training set sizes. To facilitate this +investigation, applications of few-shot learning are considered in software +engineering. For each task, high-performance techniques and their associated +model checkpoints are identified through detailed empirical analysis. Our +results establish PET as a strong few-shot learning approach, and our analysis +shows that with just a few hundred labeled examples it can achieve performance +near that of fine-tuning on full-sized data sets. +" +FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark,Liang Xu,http://arxiv.org/pdf/2107.07498v2.pdf,2021-07-15,"['cs.cl', 'cs.ai']",2107.07498v2.pdf," Pretrained Language Models (PLMs) have achieved tremendous success in natural +language understanding tasks. While different learning schemes -- fine-tuning, +zero-shot, and few-shot learning -- have been widely explored and compared for +languages such as English, there is comparatively little work in Chinese to +fairly and comprehensively evaluate and compare these methods and thus hinders +cumulative progress. In this paper, we introduce the Chinese Few-shot Learning +Evaluation Benchmark (FewCLUE), the first comprehensive few-shot evaluation +benchmark in Chinese. It includes nine tasks, ranging from single-sentence and +sentence-pair classification tasks to machine reading comprehension tasks. We +systematically evaluate five state-of-the-art (SOTA) few-shot learning methods +(including PET, ADAPET, LM-BFF, P-tuning and EFL), and compare their +performance with fine-tuning and zero-shot learning schemes on the newly +constructed FewCLUE benchmark. Experimental results reveal that: 1) The effect +of different few-shot learning methods is sensitive to the pre-trained model to +which the methods are applied; 2) PET and P-tuning achieve the best overall +performance with RoBERTa and ERNIE respectively. Our benchmark is used in the +few-shot learning contest of NLPCC 2021. In addition, we provide a +user-friendly toolkit, as well as an online leaderboard to help facilitate +further progress on Chinese few-shot learning. We provide a baseline +performance on different learning methods, a reference for future research. +" +Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning,Jishnu Jaykumar P,http://arxiv.org/pdf/2307.03073v2.pdf,2023-07-06,"['cs.cv', 'cs.ro']",2307.03073v2.pdf," We propose a novel framework for few-shot learning by leveraging large-scale +vision-language models such as CLIP. Motivated by the unimodal prototypical +networks for few-shot learning, we introduce PROTO-CLIP that utilizes image +prototypes and text prototypes for few-shot learning. Specifically, PROTO-CLIP +adapts the image encoder and text encoder in CLIP in a joint fashion using +few-shot examples. The two encoders are used to compute prototypes of image +classes for classification. During adaptation, we propose aligning the image +and text prototypes of corresponding classes. Such a proposed alignment is +beneficial for few-shot classification due to the contributions from both types +of prototypes. We demonstrate the effectiveness of our method by conducting +experiments on benchmark datasets for few-shot learning as well as in the real +world for robot perception. +" +A Survey on Recent Named Entity Recognition and Relation Classification Methods with Focus on Few-Shot Learning Approaches,Sakher Alqaaidi,http://arxiv.org/pdf/2310.19055v1.pdf,2023-10-29,['cs.cl'],2310.19055v1.pdf," Named entity recognition and relation classification are key stages for +extracting information from unstructured text. Several natural language +processing applications utilize the two tasks, such as information retrieval, +knowledge graph construction and completion, question answering and other +domain-specific applications, such as biomedical data mining. We present a +survey of recent approaches in the two tasks with focus on few-shot learning +approaches. Our work compares the main approaches followed in the two +paradigms. Additionally, we report the latest metric scores in the two tasks +with a structured analysis that considers the results in the few-shot learning +scope. +" +True Few-Shot Learning with Prompts -- A Real-World Perspective,Timo Schick,http://arxiv.org/pdf/2111.13440v1.pdf,2021-11-26,['cs.cl'],2111.13440v1.pdf," Prompt-based approaches are strong at few-shot learning. However, Perez et +al. (2021) have recently cast doubt on their performance because they had +difficulty getting good results in a ""true"" few-shot setting in which prompts +and hyperparameters cannot be tuned on a dev set. In view of this, we conduct +an extensive study of PET, a method that combines textual instructions with +example-based finetuning. We show that, if correctly configured, PET performs +strongly in a true few-shot setting, i.e., without a dev set. Crucial for this +strong performance is PET's ability to intelligently handle multiple prompts. +We then put our findings to a real-world test by running PET on RAFT, a +benchmark of tasks taken directly from realistic NLP applications for which no +labeled dev or test sets are available. PET achieves a new state of the art on +RAFT and performs close to non-expert humans for 7 out of 11 tasks. These +results demonstrate that prompt-based learners like PET excel at true few-shot +learning and underpin our belief that learning from instructions will play an +important role on the path towards human-like few-shot learning capabilities. +" +Improving In-Context Few-Shot Learning via Self-Supervised Training,Mingda Chen,http://arxiv.org/pdf/2205.01703v2.pdf,2022-05-03,['cs.cl'],2205.01703v2.pdf," Self-supervised pretraining has made few-shot learning possible for many NLP +tasks. But the pretraining objectives are not typically adapted specifically +for in-context few-shot learning. In this paper, we propose to use +self-supervision in an intermediate training stage between pretraining and +downstream few-shot usage with the goal to teach the model to perform +in-context few shot learning. We propose and evaluate four self-supervised +objectives on two benchmarks. We find that the intermediate self-supervision +stage produces models that outperform strong baselines. Ablation study shows +that several factors affect the downstream performance, such as the amount of +training data and the diversity of the self-supervised objectives. +Human-annotated cross-task supervision and self-supervision are complementary. +Qualitative analysis suggests that the self-supervised-trained models are +better at following task requirements. +" +Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models,Mengzhou Xia,http://arxiv.org/pdf/2205.15223v3.pdf,2022-05-30,"['cs.cl', 'cs.lg']",2205.15223v3.pdf," Pre-trained masked language models successfully perform few-shot learning by +formulating downstream tasks as text infilling. However, as a strong +alternative in full-shot settings, discriminative pre-trained models like +ELECTRA do not fit into the paradigm. In this work, we adapt prompt-based +few-shot learning to ELECTRA and show that it outperforms masked language +models in a wide range of tasks. ELECTRA is pre-trained to distinguish if a +token is generated or original. We naturally extend that to prompt-based +few-shot learning by training to score the originality of the target options +without introducing new parameters. Our method can be easily adapted to tasks +involving multi-token predictions without extra computation overhead. Analysis +shows that ELECTRA learns distributions that align better with downstream +tasks. +" +Revisiting Few-Shot Learning from a Causal Perspective,Guoliang Lin,http://arxiv.org/pdf/2209.13816v1.pdf,2022-09-28,"['cs.lg', 'cs.ai']",2209.13816v1.pdf," Few-shot learning with N-way K-shot scheme is an open challenge in machine +learning. Many approaches have been proposed to tackle this problem, e.g., the +Matching Networks and CLIP-Adapter. Despite that these approaches have shown +significant progress, the mechanism of why these methods succeed has not been +well explored. In this paper, we interpret these few-shot learning methods via +causal mechanism. We show that the existing approaches can be viewed as +specific forms of front-door adjustment, which is to remove the effects of +confounders. Based on this, we introduce a general causal method for few-shot +learning, which considers not only the relationship between examples but also +the diversity of representations. Experimental results demonstrate the +superiority of our proposed method in few-shot classification on various +benchmark datasets. Code is available in the supplementary material. +" +In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models,Yukun Huang,http://arxiv.org/pdf/2212.10670v1.pdf,2022-12-20,"['cs.cl', 'cs.lg']",2212.10670v1.pdf," Given the success with in-context learning of large pre-trained language +models, we introduce in-context learning distillation to transfer in-context +few-shot learning ability from large models to smaller models. We propose to +combine in-context learning objectives with language modeling objectives to +distill both the ability to read in-context examples and task knowledge to the +smaller models. We perform in-context learning distillation under two different +few-shot learning paradigms: Meta In-context Tuning (Meta-ICT) and Multitask +In-context Tuning (Multitask-ICT). Multitask-ICT performs better on multitask +few-shot learning but also requires more computation than Meta-ICT. Our method +shows consistent improvements for both Meta-ICT and Multitask-ICT on two +benchmarks: LAMA and CrossFit. Our extensive experiments and analysis reveal +that in-context learning objectives and language modeling objectives are +complementary under the Multitask-ICT paradigm. In-context learning objectives +achieve the best performance when combined with language modeling objectives. +" +FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?,Zihao Jiang,http://arxiv.org/pdf/2307.04114v1.pdf,2023-07-09,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.mm']",2307.04114v1.pdf," Few-shot learning aims to train models that can be generalized to novel +classes with only a few samples. Recently, a line of works are proposed to +enhance few-shot learning with accessible semantic information from class +names. However, these works focus on improving existing modules such as visual +prototypes and feature extractors of the standard few-shot learning framework. +This limits the full potential use of semantic information. In this paper, we +propose a novel few-shot learning framework that uses pre-trained language +models based on contrastive learning. To address the challenge of alignment +between visual features and textual embeddings obtained from text-based +pre-trained language model, we carefully design the textual branch of our +framework and introduce a metric module to generalize the cosine similarity. +For better transferability, we let the metric module adapt to different +few-shot tasks and adopt MAML to train the model via bi-level optimization. +Moreover, we conduct extensive experiments on multiple benchmarks to +demonstrate the effectiveness of our method. +" +Reordering Examples Helps during Priming-based Few-Shot Learning,Sawan Kumar,http://arxiv.org/pdf/2106.01751v1.pdf,2021-06-03,['cs.cl'],2106.01751v1.pdf," The ability to learn from limited data, or few-shot learning, is a desirable +and often critical requirement for NLP systems. While many existing methods do +poorly at learning from a handful of examples, large pretrained language models +have recently been shown to be efficient few-shot learners. One approach to +few-shot learning, which does not require finetuning of model parameters, is to +augment the language model's input with priming text which is typically +constructed using task specific descriptions and examples. In this work, we +further explore priming-based few-shot learning, with focus on using examples +as prompts. We show that presenting examples in the right order is key for +generalization. We introduce PERO (Prompting with Examples in the Right Order), +where we formulate few-shot learning as search over the set of permutations of +the training examples. We show that PERO can learn to generalize efficiently +using as few as 10 examples, in contrast to existing approaches. While the +newline token is a natural choice for separating the examples in the prompt, we +show that learning a new separator token can potentially provide further gains +in performance. We demonstrate the effectiveness of the proposed method on the +tasks of sentiment classification, natural language inference and fact +retrieval. Finally, we analyze the learned prompts to reveal novel insights, +including the idea that two training examples in the right order alone can +provide competitive performance for sentiment classification and natural +language inference. +" +CLUES: Few-Shot Learning Evaluation in Natural Language Understanding,Subhabrata Mukherjee,http://arxiv.org/pdf/2111.02570v1.pdf,2021-11-04,"['cs.cl', 'cs.lg']",2111.02570v1.pdf," Most recent progress in natural language understanding (NLU) has been driven, +in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU +models have now matched or exceeded ""human-level"" performance on many tasks in +these benchmarks. Most of these benchmarks, however, give models access to +relatively large amounts of labeled data for training. As such, the models are +provided far more data than required by humans to achieve strong performance. +That has motivated a line of work that focuses on improving few-shot learning +performance of NLU models. However, there is a lack of standardized evaluation +benchmarks for few-shot NLU resulting in different experimental settings in +different papers. To help accelerate this line of work, we introduce CLUES +(Constrained Language Understanding Evaluation Standard), a benchmark for +evaluating the few-shot learning capabilities of NLU models. We demonstrate +that while recent models reach human performance when they have access to large +amounts of labeled data, there is a huge gap in performance in the few-shot +setting for most tasks. We also demonstrate differences between alternative +model families and adaptation techniques in the few shot setting. Finally, we +discuss several principles and choices in designing the experimental settings +for evaluating the true few-shot learning performance and suggest a unified +standardized approach to few-shot learning evaluation. We aim to encourage +research on NLU models that can generalize to new tasks with a small number of +examples. Code and data for CLUES are available at +https://github.com/microsoft/CLUES. +" +Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning,Yu Meng,http://arxiv.org/pdf/2211.03044v2.pdf,2022-11-06,"['cs.cl', 'cs.lg']",2211.03044v2.pdf," Recent studies have revealed the intriguing few-shot learning ability of +pretrained language models (PLMs): They can quickly adapt to a new task when +fine-tuned on a small amount of labeled data formulated as prompts, without +requiring abundant task-specific annotations. Despite their promising +performance, most existing few-shot approaches that only learn from the small +training set still underperform fully supervised training by nontrivial +margins. In this work, we study few-shot learning with PLMs from a different +perspective: We first tune an autoregressive PLM on the few-shot samples and +then use it as a generator to synthesize a large amount of novel training +samples which augment the original training set. To encourage the generator to +produce label-discriminative samples, we train it via weighted maximum +likelihood where the weight of each token is automatically adjusted based on a +discriminative meta-learning objective. A classification PLM can then be +fine-tuned on both the few-shot and the synthetic samples with regularization +for better generalization and stability. Our approach FewGen achieves an +overall better result across seven classification tasks of the GLUE benchmark +than existing few-shot learning methods, improving no-augmentation methods by +5+ average points, and outperforming augmentation methods by 3+ average points. +" +Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data,Alon Albalak,http://arxiv.org/pdf/2302.00674v4.pdf,2023-02-01,"['cs.lg', 'cs.cl']",2302.00674v4.pdf," Few-shot learning is valuable in many real-world applications, but learning a +generalizable model without overfitting to the few labeled datapoints is +challenging. In this work, we focus on Few-shot Learning with Auxiliary Data +(FLAD), a training paradigm that assumes access to auxiliary data during +few-shot learning in hopes of improving generalization. Previous works have +proposed automated methods for mixing auxiliary and target data, but these +methods typically scale linearly (or worse) with the number of auxiliary +datasets, limiting their practicality. In this work we relate FLAD to the +explore-exploit dilemma that is central to the multi-armed bandit setting and +derive algorithms whose computational complexity is independent of the number +of auxiliary datasets, allowing us to scale to 100x more auxiliary datasets +than prior methods. We propose two algorithms -- EXP3-FLAD and UCB1-FLAD -- and +compare them with prior FLAD methods that either explore or exploit, finding +that the combination of exploration and exploitation is crucial. Through +extensive experimentation we find that our methods outperform all pre-existing +FLAD methods by 4% and lead to the first 3 billion parameter language models +that outperform the 175 billion parameter GPT-3. Overall, our work suggests +that the discovery of better, more efficient mixing strategies for FLAD may +provide a viable path towards substantially improving generalization in +few-shot learning. +" +Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching,Donggyun Kim,http://arxiv.org/pdf/2303.14969v1.pdf,2023-03-27,"['cs.cv', 'cs.ai']",2303.14969v1.pdf," Dense prediction tasks are a fundamental class of problems in computer +vision. As supervised methods suffer from high pixel-wise labeling cost, a +few-shot learning solution that can learn any dense task from a few labeled +images is desired. Yet, current few-shot learning methods target a restricted +set of tasks such as semantic segmentation, presumably due to challenges in +designing a general and unified model that is able to flexibly and efficiently +adapt to arbitrary tasks of unseen semantics. We propose Visual Token Matching +(VTM), a universal few-shot learner for arbitrary dense prediction tasks. It +employs non-parametric matching on patch-level embedded tokens of images and +labels that encapsulates all tasks. Also, VTM flexibly adapts to any task with +a tiny amount of task-specific parameters that modulate the matching algorithm. +We implement VTM as a powerful hierarchical encoder-decoder architecture +involving ViT backbones where token matching is performed at multiple feature +hierarchies. We experiment VTM on a challenging variant of Taskonomy dataset +and observe that it robustly few-shot learns various unseen dense prediction +tasks. Surprisingly, it is competitive with fully supervised baselines using +only 10 labeled examples of novel tasks (0.004% of full supervision) and +sometimes outperforms using 0.1% of full supervision. Codes are available at +https://github.com/GitGyun/visual_token_matching. +" +FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning,Kun Song,http://arxiv.org/pdf/2310.15105v3.pdf,2023-10-23,['cs.cv'],2310.15105v3.pdf," Due to the limited availability of data, existing few-shot learning methods +trained from scratch fail to achieve satisfactory performance. In contrast, +large-scale pre-trained models such as CLIP demonstrate remarkable few-shot and +zero-shot capabilities. To enhance the performance of pre-trained models for +downstream tasks, fine-tuning the model on downstream data is frequently +necessary. However, fine-tuning the pre-trained model leads to a decrease in +its generalizability in the presence of distribution shift, while the limited +number of samples in few-shot learning makes the model highly susceptible to +overfitting. Consequently, existing methods for fine-tuning few-shot learning +primarily focus on fine-tuning the model's classification head or introducing +additional structure. In this paper, we introduce a fine-tuning approach termed +Feature Discrimination Alignment (FD-Align). Our method aims to bolster the +model's generalizability by preserving the consistency of spurious features +across the fine-tuning process. Extensive experimental results validate the +efficacy of our approach for both ID and OOD tasks. Once fine-tuned, the model +can seamlessly integrate with existing methods, leading to performance +improvements. Our code can be found in https://github.com/skingorz/FD-Align. +" +Few-Shot Learning with Localization in Realistic Settings,Davis Wertheimer,http://arxiv.org/pdf/1904.08502v2.pdf,2019-04-09,"['cs.cv', 'cs.ai', 'cs.lg', 'stat.ml']",1904.08502v2.pdf," Traditional recognition methods typically require large, +artificially-balanced training classes, while few-shot learning methods are +tested on artificially small ones. In contrast to both extremes, real world +recognition problems exhibit heavy-tailed class distributions, with cluttered +scenes and a mix of coarse and fine-grained class distinctions. We show that +prior methods designed for few-shot learning do not work out of the box in +these challenging conditions, based on a new ""meta-iNat"" benchmark. We +introduce three parameter-free improvements: (a) better training procedures +based on adapting cross-validation to meta-learning, (b) novel architectures +that localize objects using limited bounding box annotations before +classification, and (c) simple parameter-free expansions of the feature space +based on bilinear pooling. Together, these improvements double the accuracy of +state-of-the-art models on meta-iNat while generalizing to prior benchmarks, +complex neural architectures, and settings with substantial domain shift. +" +Model-Agnostic Graph Regularization for Few-Shot Learning,Ethan Shen,http://arxiv.org/pdf/2102.07077v1.pdf,2021-02-14,"['cs.lg', 'cs.cv']",2102.07077v1.pdf," In many domains, relationships between categories are encoded in the +knowledge graph. Recently, promising results have been achieved by +incorporating knowledge graph as side information in hard classification tasks +with severely limited data. However, prior models consist of highly complex +architectures with many sub-components that all seem to impact performance. In +this paper, we present a comprehensive empirical study on graph embedded +few-shot learning. We introduce a graph regularization approach that allows a +deeper understanding of the impact of incorporating graph information between +labels. Our proposed regularization is widely applicable and model-agnostic, +and boosts the performance of any few-shot learning model, including +fine-tuning, metric-based, and optimization-based meta-learning. Our approach +improves the performance of strong base learners by up to 2% on Mini-ImageNet +and 6.7% on ImageNet-FS, outperforming state-of-the-art graph embedded methods. +Additional analyses reveal that graph regularizing models result in a lower +loss for more difficult tasks, such as those with fewer shots and less +informative support examples. +" +Uniform Sampling over Episode Difficulty,Sébastien M. R. Arnold,http://arxiv.org/pdf/2108.01662v2.pdf,2021-08-03,"['cs.lg', 'cs.ai', 'cs.cv']",2108.01662v2.pdf," Episodic training is a core ingredient of few-shot learning to train models +on tasks with limited labelled data. Despite its success, episodic training +remains largely understudied, prompting us to ask the question: what is the +best way to sample episodes? In this paper, we first propose a method to +approximate episode sampling distributions based on their difficulty. Building +on this method, we perform an extensive analysis and find that sampling +uniformly over episode difficulty outperforms other sampling schemes, including +curriculum and easy-/hard-mining. As the proposed sampling method is algorithm +agnostic, we can leverage these insights to improve few-shot learning +accuracies across many episodic training algorithms. We demonstrate the +efficacy of our method across popular few-shot learning datasets, algorithms, +network architectures, and protocols. +" +CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems,Fei Mi,http://arxiv.org/pdf/2109.04645v4.pdf,2021-09-10,"['cs.cl', 'cs.lg']",2109.04645v4.pdf," As labeling cost for different modules in task-oriented dialog (ToD) systems +is high, a major challenge in practice is to learn different tasks with the +least amount of labeled data. Recently, prompting methods over pre-trained +language models (PLMs) have shown promising results for few-shot learning in +ToD. To better utilize the power of PLMs, this paper proposes Comprehensive +Instruction (CINS) that exploits PLMs with extra task-specific instructions. We +design a schema (definition, constraint, prompt) of instructions and their +customized realizations for three important downstream tasks in ToD, i.e. +intent classification, dialog state tracking, and natural language generation. +A sequence-to-sequence model (T5) is adopted to solve these three tasks in a +unified framework. Extensive experiments are conducted on these ToD tasks in +realistic few-shot learning scenarios with small validation data. Empirical +results demonstrate that the proposed CINS approach consistently improves +techniques that finetune PLMs with raw input or short prompts. +" +Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation,Chujie Zheng,http://arxiv.org/pdf/2109.06513v2.pdf,2021-09-14,['cs.cl'],2109.06513v2.pdf," Dialog models can be greatly strengthened through grounding on various +external information, but grounded dialog corpora are usually not naturally +accessible. In this work, we focus on the few-shot learning for grounded dialog +generation (GDG). We first propose a simple prompting method for GDG tasks, +where different constructs of model input, such as the grounding source and the +conversation context, are distinguished through continuous or discrete prompts. +On three typical GDG tasks, we empirically demonstrate and analyze in-depth the +effectiveness of our method. We then conduct extensive experiments to +thoroughly investigate how our prompting method works with different +pre-trained models. We show that prompted language models perform superiorly to +conversational models, and further analyze various factors that influence the +effects of prompting. Overall, our work introduces a prompt-based perspective +to the few-shot learning for GDG tasks, and provides valuable findings and +insights for future research. +" +Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning,Sungyong Baik,http://arxiv.org/pdf/2110.03909v2.pdf,2021-10-08,"['cs.lg', 'cs.cv']",2110.03909v2.pdf," In few-shot learning scenarios, the challenge is to generalize and perform +well on new unseen examples when only very few labeled examples are available +for each task. Model-agnostic meta-learning (MAML) has gained the popularity as +one of the representative few-shot learning methods for its flexibility and +applicability to diverse problems. However, MAML and its variants often resort +to a simple loss function without any auxiliary loss function or regularization +terms that can help achieve better generalization. The problem lies in that +each application and task may require different auxiliary loss function, +especially when tasks are diverse and distinct. Instead of attempting to +hand-design an auxiliary loss function for each application and task, we +introduce a new meta-learning framework with a loss function that adapts to +each task. Our proposed framework, named Meta-Learning with Task-Adaptive Loss +Function (MeTAL), demonstrates the effectiveness and the flexibility across +various domains, such as few-shot classification and few-shot regression. +" +Ontology-enhanced Prompt-tuning for Few-shot Learning,Hongbin Ye,http://arxiv.org/pdf/2201.11332v1.pdf,2022-01-27,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2201.11332v1.pdf," Few-shot Learning (FSL) is aimed to make predictions based on a limited +number of samples. Structured data such as knowledge graphs and ontology +libraries has been leveraged to benefit the few-shot setting in various tasks. +However, the priors adopted by the existing methods suffer from challenging +knowledge missing, knowledge noise, and knowledge heterogeneity, which hinder +the performance for few-shot learning. In this study, we explore knowledge +injection for FSL with pre-trained language models and propose +ontology-enhanced prompt-tuning (OntoPrompt). Specifically, we develop the +ontology transformation based on the external knowledge graph to address the +knowledge missing issue, which fulfills and converts structure knowledge to +text. We further introduce span-sensitive knowledge injection via a visible +matrix to select informative knowledge to handle the knowledge noise issue. To +bridge the gap between knowledge and text, we propose a collective training +algorithm to optimize representations jointly. We evaluate our proposed +OntoPrompt in three tasks, including relation extraction, event extraction, and +knowledge graph completion, with eight datasets. Experimental results +demonstrate that our approach can obtain better few-shot performance than +baselines. +" +Impossible Triangle: What's Next for Pre-trained Language Models?,Chenguang Zhu,http://arxiv.org/pdf/2204.06130v2.pdf,2022-04-13,['cs.cl'],2204.06130v2.pdf," Recent development of large-scale pre-trained language models (PLM) have +significantly improved the capability of models in various NLP tasks, in terms +of performance after task-specific fine-tuning and zero-shot / few-shot +learning. However, many of such models come with a dauntingly huge size that +few institutions can afford to pre-train, fine-tune or even deploy, while +moderate-sized models usually lack strong generalized few-shot learning +capabilities. In this paper, we first elaborate the current obstacles of using +PLM models in terms of the Impossible Triangle: 1) moderate model size, 2) +state-of-the-art few-shot learning capability, and 3) state-of-the-art +fine-tuning capability. We argue that all existing PLM models lack one or more +properties from the Impossible Triangle. To remedy these missing properties of +PLMs, various techniques have been proposed, such as knowledge distillation, +data augmentation and prompt learning, which inevitably brings additional work +to the application of PLMs in real scenarios. We then offer insights into +future research directions of PLMs to achieve the Impossible Triangle, and +break down the task into several key phases. +" +A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems,Debjoy Saha,http://arxiv.org/pdf/2204.08167v1.pdf,2022-04-18,"['cs.cl', 'cs.ai']",2204.08167v1.pdf," We tackle the Dialogue Belief State Tracking(DST) problem of task-oriented +conversational systems. Recent approaches to this problem leveraging +Transformer-based models have yielded great results. However, training these +models is expensive, both in terms of computational resources and time. +Additionally, collecting high quality annotated dialogue datasets remains a +challenge for researchers because of the extensive annotation required for +training these models. Driven by the recent success of pre-trained language +models and prompt-based learning, we explore prompt-based few-shot learning for +Dialogue Belief State Tracking. We formulate the DST problem as a 2-stage +prompt-based language modelling task and train language models for both tasks +and present a comprehensive empirical analysis of their separate and joint +performance. We demonstrate the potential of prompt-based methods in few-shot +learning for DST and provide directions for future improvement. +" +How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models,Hai Dang,http://arxiv.org/pdf/2209.01390v1.pdf,2022-09-03,"['cs.hc', 'cs.cl', 'h.5.2; i.2.7']",2209.01390v1.pdf," Deep generative models have the potential to fundamentally change the way we +create high-fidelity digital content but are often hard to control. Prompting a +generative model is a promising recent development that in principle enables +end-users to creatively leverage zero-shot and few-shot learning to assign new +tasks to an AI ad-hoc, simply by writing them down. However, for the majority +of end-users writing effective prompts is currently largely a trial and error +process. To address this, we discuss the key opportunities and challenges for +interactive creative applications that use prompting as a new paradigm for +Human-AI interaction. Based on our analysis, we propose four design goals for +user interfaces that support prompting. We illustrate these with concrete UI +design sketches, focusing on the use case of creative writing. The research +community in HCI and AI can take these as starting points to develop adequate +user interfaces for models capable of zero- and few-shot learning. +" +On Measuring the Intrinsic Few-Shot Hardness of Datasets,Xinran Zhao,http://arxiv.org/pdf/2211.09113v1.pdf,2022-11-16,['cs.cl'],2211.09113v1.pdf," While advances in pre-training have led to dramatic improvements in few-shot +learning of NLP tasks, there is limited understanding of what drives successful +few-shot adaptation in datasets. In particular, given a new dataset and a +pre-trained model, what properties of the dataset make it \emph{few-shot +learnable} and are these properties independent of the specific adaptation +techniques used? We consider an extensive set of recent few-shot learning +methods, and show that their performance across a large number of datasets is +highly correlated, showing that few-shot hardness may be intrinsic to datasets, +for a given pre-trained model. To estimate intrinsic few-shot hardness, we then +propose a simple and lightweight metric called ""Spread"" that captures the +intuition that few-shot learning is made possible by exploiting feature-space +invariances between training and test samples. Our metric better accounts for +few-shot hardness compared to existing notions of hardness, and is ~8-100x +faster to compute. +" +Differentiable Entailment for Parameter Efficient Few Shot Learning,Ethan Kim,http://arxiv.org/pdf/2301.13345v1.pdf,2023-01-31,['cs.cl'],2301.13345v1.pdf," Few-shot learning allows pre-trained language models to adapt to downstream +tasks while using a limited number of training examples. However, practical +applications are limited when all model parameters must be optimized. In this +work we apply a new technique for parameter efficient few shot learning while +adopting a strict definition of parameter efficiency. Our training method +combines 1) intermediate training by reformulating natural language tasks as +entailment tasks \cite{wang_entailment_2021} and 2) differentiable optimization +of template and label tokens \cite{zhang_differentiable_2021}. We quantify the +tradeoff between parameter efficiency and performance in the few-shot regime +and propose a simple model agnostic approach that can be extended to any task +By achieving competitive performance while only optimizing 3\% of a model's +parameters and allowing for batched inference, we allow for more efficient +practical deployment of models. +" +MerA: Merging Pretrained Adapters For Few-Shot Learning,Shwai He,http://arxiv.org/pdf/2308.15982v1.pdf,2023-08-30,['cs.cl'],2308.15982v1.pdf," Adapter tuning, which updates only a few parameters, has become a mainstream +method for fine-tuning pretrained language models to downstream tasks. However, +it often yields subpar results in few-shot learning. AdapterFusion, which +assembles pretrained adapters using composition layers tailored to specific +tasks, is a possible solution but significantly increases trainable parameters +and deployment costs. Despite this, our preliminary study reveals that even +single adapters can outperform Adapterfusion in few-shot learning, urging us to +propose \textbf{\texttt{Merging Pretrained Adapters}} (MerA) that efficiently +incorporates pretrained adapters to a single model through model fusion. +Extensive experiments on two PLMs demonstrate that MerA achieves substantial +improvements compared to both single adapters and AdapterFusion. To further +enhance the capacity of MerA, we also introduce a simple yet effective +technique, referred to as the ""\textit{same-track}"" setting, that merges +adapters from the same track of pretraining tasks. With the implementation of +the ""\textit{same-track}"" setting, we observe even more impressive gains, +surpassing the performance of both full fine-tuning and adapter tuning by a +substantial margin, e.g., 3.5\% in MRPC and 5.0\% in MNLI. +" +Meta-Adapter: An Online Few-shot Learner for Vision-Language Model,Cheng Cheng,http://arxiv.org/pdf/2311.03774v1.pdf,2023-11-07,['cs.cv'],2311.03774v1.pdf," The contrastive vision-language pre-training, known as CLIP, demonstrates +remarkable potential in perceiving open-world visual concepts, enabling +effective zero-shot image recognition. Nevertheless, few-shot learning methods +based on CLIP typically require offline fine-tuning of the parameters on +few-shot samples, resulting in longer inference time and the risk of +over-fitting in certain domains. To tackle these challenges, we propose the +Meta-Adapter, a lightweight residual-style adapter, to refine the CLIP features +guided by the few-shot samples in an online manner. With a few training +samples, our method can enable effective few-shot learning capabilities and +generalize to unseen data or tasks without additional fine-tuning, achieving +competitive performance and high efficiency. Without bells and whistles, our +approach outperforms the state-of-the-art online few-shot learning method by an +average of 3.6\% on eight image classification datasets with higher inference +speed. Furthermore, our model is simple and flexible, serving as a +plug-and-play module directly applicable to downstream tasks. Without further +fine-tuning, Meta-Adapter obtains notable performance improvements in +open-vocabulary object detection and segmentation tasks. +" +Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference,Shell Xu Hu,http://arxiv.org/pdf/2204.07305v1.pdf,2022-04-15,"['cs.cv', 'cs.lg']",2204.07305v1.pdf," Few-shot learning (FSL) is an important and topical problem in computer +vision that has motivated extensive research into numerous methods spanning +from sophisticated meta-learning methods to simple transfer learning baselines. +We seek to push the limits of a simple-but-effective pipeline for more +realistic and practical settings of few-shot image classification. To this end, +we explore few-shot learning from the perspective of neural network +architecture, as well as a three stage pipeline of network updates under +different data supplies, where unsupervised external data is considered for +pre-training, base categories are used to simulate few-shot tasks for +meta-training, and the scarcely labelled data of an novel task is taken for +fine-tuning. We investigate questions such as: (1) How pre-training on external +data benefits FSL? (2) How state-of-the-art transformer architectures can be +exploited? and (3) How fine-tuning mitigates domain shift? Ultimately, we show +that a simple transformer-based pipeline yields surprisingly good performance +on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset. +Our code and demo are available at https://hushell.github.io/pmf. +" +"Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence",Markus Bayer,http://arxiv.org/pdf/2207.11076v1.pdf,2022-07-22,"['cs.cr', 'cs.cl']",2207.11076v1.pdf," Gathering cyber threat intelligence from open sources is becoming +increasingly important for maintaining and achieving a high level of security +as systems become larger and more complex. However, these open sources are +often subject to information overload. It is therefore useful to apply machine +learning models that condense the amount of information to what is necessary. +Yet, previous studies and applications have shown that existing classifiers are +not able to extract specific information about emerging cybersecurity events +due to their low generalization ability. Therefore, we propose a system to +overcome this problem by training a new classifier for each new incident. Since +this requires a lot of labelled data using standard training methods, we +combine three different low-data regime techniques - transfer learning, data +augmentation, and few-shot learning - to train a high-quality classifier from +very few labelled instances. We evaluated our approach using a novel dataset +derived from the Microsoft Exchange Server data breach of 2021 which was +labelled by three experts. Our findings reveal an increase in F1 score of more +than 21 points compared to standard training methods and more than 18 points +compared to a state-of-the-art method in few-shot learning. Furthermore, the +classifier trained with this method and 32 instances is only less than 5 F1 +score points worse than a classifier trained with 1800 instances. +" +Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning,Tianxiang Sun,http://arxiv.org/pdf/2210.07565v3.pdf,2022-10-14,['cs.cl'],2210.07565v3.pdf," Prompt tuning is a parameter-efficient approach to adapting pre-trained +language models to downstream tasks. Although prompt tuning has been shown to +match the performance of full model tuning when training data is sufficient, it +tends to struggle in few-shot learning settings. In this paper, we present +Multi-task Pre-trained Modular Prompt (MP2) to boost prompt tuning for few-shot +learning. MP2 is a set of combinable prompts pre-trained on 38 Chinese tasks. +On downstream tasks, the pre-trained prompts are selectively activated and +combined, leading to strong compositional generalization to unseen tasks. To +bridge the gap between pre-training and fine-tuning, we formulate upstream and +downstream tasks into a unified machine reading comprehension task. Extensive +experiments under two learning paradigms, i.e., gradient descent and black-box +tuning, show that MP2 significantly outperforms prompt tuning, full model +tuning, and prior prompt pre-training methods in few-shot settings. In +addition, we demonstrate that MP2 can achieve surprisingly fast and strong +adaptation to downstream tasks by merely learning 8 parameters to combine the +pre-trained modular prompts. +" +Few-shot Classification with Hypersphere Modeling of Prototypes,Ning Ding,http://arxiv.org/pdf/2211.05319v1.pdf,2022-11-10,"['cs.lg', 'cs.cl', 'cs.cv']",2211.05319v1.pdf," Metric-based meta-learning is one of the de facto standards in few-shot +learning. It composes of representation learning and metrics calculation +designs. Previous works construct class representations in different ways, +varying from mean output embedding to covariance and distributions. However, +using embeddings in space lacks expressivity and cannot capture class +information robustly, while statistical complex modeling poses difficulty to +metric designs. In this work, we use tensor fields (``areas'') to model classes +from the geometrical perspective for few-shot learning. We present a simple and +effective method, dubbed hypersphere prototypes (HyperProto), where class +information is represented by hyperspheres with dynamic sizes with two sets of +learnable parameters: the hypersphere's center and the radius. Extending from +points to areas, hyperspheres are much more expressive than embeddings. +Moreover, it is more convenient to perform metric-based classification with +hypersphere prototypes than statistical modeling, as we only need to calculate +the distance from a data point to the surface of the hypersphere. Following +this idea, we also develop two variants of prototypes under other measurements. +Extensive experiments and analysis on few-shot learning tasks across NLP and CV +and comparison with 20+ competitive baselines demonstrate the effectiveness of +our approach. +" +StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning,Yuqian Fu,http://arxiv.org/pdf/2302.09309v2.pdf,2023-02-18,['cs.cv'],2302.09309v2.pdf," Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that +tackles few-shot learning across different domains. It aims at transferring +prior knowledge learned on the source dataset to novel target datasets. The +CD-FSL task is especially challenged by the huge domain gap between different +datasets. Critically, such a domain gap actually comes from the changes of +visual styles, and wave-SAN empirically shows that spanning the style +distribution of the source data helps alleviate this issue. However, wave-SAN +simply swaps styles of two images. Such a vanilla operation makes the generated +styles ``real'' and ``easy'', which still fall into the original set of the +source styles. Thus, inspired by vanilla adversarial learning, a novel +model-agnostic meta Style Adversarial training (StyleAdv) method together with +a novel style adversarial attack method is proposed for CD-FSL. Particularly, +our style attack method synthesizes both ``virtual'' and ``hard'' adversarial +styles for model training. This is achieved by perturbing the original style +with the signed style gradients. By continually attacking styles and forcing +the model to recognize these challenging adversarial styles, our model is +gradually robust to the visual styles, thus boosting the generalization ability +for novel target datasets. Besides the typical CNN-based backbone, we also +employ our StyleAdv method on large-scale pretrained vision transformer. +Extensive experiments conducted on eight various target datasets show the +effectiveness of our method. Whether built upon ResNet or ViT, we achieve the +new state of the art for CD-FSL. Code is available at +https://github.com/lovelyqian/StyleAdv-CDFSL. +" +Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment,Runqi Wang,http://arxiv.org/pdf/2305.11439v1.pdf,2023-05-19,['cs.cv'],2305.11439v1.pdf," Pre-trained vision-language models have inspired much research on few-shot +learning. However, with only a few training images, there exist two crucial +problems: (1) the visual feature distributions are easily distracted by +class-irrelevant information in images, and (2) the alignment between the +visual and language feature distributions is difficult. To deal with the +distraction problem, we propose a Selective Attack module, which consists of +trainable adapters that generate spatial attention maps of images to guide the +attacks on class-irrelevant image areas. By messing up these areas, the +critical features are captured and the visual distributions of image features +are calibrated. To better align the visual and language feature distributions +that describe the same object class, we propose a cross-modal distribution +alignment module, in which we introduce a vision-language prototype for each +class to align the distributions, and adopt the Earth Mover's Distance (EMD) to +optimize the prototypes. For efficient computation, the upper bound of EMD is +derived. In addition, we propose an augmentation strategy to increase the +diversity of the images and the text prompts, which can reduce overfitting to +the few-shot training images. Extensive experiments on 11 datasets demonstrate +that our method consistently outperforms prior arts in few-shot learning. The +implementation code will be available at https://github.com/bhrqw/SADA. +" +Federated Few-shot Learning for Cough Classification with Edge Devices,Ngan Dao Hoang,http://arxiv.org/pdf/2309.01076v1.pdf,2023-09-03,"['cs.lg', 'cs.sd', 'eess.as']",2309.01076v1.pdf," Automatically classifying cough sounds is one of the most critical tasks for +the diagnosis and treatment of respiratory diseases. However, collecting a huge +amount of labeled cough dataset is challenging mainly due to high laborious +expenses, data scarcity, and privacy concerns. In this work, our aim is to +develop a framework that can effectively perform cough classification even in +situations when enormous cough data is not available, while also addressing +privacy concerns. Specifically, we formulate a new problem to tackle these +challenges and adopt few-shot learning and federated learning to design a novel +framework, termed F2LCough, for solving the newly formulated problem. We +illustrate the superiority of our method compared with other approaches on +COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average +F1-Score of 86%. Our results show the feasibility of few-shot learning combined +with federated learning to build a classification model of cough sounds. This +new methodology is able to classify cough sounds in data-scarce situations and +maintain privacy properties. The outcomes of this work can be a fundamental +framework for building support systems for the detection and diagnosis of +cough-related diseases. +" +Few-Shot Bot: Prompt-Based Learning for Dialogue Systems,Andrea Madotto,http://arxiv.org/pdf/2110.08118v1.pdf,2021-10-15,"['cs.cl', 'cs.ai']",2110.08118v1.pdf," Learning to converse using only a few examples is a great challenge in +conversational AI. The current best conversational models, which are either +good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL), +are language models (LMs) fine-tuned on large conversational datasets. Training +these models is expensive, both in terms of computational resources and time, +and it is hard to keep them up to date with new conversational skills. A simple +yet unexplored solution is prompt-based few-shot learning (Brown et al. 2020) +which does not require gradient-based fine-tuning but instead uses a few +examples in the LM context as the only source of learning. In this paper, we +explore prompt-based few-shot learning in dialogue tasks. We benchmark LMs of +different sizes in nine response generation tasks, which include four +knowledge-grounded tasks, a task-oriented generations task, three open-chat +tasks, and controlled stylistic generation, and five conversational parsing +tasks, which include dialogue state tracking, graph path generation, persona +information extraction, document retrieval, and internet query generation. The +current largest released LM (GPT-J-6B) using prompt-based few-shot learning, +and thus requiring no training, achieves competitive performance to fully +trained state-of-the-art models. Moreover, we propose a novel prompt-based +few-shot classifier, that also does not require any fine-tuning, to select the +most appropriate prompt given a dialogue history. Finally, by combining the +power of prompt-based few-shot learning and a Skill Selector, we create an +end-to-end chatbot named the Few-Shot Bot (FSB), which automatically selects +the most appropriate conversational skill, queries different knowledge bases or +the internet, and uses the retrieved knowledge to generate a human-like +response, all using only few dialogue examples per skill. +" +"A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level",Iddo Drori,http://arxiv.org/pdf/2112.15594v4.pdf,2021-12-31,"['cs.lg', 'cs.ai']",2112.15594v4.pdf," We demonstrate that a neural network pre-trained on text and fine-tuned on +code solves mathematics course problems, explains solutions, and generates new +questions at a human level. We automatically synthesize programs using few-shot +learning and OpenAI's Codex transformer and execute them to solve course +problems at 81% automatic accuracy. We curate a new dataset of questions from +MIT's largest mathematics courses (Single Variable and Multivariable Calculus, +Differential Equations, Introduction to Probability and Statistics, Linear +Algebra, and Mathematics for Computer Science) and Columbia University's +Computational Linear Algebra. We solve questions from a MATH dataset (on +Prealgebra, Algebra, Counting and Probability, Intermediate Algebra, Number +Theory, and Precalculus), the latest benchmark of advanced mathematics problems +designed to assess mathematical reasoning. We randomly sample questions and +generate solutions with multiple modalities, including numbers, equations, and +plots. The latest GPT-3 language model pre-trained on text automatically solves +only 18.8% of these university questions using zero-shot learning and 30.8% +using few-shot learning and the most recent chain of thought prompting. In +contrast, program synthesis with few-shot learning using Codex fine-tuned on +code generates programs that automatically solve 81% of these questions. Our +approach improves the previous state-of-the-art automatic solution accuracy on +the benchmark topics from 8.8% to 81.1%. We perform a survey to evaluate the +quality and difficulty of generated questions. This work is the first to +automatically solve university-level mathematics course questions at a human +level and the first work to explain and generate university-level mathematics +course questions at scale, a milestone for higher education. +" +Is Support Set Diversity Necessary for Meta-Learning?,Amrith Setlur,http://arxiv.org/pdf/2011.14048v2.pdf,2020-11-28,"['cs.lg', 'stat.ml']",2011.14048v2.pdf," Meta-learning is a popular framework for learning with limited data in which +an algorithm is produced by training over multiple few-shot learning tasks. For +classification problems, these tasks are typically constructed by sampling a +small number of support and query examples from a subset of the classes. While +conventional wisdom is that task diversity should improve the performance of +meta-learning, in this work we find evidence to the contrary: we propose a +modification to traditional meta-learning approaches in which we keep the +support sets fixed across tasks, thus reducing task diversity. Surprisingly, we +find that not only does this modification not result in adverse effects, it +almost always improves the performance for a variety of datasets and +meta-learning methods. We also provide several initial analyses to understand +this phenomenon. Our work serves to: (i) more closely investigate the effect of +support set construction for the problem of meta-learning, and (ii) suggest a +simple, general, and competitive baseline for few-shot learning. +" +Detecting Hate Speech with GPT-3,Ke-Li Chiu,http://arxiv.org/pdf/2103.12407v4.pdf,2021-03-23,['cs.cl'],2103.12407v4.pdf," Sophisticated language models such as OpenAI's GPT-3 can generate hateful +text that targets marginalized groups. Given this capacity, we are interested +in whether large language models can be used to identify hate speech and +classify text as sexist or racist. We use GPT-3 to identify sexist and racist +text passages with zero-, one-, and few-shot learning. We find that with zero- +and one-shot learning, GPT-3 can identify sexist or racist text with an average +accuracy between 55 per cent and 67 per cent, depending on the category of text +and type of learning. With few-shot learning, the model's accuracy can be as +high as 85 per cent. Large language models have a role to play in hate speech +detection, and with further development they could eventually be used to +counter hate speech. +" +CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP,Qinyuan Ye,http://arxiv.org/pdf/2104.08835v2.pdf,2021-04-18,"['cs.cl', 'cs.lg']",2104.08835v2.pdf," Humans can learn a new language task efficiently with only few examples, by +leveraging their knowledge obtained when learning prior tasks. In this paper, +we explore whether and how such cross-task generalization ability can be +acquired, and further applied to build better few-shot learners across diverse +NLP tasks. We introduce CrossFit, a problem setup for studying cross-task +generalization ability, which standardizes seen/unseen task partitions, data +access during different learning stages, and the evaluation protocols. To +instantiate different seen/unseen task partitions in CrossFit and facilitate +in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse +few-shot NLP tasks created from open-access NLP datasets and converted to a +unified text-to-text format. Our analysis reveals that the few-shot learning +ability on unseen tasks can be improved via an upstream learning stage using a +set of seen tasks. We also observe that the selection of upstream learning +tasks can significantly influence few-shot performance on unseen tasks, asking +further analysis on task similarity and transferability. +" +Entailment as Few-Shot Learner,Sinong Wang,http://arxiv.org/pdf/2104.14690v1.pdf,2021-04-29,"['cs.cl', 'cs.ai']",2104.14690v1.pdf," Large pre-trained language models (LMs) have demonstrated remarkable ability +as few-shot learners. However, their success hinges largely on scaling model +parameters to a degree that makes it challenging to train and serve. In this +paper, we propose a new approach, named as EFL, that can turn small LMs into +better few-shot learners. The key idea of this approach is to reformulate +potential NLP task into an entailment one, and then fine-tune the model with as +little as 8 examples. We further demonstrate our proposed method can be: (i) +naturally combined with an unsupervised contrastive learning-based data +augmentation method; (ii) easily extended to multilingual few-shot learning. A +systematic evaluation on 18 standard NLP tasks demonstrates that this approach +improves the various existing SOTA few-shot learning methods by 12\%, and +yields competitive few-shot performance with 500 times larger models, such as +GPT-3. +" +True Few-Shot Learning with Language Models,Ethan Perez,http://arxiv.org/pdf/2105.11447v1.pdf,2021-05-24,"['cs.cl', 'cs.lg', 'stat.ml']",2105.11447v1.pdf," Pretrained language models (LMs) perform well on many tasks even when +learning from a few examples, but prior work uses many held-out examples to +tune various aspects of learning, such as hyperparameters, training objectives, +and natural language templates (""prompts""). Here, we evaluate the few-shot +ability of LMs when such held-out examples are unavailable, a setting we call +true few-shot learning. We test two model selection criteria, cross-validation +and minimum description length, for choosing LM prompts and hyperparameters in +the true few-shot setting. On average, both marginally outperform random +selection and greatly underperform selection based on held-out examples. +Moreover, selection criteria often prefer models that perform significantly +worse than randomly-selected ones. We find similar results even when taking +into account our uncertainty in a model's true performance during selection, as +well as when varying the amount of computation and number of examples used for +selection. Overall, our findings suggest that prior work significantly +overestimated the true few-shot ability of LMs given the difficulty of few-shot +model selection. +" +"Generate, Annotate, and Learn: NLP with Synthetic Text",Xuanli He,http://arxiv.org/pdf/2106.06168v3.pdf,2021-06-11,['cs.lg'],2106.06168v3.pdf," This paper studies the use of language models as a source of synthetic +unlabeled text for NLP. We formulate a general framework called ``generate, +annotate, and learn (GAL)'' to take advantage of synthetic text within +knowledge distillation, self-training, and few-shot learning applications. To +generate high-quality task-specific text, we either fine-tune LMs on inputs +from the task of interest, or prompt large LMs with few examples. We use the +best available classifier to annotate synthetic text with soft pseudo labels +for knowledge distillation and self-training, and use LMs to obtain hard labels +for few-shot learning. We train new supervised models on the combination of +labeled and pseudo-labeled data, which results in significant gains across +several applications. We investigate key components of GAL and present +theoretical and empirical arguments against the use of class-conditional LMs to +generate synthetic labeled text instead of unlabeled text. GAL achieves new +state-of-the-art knowledge distillation results for 6-layer transformers on the +GLUE leaderboard. +" +Multimodal Few-Shot Learning with Frozen Language Models,Maria Tsimpoukelli,http://arxiv.org/pdf/2106.13884v2.pdf,2021-06-25,"['cs.cv', 'cs.cl', 'cs.lg']",2106.13884v2.pdf," When trained at sufficient scale, auto-regressive language models exhibit the +notable ability to learn a new language task after being prompted with just a +few examples. Here, we present a simple, yet effective, approach for +transferring this few-shot learning ability to a multimodal setting (vision and +language). Using aligned image and caption data, we train a vision encoder to +represent each image as a sequence of continuous embeddings, such that a +pre-trained, frozen language model prompted with this prefix generates the +appropriate caption. The resulting system is a multimodal few-shot learner, +with the surprising ability to learn a variety of new tasks when conditioned on +examples, represented as a sequence of multiple interleaved image and text +embeddings. We demonstrate that it can rapidly learn words for new objects and +novel visual categories, do visual question-answering with only a handful of +examples, and make use of outside knowledge, by measuring a single model on a +variety of established and new benchmarks. +" +Revisiting Self-Training for Few-Shot Learning of Language Model,Yiming Chen,http://arxiv.org/pdf/2110.01256v1.pdf,2021-10-04,['cs.cl'],2110.01256v1.pdf," As unlabeled data carry rich task-relevant information, they are proven +useful for few-shot learning of language model. The question is how to +effectively make use of such data. In this work, we revisit the self-training +technique for language model fine-tuning and present a state-of-the-art +prompt-based few-shot learner, SFLM. Given two views of a text sample via weak +and strong augmentation techniques, SFLM generates a pseudo label on the weakly +augmented version. Then, the model predicts the same pseudo label when +fine-tuned with the strongly augmented version. This simple approach is shown +to outperform other state-of-the-art supervised and semi-supervised +counterparts on six sentence classification and six sentence-pair +classification benchmarking tasks. In addition, SFLM only relies on a few +in-domain unlabeled data. We conduct a comprehensive analysis to demonstrate +the robustness of our proposed approach under various settings, including +augmentation techniques, model scale, and few-shot knowledge transfer across +tasks. +" +In-Context Learning for Few-Shot Dialogue State Tracking,Yushi Hu,http://arxiv.org/pdf/2203.08568v3.pdf,2022-03-16,['cs.cl'],2203.08568v3.pdf," Collecting and annotating task-oriented dialogues is time-consuming and +costly; thus, zero and few shot learning could greatly benefit dialogue state +tracking (DST). In this work, we propose an in-context learning (ICL) framework +for zero-shot and few-shot learning DST, where a large pre-trained language +model (LM) takes a test instance and a few exemplars as input, and directly +decodes the dialogue state without any parameter updates. To better leverage a +tabular domain description in the LM prompt, we reformulate DST into a +text-to-SQL problem. We also propose a novel approach to retrieve annotated +dialogues as exemplars. Empirical results on MultiWOZ show that our method +IC-DST substantially outperforms previous fine-tuned state-of-the-art models in +few-shot settings. In addition, we test IC-DST in zero-shot settings, in which +the model only takes a fixed task instruction as input, finding that it +outperforms previous zero-shot methods by a large margin. +" +WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models,Heting Gao,http://arxiv.org/pdf/2203.15863v2.pdf,2022-03-29,"['eess.as', 'cs.ai', 'cs.cl']",2203.15863v2.pdf," Large-scale auto-regressive language models pretrained on massive text have +demonstrated their impressive ability to perform new natural language tasks +with only a few text examples, without the need for fine-tuning. Recent studies +further show that such a few-shot learning ability can be extended to the +text-image setting by training an encoder to encode the images into embeddings +functioning like the text embeddings of the language model. Interested in +exploring the possibility of transferring the few-shot learning ability to the +audio-text setting, we propose a novel speech understanding framework, +WavPrompt, where we finetune a wav2vec model to generate a sequence of audio +embeddings understood by the language model. We show that WavPrompt is a +few-shot learner that can perform speech understanding tasks better than a +naive text baseline. We conduct detailed ablation studies on different +components and hyperparameters to empirically identify the best model +configuration. In addition, we conduct a non-speech understanding experiment to +show WavPrompt can extract more information than just the transcriptions. Code +is available at https://github.com/Hertin/WavPrompt +" +Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values,Yejin Bang,http://arxiv.org/pdf/2210.07652v1.pdf,2022-10-14,"['cs.cl', 'cs.ai']",2210.07652v1.pdf," Many NLP classification tasks, such as sexism/racism detection or toxicity +detection, are based on human values. Yet, human values can vary under diverse +cultural conditions. Therefore, we introduce a framework for value-aligned +classification that performs prediction based on explicitly written human +values in the command. Along with the task, we propose a practical approach +that distills value-aligned knowledge from large-scale language models (LLMs) +to construct value-aligned classifiers in two steps. First, we generate +value-aligned training data from LLMs by prompt-based few-shot learning. Next, +we fine-tune smaller classification models with the generated data for the +task. Empirical results show that our VA-Models surpass multiple baselines by +at least 15.56% on the F1-score, including few-shot learning with OPT-175B and +existing text augmentation methods. We suggest that using classifiers with +explicit human value input improves both inclusivity & explainability in AI. +" +Aligning MAGMA by Few-Shot Learning and Finetuning,Jean-Charles Layoun,http://arxiv.org/pdf/2210.14161v1.pdf,2022-10-18,"['cs.cv', 'cs.ai']",2210.14161v1.pdf," The goal of vision-language modeling is to allow models to tie language +understanding with visual inputs. The aim of this paper is to evaluate and +align the Visual Language Model (VLM) called Multimodal Augmentation of +Generative Models through Adapter-based finetuning (MAGMA) with human values. +MAGMA is a VLM that is capable of image captioning and visual +question-answering. We will evaluate its alignment in three different +scenarios. To begin, we assess MAGMA's out-of-the-box alignment through the +checkpoint provided by Hugging Face. Then, we measure if few-shot learning +manages to improve the results. Finally, we finetune the model on aligned +examples and evaluate its behavior. +" +GPS: Genetic Prompt Search for Efficient Few-shot Learning,Hanwei Xu,http://arxiv.org/pdf/2210.17041v1.pdf,2022-10-31,['cs.cl'],2210.17041v1.pdf," Prompt-based techniques have demostrated great potential for improving the +few-shot generalization of pretrained language models. However, their +performance heavily relies on the manual design of prompts and thus requires a +lot of human efforts. In this paper, we introduce Genetic Prompt Search (GPS) +to improve few-shot learning with prompts, which utilizes a genetic algorithm +to automatically search for high-performing prompts. GPS is gradient-free and +requires no update of model parameters but only a small validation set. +Experiments on diverse datasets proved the effectiveness of GPS, which +outperforms manual prompts by a large margin of 2.6 points. Our method is also +better than other parameter-efficient tuning methods such as prompt tuning. +" +MEAL: Stable and Active Learning for Few-Shot Prompting,Abdullatif Köksal,http://arxiv.org/pdf/2211.08358v2.pdf,2022-11-15,['cs.cl'],2211.08358v2.pdf," Few-shot classification has made great strides due to foundation models that, +through priming and prompting, are highly effective few-shot learners. However, +this approach has high variance both across different sets of few shots (data +selection) and across different finetuning runs (run variability). This is +problematic not only because it impedes the fair comparison of different +approaches, but especially because it makes few-shot learning too unreliable +for many real-world applications. To alleviate these issues, we make two +contributions for more stable and effective few-shot learning: First, we +propose novel ensembling methods and show that they substantially reduce run +variability. Second, we introduce a new active learning (AL) criterion for data +selection and present the first AL-based approach specifically tailored towards +prompt-based learning. In our experiments, we show that our combined method, +MEAL (Multiprompt finetuning and prediction Ensembling with Active Learning), +improves overall performance of prompt-based finetuning by 2.3 points on five +diverse tasks. +" +Few-shot Query-Focused Summarization with Prefix-Merging,Ruifeng Yuan,http://arxiv.org/pdf/2211.16164v1.pdf,2022-11-29,"['cs.cl', 'cs.ai']",2211.16164v1.pdf," Query-focused summarization has been considered as an important extension for +text summarization. It aims to generate a concise highlight for a given query. +Different from text summarization, query-focused summarization has long been +plagued by the problem of lacking high-quality large-scale datasets. In this +paper, we investigate the idea that whether we can integrate and transfer the +knowledge of text summarization and question answering to assist the few-shot +learning in query-focused summarization. Here, we propose prefix-merging, a +prefix-based pretraining strategy for few-shot learning in query-focused +summarization. Drawn inspiration from prefix-tuning, we are allowed to +integrate the task knowledge from text summarization and question answering +into a properly designed prefix and apply the merged prefix to query-focused +summarization. With only a small amount of trainable parameters, prefix-merging +outperforms fine-tuning on query-focused summarization. We further discuss the +influence of different prefix designs and propose a visualized explanation for +how prefix-merging works. +" +JASMINE: Arabic GPT Models for Few-Shot Learning,El Moatez Billah Nagoudi,http://arxiv.org/pdf/2212.10755v2.pdf,2022-12-21,['cs.cl'],2212.10755v2.pdf," Scholarship on generative pretraining (GPT) remains acutely Anglocentric, +leaving serious gaps in our understanding of the whole class of autoregressive +models. For example, we have little knowledge about the potential of these +models and their societal impacts in diverse linguistic and cultural settings. +We alleviate this issue for Arabic, a wide collection of languages and +dialectal varieties with more than 400 million population, by introducing +JASMINE. JASMINE is a suite of powerful Arabic autoregressive Transformer +language models ranging in size between 300 million-6.7 billion parameters +pretrained on a large and diverse dataset (~ 235 GB of text). We also carefully +design and release a comprehensive benchmark for both automated and human +evaluation of Arabic autoregressive models, with coverage of potential social +biases, harms, and toxicity. Using our novel benchmark, we evaluate JASMINE +extensively showing powerful performance intrinsically as well as in few-shot +learning on a wide range of NLP tasks. We aim to responsibly release our models +and evaluation benchmark with interested researchers, along with code for +experimenting with them. +" +Log Parsing with Prompt-based Few-shot Learning,Van-Hoang Le,http://arxiv.org/pdf/2302.07435v1.pdf,2023-02-15,['cs.se'],2302.07435v1.pdf," Logs generated by large-scale software systems provide crucial information +for engineers to understand the system status and diagnose problems of the +systems. Log parsing, which converts raw log messages into structured data, is +the first step to enabling automated log analytics. Existing log parsers +extract the common part as log templates using statistical features. However, +these log parsers often fail to identify the correct templates and parameters +because: 1) they often overlook the semantic meaning of log messages, and 2) +they require domain-specific knowledge for different log datasets. To address +the limitations of existing methods, in this paper, we propose LogPPT to +capture the patterns of templates using prompt-based few-shot learning. LogPPT +utilises a novel prompt tuning method to recognise keywords and parameters +based on a few labelled log data. In addition, an adaptive random sampling +algorithm is designed to select a small yet diverse training set. We have +conducted extensive experiments on 16 public log datasets. The experimental +results show that LogPPT is effective and efficient for log parsing. +" +Conversation Style Transfer using Few-Shot Learning,Shamik Roy,http://arxiv.org/pdf/2302.08362v2.pdf,2023-02-16,['cs.cl'],2302.08362v2.pdf," Conventional text style transfer approaches focus on sentence-level style +transfer without considering contextual information, and the style is described +with attributes (e.g., formality). When applying style transfer in +conversations such as task-oriented dialogues, existing approaches suffer from +these limitations as context can play an important role and the style +attributes are often difficult to define in conversations. In this paper, we +introduce conversation style transfer as a few-shot learning problem, where the +model learns to perform style transfer by observing only a few example +dialogues in the target style. We propose a novel in-context learning approach +to solve the task with style-free dialogues as a pivot. Human evaluation shows +that by incorporating multi-turn context, the model is able to match the target +style while having better appropriateness and semantic correctness compared to +utterance/sentence-level style transfer. Additionally, we show that +conversation style transfer can also benefit downstream tasks. For example, in +multi-domain intent classification tasks, the F1 scores improve after +transferring the style of training data to match the style of the test data. +" +STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables,Jaehyun Nam,http://arxiv.org/pdf/2303.00918v1.pdf,2023-03-02,"['cs.lg', 'cs.ai']",2303.00918v1.pdf," Learning with few labeled tabular samples is often an essential requirement +for industrial machine learning applications as varieties of tabular data +suffer from high annotation costs or have difficulties in collecting new +samples for novel tasks. Despite the utter importance, such a problem is quite +under-explored in the field of tabular learning, and existing few-shot learning +schemes from other domains are not straightforward to apply, mainly due to the +heterogeneous characteristics of tabular data. In this paper, we propose a +simple yet effective framework for few-shot semi-supervised tabular learning, +coined Self-generated Tasks from UNlabeled Tables (STUNT). Our key idea is to +self-generate diverse few-shot tasks by treating randomly chosen columns as a +target label. We then employ a meta-learning scheme to learn generalizable +knowledge with the constructed tasks. Moreover, we introduce an unsupervised +validation scheme for hyperparameter search (and early stopping) by generating +a pseudo-validation set using STUNT from unlabeled data. Our experimental +results demonstrate that our simple framework brings significant performance +gain under various tabular few-shot learning benchmarks, compared to prior +semi- and self-supervised baselines. Code is available at +https://github.com/jaehyun513/STUNT. +" +CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models,Tianhao Li,http://arxiv.org/pdf/2304.10946v1.pdf,2023-04-18,"['cs.cl', 'cs.lg', 'q-bio.bm']",2304.10946v1.pdf," Large pre-trained language models (LLMs) have been shown to have significant +potential in few-shot learning across various fields, even with minimal +training data. However, their ability to generalize to unseen tasks in more +complex fields, such as biology, has yet to be fully evaluated. LLMs can offer +a promising alternative approach for biological inference, particularly in +cases where structured data and sample size are limited, by extracting prior +knowledge from text corpora. Our proposed few-shot learning approach uses LLMs +to predict the synergy of drug pairs in rare tissues that lack structured data +and features. Our experiments, which involved seven rare tissues from different +cancer types, demonstrated that the LLM-based prediction model achieved +significant accuracy with very few or zero samples. Our proposed model, the +CancerGPT (with $\sim$ 124M parameters), was even comparable to the larger +fine-tuned GPT-3 model (with $\sim$ 175B parameters). Our research is the first +to tackle drug pair synergy prediction in rare tissues with limited data. We +are also the first to utilize an LLM-based prediction model for biological +reaction prediction tasks. +" +Automated Few-shot Classification with Instruction-Finetuned Language Models,Rami Aly,http://arxiv.org/pdf/2305.12576v2.pdf,2023-05-21,['cs.cl'],2305.12576v2.pdf," A particularly successful class of approaches for few-shot learning combines +language models with prompts -- hand-crafted task descriptions that complement +data samples. However, designing prompts by hand for each task commonly +requires domain knowledge and substantial guesswork. We observe, in the context +of classification tasks, that instruction finetuned language models exhibit +remarkable prompt robustness, and we subsequently propose a simple method to +eliminate the need for handcrafted prompts, named AuT-Few. This approach +consists of (i) a prompt retrieval module that selects suitable task +instructions from the instruction-tuning knowledge base, and (ii) the +generation of two distinct, semantically meaningful, class descriptions and a +selection mechanism via cross-validation. Over $12$ datasets, spanning $8$ +classification tasks, we show that AuT-Few outperforms current state-of-the-art +few-shot learning methods. Moreover, AuT-Few is the best ranking method across +datasets on the RAFT few-shot benchmark. Notably, these results are achieved +without task-specific handcrafted prompts on unseen tasks. +" +Active Learning Principles for In-Context Learning with Large Language Models,Katerina Margatina,http://arxiv.org/pdf/2305.14264v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14264v1.pdf," The remarkable advancements in large language models (LLMs) have +significantly enhanced the performance in few-shot learning settings. By using +only a small number of labeled examples, referred to as demonstrations, LLMs +can effectively grasp the task at hand through in-context learning. However, +the process of selecting appropriate demonstrations has received limited +attention in prior work. This paper addresses the issue of identifying the most +informative demonstrations for few-shot learning by approaching it as a +pool-based Active Learning (AL) problem over a single iteration. Our objective +is to investigate how AL algorithms can serve as effective demonstration +selection methods for in-context learning. We compare various standard AL +algorithms based on uncertainty, diversity, and similarity, and consistently +observe that the latter outperforms all other methods, including random +sampling. Notably, uncertainty sampling, despite its success in conventional +supervised learning scenarios, performs poorly in this context. Our extensive +experimentation involving a diverse range of GPT and OPT models across $24$ +classification and multi-choice tasks, coupled with thorough analysis, +unambiguously demonstrates that in-context example selection through AL +prioritizes high-quality examples that exhibit low uncertainty and bear +similarity to the test examples. +" +Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts,Mohna Chakraborty,http://arxiv.org/pdf/2305.15689v2.pdf,2023-05-25,"['cs.cl', 'cs.ai']",2305.15689v2.pdf," Recent studies have demonstrated that natural-language prompts can help to +leverage the knowledge learned by pre-trained language models for the binary +sentence-level sentiment classification task. Specifically, these methods +utilize few-shot learning settings to fine-tune the sentiment classification +model using manual or automatically generated prompts. However, the performance +of these methods is sensitive to the perturbations of the utilized prompts. +Furthermore, these methods depend on a few labeled instances for automatic +prompt generation and prompt ranking. This study aims to find high-quality +prompts for the given task in a zero-shot setting. Given a base prompt, our +proposed approach automatically generates multiple prompts similar to the base +prompt employing positional, reasoning, and paraphrasing techniques and then +ranks the prompts using a novel metric. We empirically demonstrate that the +top-ranked prompts are high-quality and significantly outperform the base +prompt and the prompts generated using few-shot learning for the binary +sentence-level sentiment classification task. +" +FLamE: Few-shot Learning from Natural Language Explanations,Yangqiaoyu Zhou,http://arxiv.org/pdf/2306.08042v1.pdf,2023-06-13,"['cs.cl', 'cs.ai']",2306.08042v1.pdf," Natural language explanations have the potential to provide rich information +that in principle guides model reasoning. Yet, recent work by Lampinen et al. +(2022) has shown limited utility of natural language explanations in improving +classification. To effectively learn from explanations, we present FLamE, a +two-stage few-shot learning framework that first generates explanations using +GPT-3, and then finetunes a smaller model (e.g., RoBERTa) with generated +explanations. Our experiments on natural language inference demonstrate +effectiveness over strong baselines, increasing accuracy by 17.6% over GPT-3 +Babbage and 5.7% over GPT-3 Davinci in e-SNLI. Despite improving classification +performance, human evaluation surprisingly reveals that the majority of +generated explanations does not adequately justify classification decisions. +Additional analyses point to the important role of label-specific cues (e.g., +""not know"" for the neutral label) in generated explanations. +" +Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners,Jihyeon Lee,http://arxiv.org/pdf/2307.14856v1.pdf,2023-07-27,"['cs.cl', 'cs.ai']",2307.14856v1.pdf," In-context learning, which offers substantial advantages over fine-tuning, is +predominantly observed in decoder-only models, while encoder-decoder (i.e., +seq2seq) models excel in methods that rely on weight updates. Recently, a few +studies have demonstrated the feasibility of few-shot learning with seq2seq +models; however, this has been limited to tasks that align well with the +seq2seq architecture, such as summarization and translation. Inspired by these +initial studies, we provide a first-ever extensive experiment comparing the +in-context few-shot learning capabilities of decoder-only and encoder-decoder +models on a broad range of tasks. Furthermore, we propose two methods to more +effectively elicit in-context learning ability in seq2seq models: +objective-aligned prompting and a fusion-based approach. Remarkably, our +approach outperforms a decoder-only model that is six times larger and exhibits +significant performance improvements compared to conventional seq2seq models +across a variety of settings. We posit that, with the right configuration and +prompt design, seq2seq models can be highly effective few-shot learners for a +wide spectrum of applications. +" +Prototypes-oriented Transductive Few-shot Learning with Conditional Transport,Long Tian,http://arxiv.org/pdf/2308.03047v1.pdf,2023-08-06,['cs.cv'],2308.03047v1.pdf," Transductive Few-Shot Learning (TFSL) has recently attracted increasing +attention since it typically outperforms its inductive peer by leveraging +statistics of query samples. However, previous TFSL methods usually encode +uniform prior that all the classes within query samples are equally likely, +which is biased in imbalanced TFSL and causes severe performance degradation. + Given this pivotal issue, in this work, we propose a novel Conditional +Transport (CT) based imbalanced TFSL model called {\textbf P}rototypes-oriented +{\textbf U}nbiased {\textbf T}ransfer {\textbf M}odel (PUTM) to fully exploit +unbiased statistics of imbalanced query samples, which employs forward and +backward navigators as transport matrices to balance the prior of query samples +per class between uniform and adaptive data-driven distributions. For +efficiently transferring statistics learned by CT, we further derive a closed +form solution to refine prototypes based on MAP given the learned navigators. +The above two steps of discovering and transferring unbiased statistics follow +an iterative manner, formulating our EM-based solver. + Experimental results on four standard benchmarks including miniImageNet, +tieredImageNet, CUB, and CIFAR-FS demonstrate superiority of our model in +class-imbalanced generalization. +" +Approximating Human-Like Few-shot Learning with GPT-based Compression,Cynthia Huang,http://arxiv.org/pdf/2308.06942v1.pdf,2023-08-14,"['cs.ai', 'cs.cl', 'cs.it', 'math.it']",2308.06942v1.pdf," In this work, we conceptualize the learning process as information +compression. We seek to equip generative pre-trained models with human-like +learning capabilities that enable data compression during inference. We present +a novel approach that utilizes the Generative Pre-trained Transformer (GPT) to +approximate Kolmogorov complexity, with the aim of estimating the optimal +Information Distance for few-shot learning. We first propose using GPT as a +prior for lossless text compression, achieving a noteworthy compression ratio. +Experiment with LLAMA2-7B backbone achieves a compression ratio of 15.5 on +enwik9. We justify the pre-training objective of GPT models by demonstrating +its equivalence to the compression length, and, consequently, its ability to +approximate the information distance for texts. Leveraging the approximated +information distance, our method allows the direct application of GPT models in +quantitative text similarity measurements. Experiment results show that our +method overall achieves superior performance compared to embedding and prompt +baselines on challenging NLP tasks, including semantic similarity, zero and +one-shot text classification, and zero-shot text ranking. +" +COCA: Classifier-Oriented Calibration for Source-Free Universal Domain Adaptation via Textual Prototype,Xinghong Liu,http://arxiv.org/pdf/2308.10450v1.pdf,2023-08-21,['cs.cv'],2308.10450v1.pdf," Universal Domain Adaptation (UniDA) aims to distinguish common and private +classes between the source and target domains where domain shift exists. +Recently, due to more stringent data restrictions, researchers have introduced +Source-Free UniDA (SF-UniDA) in more realistic scenarios. SF-UniDA methods +eliminate the need for direct access to source samples when performing +adaptation to the target domain. However, existing SF-UniDA methods still +require an extensive quantity of labeled source samples to train a source +model, resulting in significant labeling costs. To tackle this issue, we +present a novel Classifier-Oriented Calibration (COCA) method. This method, +which leverages textual prototypes, is formulated for the source model based on +few-shot learning. Specifically, we propose studying few-shot learning, usually +explored for closed-set scenarios, to identify common and domain-private +classes despite a significant domain shift between source and target domains. +Essentially, we present a novel paradigm based on the vision-language model to +learn SF-UniDA and hugely reduce the labeling costs on the source domain. +Experimental results demonstrate that our approach outperforms state-of-the-art +UniDA and SF-UniDA models. +" +Evaluating the Decency and Consistency of Data Validation Tests Generated by LLMs,Rohan Alexander,http://arxiv.org/pdf/2310.01402v1.pdf,2023-10-02,['stat.me'],2310.01402v1.pdf," We investigated the potential of large language models (LLMs) in developing +dataset validation tests. We carried out 96 experiments each for both GPT-3.5 +and GPT-4, examining different prompt scenarios, learning modes, temperature +settings, and roles. The prompt scenarios were: 1) Asking for expectations, 2) +Asking for expectations with a given context, 3) Asking for expectations after +requesting a simulation, and 4) Asking for expectations with a provided data +sample. For learning modes, we tested: 1) zero-shot, 2) one-shot, and 3) +few-shot learning. We also tested four temperature settings: 0, 0.4, 0.6, and +1. Furthermore, two distinct roles were considered: 1) ""helpful assistant"", 2) +""expert data scientist"". To gauge consistency, every setup was tested five +times. The LLM-generated responses were benchmarked against a gold standard +suite, created by an experienced data scientist knowledgeable about the data in +question. We find there are considerable returns to the use of few-shot +learning, and that the more explicit the data setting can be the better. The +best LLM configurations complement, rather than substitute, the gold standard +results. This study underscores the value LLMs can bring to the data cleaning +and preparation stages of the data science workflow. +" +Improving generalization in large language models by learning prefix subspaces,Louis Falissard,http://arxiv.org/pdf/2310.15793v1.pdf,2023-10-24,"['cs.lg', 'cs.ai', 'cs.cl']",2310.15793v1.pdf," This article focuses on large language models (LLMs) fine-tuning in the +scarce data regime (also known as the ""few-shot"" learning setting). We propose +a method to increase the generalization capabilities of LLMs based on neural +network subspaces. This optimization method, recently introduced in computer +vision, aims to improve model generalization by identifying wider local optima +through the joint optimization of an entire simplex of models in parameter +space. Its adaptation to massive, pretrained transformers, however, poses some +challenges. First, their considerable number of parameters makes it difficult +to train several models jointly, and second, their deterministic parameter +initialization schemes make them unfit for the subspace method as originally +proposed. We show in this paper that ""Parameter Efficient Fine-Tuning"" (PEFT) +methods, however, are perfectly compatible with this original approach, and +propose to learn entire simplex of continuous prefixes. We test our method on a +variant of the GLUE benchmark adapted to the few-shot learning setting, and +show that both our contributions jointly lead to a gain in average performances +compared to sota methods. The implementation can be found at the following +link: https://github.com/Liloulou/prefix_subspace +" +Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey,Jiaoyan Chen,http://arxiv.org/pdf/2112.10006v6.pdf,2021-12-18,"['cs.lg', 'cs.ai']",2112.10006v6.pdf," Machine learning especially deep neural networks have achieved great success +but many of them often rely on a number of labeled samples for supervision. As +sufficient labeled training data are not always ready due to e.g., continuously +emerging prediction targets and costly sample annotation in real world +applications, machine learning with sample shortage is now being widely +investigated. Among all these studies, many prefer to utilize auxiliary +information including those in the form of Knowledge Graph (KG) to reduce the +reliance on labeled samples. In this survey, we have comprehensively reviewed +over 90 papers about KG-aware research for two major sample shortage settings +-- zero-shot learning (ZSL) where some classes to be predicted have no labeled +samples, and few-shot learning (FSL) where some classes to be predicted have +only a small number of labeled samples that are available. We first introduce +KGs used in ZSL and FSL as well as their construction methods, and then +systematically categorize and summarize KG-aware ZSL and FSL methods, dividing +them into different paradigms such as the mapping-based, the data augmentation, +the propagation-based and the optimization-based. We next present different +applications, including not only KG augmented prediction tasks such as image +classification, question answering, text classification and knowledge +extraction, but also KG completion tasks, and some typical evaluation resources +for each task. We eventually discuss some challenges and open problems from +different perspectives. +" +Few-shot Learning with Multilingual Language Models,Xi Victoria Lin,http://arxiv.org/pdf/2112.10668v3.pdf,2021-12-20,"['cs.cl', 'cs.ai']",2112.10668v3.pdf," Large-scale generative language models such as GPT-3 are competitive few-shot +learners. While these models are known to be able to jointly represent many +different languages, their training data is dominated by English, potentially +limiting their cross-lingual generalization. In this work, we train +multilingual generative language models on a corpus covering a diverse set of +languages, and study their few- and zero-shot learning capabilities in a wide +range of tasks. Our largest model with 7.5 billion parameters sets new state of +the art in few-shot learning in more than 20 representative languages, +outperforming GPT-3 of comparable size in multilingual commonsense reasoning +(with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in +4-shot settings) and natural language inference (+5.4% in each of 0-shot and +4-shot settings). On the FLORES-101 machine translation benchmark, our model +outperforms GPT-3 on 171 out of 182 directions with 32 training examples, while +surpassing the official supervised baseline in 45 directions. We conduct an +in-depth analysis of different multilingual prompting approaches, showing in +particular that strong few-shot learning performance across languages can be +achieved via cross-lingual transfer through both templates and demonstration +examples. Finally, we evaluate our models in social value tasks such as hate +speech detection in five languages and find it has limitations similar to +comparable sized GPT-3 models. +" +Flamingo: a Visual Language Model for Few-Shot Learning,Jean-Baptiste Alayrac,http://arxiv.org/pdf/2204.14198v2.pdf,2022-04-29,"['cs.cv', 'cs.ai', 'cs.lg']",2204.14198v2.pdf," Building models that can be rapidly adapted to novel tasks using only a +handful of annotated examples is an open challenge for multimodal machine +learning research. We introduce Flamingo, a family of Visual Language Models +(VLM) with this ability. We propose key architectural innovations to: (i) +bridge powerful pretrained vision-only and language-only models, (ii) handle +sequences of arbitrarily interleaved visual and textual data, and (iii) +seamlessly ingest images or videos as inputs. Thanks to their flexibility, +Flamingo models can be trained on large-scale multimodal web corpora containing +arbitrarily interleaved text and images, which is key to endow them with +in-context few-shot learning capabilities. We perform a thorough evaluation of +our models, exploring and measuring their ability to rapidly adapt to a variety +of image and video tasks. These include open-ended tasks such as visual +question-answering, where the model is prompted with a question which it has to +answer; captioning tasks, which evaluate the ability to describe a scene or an +event; and close-ended tasks such as multiple-choice visual question-answering. +For tasks lying anywhere on this spectrum, a single Flamingo model can achieve +a new state of the art with few-shot learning, simply by prompting the model +with task-specific examples. On numerous benchmarks, Flamingo outperforms +models fine-tuned on thousands of times more task-specific data. +" +"Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code",Patrick Bareiß,http://arxiv.org/pdf/2206.01335v2.pdf,2022-06-02,"['cs.se', 'cs.lg']",2206.01335v2.pdf," Few-shot learning with large-scale, pre-trained language models is a powerful +way to answer questions about code, e.g., how to complete a given code example, +or even generate code snippets from scratch. The success of these models raises +the question whether they could serve as a basis for building a wide range code +generation tools. Traditionally, such tools are built manually and separately +for each task. Instead, few-shot learning may allow to obtain different tools +from a single pre-trained language model by simply providing a few examples or +a natural language description of the expected tool behavior. This paper +studies to what extent a state-of-the-art, pre-trained language model of code, +Codex, may serve this purpose. We consider three code manipulation and code +generation tasks targeted by a range of traditional tools: (i) code mutation; +(ii) test oracle generation from natural language documentation; and (iii) test +case generation. For each task, we compare few-shot learning to a manually +built tool. Our results show that the model-based tools complement (code +mutation), are on par (test oracle generation), or even outperform their +respective traditionally built tool (test case generation), while imposing far +less effort to develop them. By comparing the effectiveness of different +variants of the model-based tools, we provide insights on how to design an +appropriate input (""prompt"") to the model and what influence the size of the +model has. For example, we find that providing a small natural language +description of the code generation task is an easy way to improve predictions. +Overall, we conclude that few-shot language models are surprisingly effective, +yet there is still more work to be done, such as exploring more diverse ways of +prompting and tackling even more involved tasks. +" +From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams,Iddo Drori,http://arxiv.org/pdf/2206.05442v7.pdf,2022-06-11,['cs.lg'],2206.05442v7.pdf," A final exam in machine learning at a top institution such as MIT, Harvard, +or Cornell typically takes faculty days to write, and students hours to solve. +We demonstrate that large language models pass machine learning finals at a +human level, on finals available online after the models were trained, and +automatically generate new human-quality final exam questions in seconds. +Previous work has developed program synthesis and few-shot learning methods to +solve university-level problem set questions in mathematics and STEM courses. +In this work, we develop and compare methods that solve final exams, which +differ from problem sets in several ways: the questions are longer, have +multiple parts, are more complicated, and span a broader set of topics. We +curate a dataset and benchmark of questions from machine learning final exams +available online and code for answering these questions and generating new +questions. We show how to generate new questions from other questions and +course notes. For reproducibility and future research on this final exam +benchmark, we use automatic checkers for multiple-choice, numeric, and +questions with expression answers. We perform ablation studies comparing +zero-shot learning with few-shot learning and chain-of-thought prompting using +GPT-3, OPT, Codex, and ChatGPT across machine learning topics and find that +few-shot learning methods perform best. We highlight the transformative +potential of language models to streamline the writing and solution of +large-scale assessments, significantly reducing the workload from human days to +mere machine seconds. Our results suggest that rather than banning large +language models such as ChatGPT in class, instructors should teach students to +harness them by asking students meta-questions about correctness, completeness, +and originality of the responses generated, encouraging critical thinking in +academic studies. +" +Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction,Cheng Peng,http://arxiv.org/pdf/2310.06239v1.pdf,2023-10-10,"['cs.cl', 'cs.ai']",2310.06239v1.pdf," Objective To develop soft prompt-based learning algorithms for large language +models (LLMs), examine the shape of prompts, prompt-tuning using +frozen/unfrozen LLMs, transfer learning, and few-shot learning abilities. +Methods We developed a soft prompt-based LLM model and compared 4 training +strategies including (1) fine-tuning without prompts; (2) hard-prompt with +unfrozen LLMs; (3) soft-prompt with unfrozen LLMs; and (4) soft-prompt with +frozen LLMs. We evaluated 7 pretrained LLMs using the 4 training strategies for +clinical concept and relation extraction on two benchmark datasets. We +evaluated the transfer learning ability of the prompt-based learning algorithms +in a cross-institution setting. We also assessed the few-shot learning ability. +Results and Conclusion When LLMs are unfrozen, GatorTron-3.9B with soft +prompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept +extraction, outperforming the traditional fine-tuning and hard prompt-based +models by 0.6~3.1% and 1.2~2.9%, respectively; GatorTron-345M with soft +prompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end +relation extraction, outperforming the other two models by 0.2~2% and +0.6~11.7%, respectively. When LLMs are frozen, small (i.e., 345 million +parameters) LLMs have a big gap to be competitive with unfrozen models; scaling +LLMs up to billions of parameters makes frozen LLMs competitive with unfrozen +LLMs. For cross-institute evaluation, soft prompting with a frozen +GatorTron-8.9B model achieved the best performance. This study demonstrates +that (1) machines can learn soft prompts better than humans, (2) frozen LLMs +have better few-shot learning ability and transfer learning ability to +facilitate muti-institution applications, and (3) frozen LLMs require large +models. +" +On Unifying Misinformation Detection,Nayeon Lee,http://arxiv.org/pdf/2104.05243v1.pdf,2021-04-12,"['cs.ai', 'cs.cl']",2104.05243v1.pdf," In this paper, we introduce UnifiedM2, a general-purpose misinformation model +that jointly models multiple domains of misinformation with a single, unified +setup. The model is trained to handle four tasks: detecting news bias, +clickbait, fake news, and verifying rumors. By grouping these tasks together, +UnifiedM2learns a richer representation of misinformation, which leads to +state-of-the-art or comparable performance across all tasks. Furthermore, we +demonstrate that UnifiedM2's learned representation is helpful for few-shot +learning of unseen misinformation tasks/datasets and model's generalizability +to unseen events. +" +Discrete and Soft Prompting for Multilingual Models,Mengjie Zhao,http://arxiv.org/pdf/2109.03630v1.pdf,2021-09-08,['cs.cl'],2109.03630v1.pdf," It has been shown for English that discrete and soft prompting perform +strongly in few-shot learning with pretrained language models (PLMs). In this +paper, we show that discrete and soft prompting perform better than finetuning +in multilingual cases: Crosslingual transfer and in-language training of +multilingual natural language inference. For example, with 48 English training +examples, finetuning obtains 33.74% accuracy in crosslingual transfer, barely +surpassing the majority baseline (33.33%). In contrast, discrete and soft +prompting outperform finetuning, achieving 36.43% and 38.79%. We also +demonstrate good performance of prompting with training data in multiple +languages other than English. +" +Cedille: A large autoregressive French language model,Martin Müller,http://arxiv.org/pdf/2202.03371v1.pdf,2022-02-07,"['cs.cl', '68t50', 'i.2.7']",2202.03371v1.pdf," Scaling up the size and training of autoregressive language models has +enabled novel ways of solving Natural Language Processing tasks using zero-shot +and few-shot learning. While extreme-scale language models such as GPT-3 offer +multilingual capabilities, zero-shot learning for languages other than English +remain largely unexplored. Here, we introduce Cedille, a large open source +auto-regressive language model, specifically trained for the French language. +Our results show that Cedille outperforms existing French language models and +is competitive with GPT-3 on a range of French zero-shot benchmarks. +Furthermore, we provide an in-depth comparison of the toxicity exhibited by +these models, showing that Cedille marks an improvement in language model +safety thanks to dataset filtering. +" +Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class,Anton Thielmann,http://arxiv.org/pdf/2212.09422v1.pdf,2022-12-19,['cs.cl'],2212.09422v1.pdf," Few-shot methods for accurate modeling under sparse label-settings have +improved significantly. However, the applications of few-shot modeling in +natural language processing remain solely in the field of document +classification. With recent performance improvements, supervised few-shot +methods, combined with a simple topic extraction method pose a significant +challenge to unsupervised topic modeling methods. Our research shows that +supervised few-shot learning, combined with a simple topic extraction method, +can outperform unsupervised topic modeling techniques in terms of generating +coherent topics, even when only a few labeled documents per class are used. +" +Sentence Simplification via Large Language Models,Yutao Feng,http://arxiv.org/pdf/2302.11957v1.pdf,2023-02-23,"['cs.cl', 'cs.ai']",2302.11957v1.pdf," Sentence Simplification aims to rephrase complex sentences into simpler +sentences while retaining original meaning. Large Language models (LLMs) have +demonstrated the ability to perform a variety of natural language processing +tasks. However, it is not yet known whether LLMs can be served as a +high-quality sentence simplification system. In this work, we empirically +analyze the zero-/few-shot learning ability of LLMs by evaluating them on a +number of benchmark test sets. Experimental results show LLMs outperform +state-of-the-art sentence simplification methods, and are judged to be on a par +with human annotators. +" +NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN,Yufei Guo,http://arxiv.org/pdf/2306.12073v1.pdf,2023-06-21,['cs.cv'],2306.12073v1.pdf," Recently, the neuromorphic vision sensor has received more and more interest. +However, the neuromorphic data consists of asynchronous event spikes, which is +not natural and difficult to construct a benchmark, thus limiting the +neuromorphic data understanding for ""unseen"" objects by deep learning. +Zero-shot and few-shot learning via Contrastive Vision-Language Pre-training +(CLIP) have shown inspirational performance in 2D frame image recognition. To +handle ""unseen"" recognition for the neuromorphic data, in this paper, we +propose NeuroCLIP, which transfers the CLIP's 2D pre-trained knowledge to event +spikes. To improve the few-shot performance, we also provide an inter-timestep +adapter based on a spiking neural network. Our code is open-sourced at +https://github.com/yfguo91/NeuroCLIP.git. +" +Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation,Lea Krause,http://arxiv.org/pdf/2308.01080v1.pdf,2023-08-02,['cs.cl'],2308.01080v1.pdf," This paper discusses our approaches for task-oriented conversational +modelling using subjective knowledge, with a particular emphasis on response +generation. Our methodology was shaped by an extensive data analysis that +evaluated key factors such as response length, sentiment, and dialogue acts +present in the provided dataset. We used few-shot learning to augment the data +with newly generated subjective knowledge items and present three approaches +for DSTC11: (1) task-specific model exploration, (2) incorporation of the most +frequent question into all generated responses, and (3) a waterfall prompting +technique using a combination of both GPT-3 and ChatGPT. +" +Making Pre-trained Language Models Better Few-shot Learners,Tianyu Gao,http://arxiv.org/pdf/2012.15723v2.pdf,2020-12-31,"['cs.cl', 'cs.lg']",2012.15723v2.pdf," The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot +performance solely by leveraging a natural-language prompt and a few task +demonstrations as input context. Inspired by their findings, we study few-shot +learning in a more practical scenario, where we use smaller language models for +which fine-tuning is computationally efficient. We present LM-BFF--better +few-shot fine-tuning of language models--a suite of simple and complementary +techniques for fine-tuning language models on a small number of annotated +examples. Our approach includes (1) prompt-based fine-tuning together with a +novel pipeline for automating prompt generation; and (2) a refined strategy for +dynamically and selectively incorporating demonstrations into each context. +Finally, we present a systematic evaluation for analyzing few-shot performance +on a range of NLP tasks, including classification and regression. Our +experiments demonstrate that our methods combine to dramatically outperform +standard fine-tuning procedures in this low resource setting, achieving up to +30% absolute improvement, and 11% on average across all tasks. Our approach +makes minimal assumptions on task resources and domain expertise, and hence +constitutes a strong task-agnostic method for few-shot learning. +" +GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain,Milad Moradi,http://arxiv.org/pdf/2109.02555v2.pdf,2021-09-06,"['cs.cl', 'cs.ai', 'cs.lg']",2109.02555v2.pdf," Deep neural language models have set new breakthroughs in many tasks of +Natural Language Processing (NLP). Recent work has shown that deep transformer +language models (pretrained on large amounts of texts) can achieve high levels +of task-specific few-shot performance comparable to state-of-the-art models. +However, the ability of these large language models in few-shot transfer +learning has not yet been explored in the biomedical domain. We investigated +the performance of two powerful transformer language models, i.e. GPT-3 and +BioBERT, in few-shot settings on various biomedical NLP tasks. The experimental +results showed that, to a great extent, both the models underperform a language +model fine-tuned on the full training data. Although GPT-3 had already achieved +near state-of-the-art results in few-shot knowledge transfer on open-domain NLP +tasks, it could not perform as effectively as BioBERT, which is orders of +magnitude smaller than GPT-3. Regarding that BioBERT was already pretrained on +large biomedical text corpora, our study suggests that language models may +largely benefit from in-domain pretraining in task-specific few-shot learning. +However, in-domain pretraining seems not to be sufficient; novel pretraining +and few-shot learning strategies are required in the biomedical NLP domain. +" +PPT: Pre-trained Prompt Tuning for Few-shot Learning,Yuxian Gu,http://arxiv.org/pdf/2109.04332v3.pdf,2021-09-09,['cs.cl'],2109.04332v3.pdf," Prompts for pre-trained language models (PLMs) have shown remarkable +performance by bridging the gap between pre-training tasks and various +downstream tasks. Among these methods, prompt tuning, which freezes PLMs and +only tunes soft prompts, provides an efficient and effective solution for +adapting large-scale PLMs to downstream tasks. However, prompt tuning is yet to +be fully explored. In our pilot experiments, we find that prompt tuning +performs comparably with conventional full-model fine-tuning when downstream +data are sufficient, whereas it performs much worse under few-shot learning +settings, which may hinder the application of prompt tuning in practice. We +attribute this low performance to the manner of initializing soft prompts. +Therefore, in this work, we propose to pre-train prompts by adding soft prompts +into the pre-training stage to obtain a better initialization. We name this +Pre-trained Prompt Tuning framework ""PPT"". To ensure the generalization of PPT, +we formulate similar classification tasks into a unified task form and +pre-train soft prompts for this unified task. Extensive experiments show that +tuning pre-trained prompts for downstream tasks can reach or even outperform +full-model fine-tuning under both full-data and few-shot settings. Our approach +is effective and efficient for using large-scale PLMs in practice. +" +Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning,Shaohua Wu,http://arxiv.org/pdf/2110.04725v2.pdf,2021-10-10,"['cs.cl', 'cs.ai']",2110.04725v2.pdf," Recent work like GPT-3 has demonstrated excellent performance of Zero-Shot +and Few-Shot learning on many natural language processing (NLP) tasks by +scaling up model size, dataset size and the amount of computation. However, +training a model like GPT-3 requires huge amount of computational resources +which makes it challengeable to researchers. In this work, we propose a method +that incorporates large-scale distributed training performance into model +architecture design. With this method, Yuan 1.0, the current largest singleton +language model with 245B parameters, achieves excellent performance on +thousands GPUs during training, and the state-of-the-art results on NLP tasks. +A data processing method is designed to efficiently filter massive amount of +raw data. The current largest high-quality Chinese corpus with 5TB high quality +texts is built based on this method. In addition, a calibration and label +expansion method is proposed to improve the Zero-Shot and Few-Shot performance, +and steady improvement is observed on the accuracy of various tasks. Yuan 1.0 +presents strong capacity of natural language generation, and the generated +articles are difficult to distinguish from the human-written ones. +" +LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners,Yaqing Wang,http://arxiv.org/pdf/2110.06274v2.pdf,2021-10-12,['cs.cl'],2110.06274v2.pdf," We present a new method LiST is short for Lite Prompted Self-Training for +parameter-efficient fine-tuning of large pre-trained language models (PLMs) for +few-shot learning. LiST improves over recent methods that adopt prompt-based +fine-tuning (FN) using two key techniques. The first is the use of +self-training to leverage large amounts of unlabeled data for prompt-based FN +in few-shot settings. We use self-training in conjunction with meta-learning +for re-weighting noisy pseudo-prompt labels. Self-training is expensive as it +requires updating all the model parameters repetitively. Therefore, we use a +second technique for light-weight fine-tuning where we introduce a small number +of task-specific parameters that are fine-tuned during self-training while +keeping the PLM encoder frozen. Our experiments show that LiST can effectively +leverage unlabeled data to improve the model performance for few-shot learning. +Additionally, the fine-tuning is efficient as it only updates a small +percentage of parameters and the overall model footprint is reduced since +several tasks can share a common PLM encoder as backbone. A comprehensive study +on six NLU tasks demonstrate LiST to improve by 35% over classic fine-tuning +and 6% over prompt-based FN with 96% reduction in number of trainable +parameters when fine-tuned with no more than 30 labeled examples from each +task. With only 14M tunable parameters, LiST outperforms GPT-3 in-context +learning by 33% on few-shot NLU tasks. +" +PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models,Rabeeh Karimi Mahabadi,http://arxiv.org/pdf/2204.01172v2.pdf,2022-04-03,['cs.cl'],2204.01172v2.pdf," Current methods for few-shot fine-tuning of pretrained masked language models +(PLMs) require carefully engineered prompts and verbalizers for each new task +to convert examples into a cloze-format that the PLM can score. In this work, +we propose PERFECT, a simple and efficient method for few-shot fine-tuning of +PLMs without relying on any such handcrafting, which is highly effective given +as few as 32 data points. PERFECT makes two key design choices: First, we show +that manually engineered task prompts can be replaced with task-specific +adapters that enable sample-efficient fine-tuning and reduce memory and storage +costs by roughly factors of 5 and 100, respectively. Second, instead of using +handcrafted verbalizers, we learn new multi-token label embeddings during +fine-tuning, which are not tied to the model vocabulary and which allow us to +avoid complex auto-regressive decoding. These embeddings are not only learnable +from limited data but also enable nearly 100x faster training and inference. +Experiments on a wide range of few-shot NLP tasks demonstrate that PERFECT, +while being simple and efficient, also outperforms existing state-of-the-art +few-shot learning methods. Our code is publicly available at +https://github.com/facebookresearch/perfect.git. +" +On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model,Seongjin Shin,http://arxiv.org/pdf/2204.13509v2.pdf,2022-04-28,['cs.cl'],2204.13509v2.pdf," Many recent studies on large-scale language models have reported successful +in-context zero- and few-shot learning ability. However, the in-depth analysis +of when in-context learning occurs is still lacking. For example, it is unknown +how in-context learning performance changes as the training corpus varies. +Here, we investigate the effects of the source and size of the pretraining +corpus on in-context learning in HyperCLOVA, a Korean-centric GPT-3 model. From +our in-depth investigation, we introduce the following observations: (1) +in-context learning performance heavily depends on the corpus domain source, +and the size of the pretraining corpus does not necessarily determine the +emergence of in-context learning, (2) in-context learning ability can emerge +when a language model is trained on a combination of multiple corpora, even +when each corpus does not result in in-context learning on its own, (3) +pretraining with a corpus related to a downstream task does not always +guarantee the competitive in-context learning performance of the downstream +task, especially in the few-shot setting, and (4) the relationship between +language modeling (measured in perplexity) and in-context learning does not +always correlate: e.g., low perplexity does not always imply high in-context +few-shot learning performance. +" +Few-Shot Stance Detection via Target-Aware Prompt Distillation,Yan Jiang,http://arxiv.org/pdf/2206.13214v1.pdf,2022-06-27,['cs.cl'],2206.13214v1.pdf," Stance detection aims to identify whether the author of a text is in favor +of, against, or neutral to a given target. The main challenge of this task +comes two-fold: few-shot learning resulting from the varying targets and the +lack of contextual information of the targets. Existing works mainly focus on +solving the second issue by designing attention-based models or introducing +noisy external knowledge, while the first issue remains under-explored. In this +paper, inspired by the potential capability of pre-trained language models +(PLMs) serving as knowledge bases and few-shot learners, we propose to +introduce prompt-based fine-tuning for stance detection. PLMs can provide +essential contextual information for the targets and enable few-shot learning +via prompts. Considering the crucial role of the target in stance detection +task, we design target-aware prompts and propose a novel verbalizer. Instead of +mapping each label to a concrete word, our verbalizer maps each label to a +vector and picks the label that best captures the correlation between the +stance and the target. Moreover, to alleviate the possible defect of dealing +with varying targets with a single hand-crafted prompt, we propose to distill +the information learned from multiple prompts. Experimental results show the +superior performance of our proposed model in both full-data and few-shot +scenarios. +" +Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks,David Oniani,http://arxiv.org/pdf/2208.14923v2.pdf,2022-08-31,['cs.cl'],2208.14923v2.pdf," Clinical Natural Language Processing (NLP) has become an emerging technology +in healthcare that leverages a large amount of free-text data in electronic +health records (EHRs) to improve patient care, support clinical decisions, and +facilitate clinical and translational science research. Recently, deep learning +has achieved state-of-the-art performance in many clinical NLP tasks. However, +training deep learning models usually requires large annotated datasets, which +are normally not publicly available and can be time-consuming to build in +clinical domains. Working with smaller annotated datasets is typical in +clinical NLP and therefore, ensuring that deep learning models perform well is +crucial for the models to be used in real-world applications. A widely adopted +approach is fine-tuning existing Pre-trained Language Models (PLMs), but these +attempts fall short when the training dataset contains only a few annotated +samples. Few-Shot Learning (FSL) has recently been investigated to tackle this +problem. Siamese Neural Network (SNN) has been widely utilized as an FSL +approach in computer vision, but has not been studied well in NLP. Furthermore, +the literature on its applications in clinical domains is scarce. In this +paper, we propose two SNN-based FSL approaches for clinical NLP, including +Pre-Trained SNN (PT-SNN) and SNN with Second-Order Embeddings (SOE-SNN). We +evaluated the proposed approaches on two clinical tasks, namely clinical text +classification and clinical named entity recognition. We tested three few-shot +settings including 4-shot, 8-shot, and 16-shot learning. Both clinical NLP +tasks were benchmarked using three PLMs, including BERT,BioBERT, and +BioClinicalBERT. The experimental results verified the effectiveness of the +proposed SNN-based FSL approaches in both NLP tasks. +" +Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models,Yue Zhang,http://arxiv.org/pdf/2210.10841v1.pdf,2022-10-19,"['cs.cl', 'cs.cv']",2210.10841v1.pdf," Prompt learning is a new learning paradigm which reformulates downstream +tasks as similar pretraining tasks on pretrained models by leveraging textual +prompts. Recent works have demonstrated that prompt learning is particularly +useful for few-shot learning, where there is limited training data. Depending +on the granularity of prompts, those methods can be roughly divided into +task-level prompting and instance-level prompting. Task-level prompting methods +learn one universal prompt for all input samples, which is efficient but +ineffective to capture subtle differences among different classes. +Instance-level prompting methods learn a specific prompt for each input, though +effective but inefficient. In this work, we develop a novel prototype-based +prompt learning method to overcome the above limitations. In particular, we +focus on few-shot image recognition tasks on pretrained vision-language models +(PVLMs) and develop a method of prompting through prototype (PTP), where we +define $K$ image prototypes and $K$ prompt prototypes. In PTP, the image +prototype represents a centroid of a certain image cluster in the latent space +and a prompt prototype is defined as a soft prompt in the continuous space. The +similarity between a query image and an image prototype determines how much +this prediction relies on the corresponding prompt prototype. Hence, in PTP, +similar images will utilize similar prompting ways. Through extensive +experiments on seven real-world benchmarks, we show that PTP is an effective +method to leverage the latent knowledge and adaptive to various PVLMs. +Moreover, through detailed analysis, we discuss pros and cons for prompt +learning and parameter-efficient fine-tuning under the context of few-shot +learning. +" +SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification,Fang Peng,http://arxiv.org/pdf/2211.16191v2.pdf,2022-11-28,"['cs.cv', 'cs.mm']",2211.16191v2.pdf," Although significant progress has been made in few-shot learning, most of +existing few-shot image classification methods require supervised pre-training +on a large amount of samples of base classes, which limits their generalization +ability in real world application. Recently, large-scale Vision-Language +Pre-trained models (VLPs) have been gaining increasing attention in few-shot +learning because they can provide a new paradigm for transferable visual +representation learning with easily available text on the Web. However, the +VLPs may neglect detailed visual information that is difficult to describe by +language sentences, but important for learning an effective classifier to +distinguish different images. To address the above problem, we propose a new +framework, named Semantic-guided Visual Adapting (SgVA), which can effectively +extend vision-language pre-trained models to produce discriminative adapted +visual features by comprehensively using an implicit knowledge distillation, a +vision-specific contrastive loss, and a cross-modal contrastive loss. The +implicit knowledge distillation is designed to transfer the fine-grained +cross-modal knowledge to guide the updating of the vision adapter. +State-of-the-art results on 13 datasets demonstrate that the adapted visual +features can well complement the cross-modal features to improve few-shot image +classification. +" +Finetune like you pretrain: Improved finetuning of zero-shot vision models,Sachin Goyal,http://arxiv.org/pdf/2212.00638v1.pdf,2022-12-01,"['cs.cv', 'cs.lg']",2212.00638v1.pdf," Finetuning image-text models such as CLIP achieves state-of-the-art +accuracies on a variety of benchmarks. However, recent works like WiseFT +(Wortsman et al., 2021) and LP-FT (Kumar et al., 2022) have shown that even +subtle differences in the finetuning process can lead to surprisingly large +differences in the final performance, both for in-distribution (ID) and +out-of-distribution (OOD) data. In this work, we show that a natural and simple +approach of mimicking contrastive pretraining consistently outperforms +alternative finetuning approaches. Specifically, we cast downstream class +labels as text prompts and continue optimizing the contrastive loss between +image embeddings and class-descriptive prompt embeddings (contrastive +finetuning). + Our method consistently outperforms baselines across 7 distribution shifts, 6 +transfer learning, and 3 few-shot learning benchmarks. On WILDS-iWILDCam, our +proposed approach FLYP outperforms the top of the leaderboard by $2.3\%$ ID and +$2.7\%$ OOD, giving the highest reported accuracy. Averaged across 7 OOD +datasets (2 WILDS and 5 ImageNet associated shifts), FLYP gives gains of +$4.2\%$ OOD over standard finetuning and outperforms the current state of the +art (LP-FT) by more than $1\%$ both ID and OOD. Similarly, on 3 few-shot +learning benchmarks, our approach gives gains up to $4.6\%$ over standard +finetuning and $4.4\%$ over the state of the art. In total, these benchmarks +establish contrastive finetuning as a simple, intuitive, and state-of-the-art +approach for supervised finetuning of image-text models like CLIP. Code is +available at https://github.com/locuslab/FLYP. +" +Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models,Zhiqiu Lin,http://arxiv.org/pdf/2301.06267v4.pdf,2023-01-16,"['cs.cv', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as']",2301.06267v4.pdf," The ability to quickly learn a new task with minimal instruction - known as +few-shot learning - is a central aspect of intelligent agents. Classical +few-shot benchmarks make use of few-shot samples from a single modality, but +such samples may not be sufficient to characterize an entire concept class. In +contrast, humans use cross-modal information to learn new concepts efficiently. +In this work, we demonstrate that one can indeed build a better ${\bf visual}$ +dog classifier by ${\bf read}$ing about dogs and ${\bf listen}$ing to them +bark. To do so, we exploit the fact that recent multimodal foundation models +such as CLIP are inherently cross-modal, mapping different modalities to the +same representation space. Specifically, we propose a simple cross-modal +adaptation approach that learns from few-shot examples spanning different +modalities. By repurposing class names as additional one-shot training samples, +we achieve SOTA results with an embarrassingly simple linear classifier for +vision-language adaptation. Furthermore, we show that our approach can benefit +existing methods such as prefix tuning, adapters, and classifier ensembling. +Finally, to explore other modalities beyond vision and language, we construct +the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal +training to improve the performance of both image and audio classification. +" +AugGPT: Leveraging ChatGPT for Text Data Augmentation,Haixing Dai,http://arxiv.org/pdf/2302.13007v3.pdf,2023-02-25,"['cs.cl', 'cs.ai', 'cs.lg']",2302.13007v3.pdf," Text data augmentation is an effective strategy for overcoming the challenge +of limited sample sizes in many natural language processing (NLP) tasks. This +challenge is especially prominent in the few-shot learning scenario, where the +data in the target domain is generally much scarcer and of lowered quality. A +natural and widely-used strategy to mitigate such challenges is to perform data +augmentation to better capture the data invariance and increase the sample +size. However, current text data augmentation methods either can't ensure the +correct labeling of the generated data (lacking faithfulness) or can't ensure +sufficient diversity in the generated data (lacking compactness), or both. +Inspired by the recent success of large language models, especially the +development of ChatGPT, which demonstrated improved language comprehension +abilities, in this work, we propose a text data augmentation approach based on +ChatGPT (named AugGPT). AugGPT rephrases each sentence in the training samples +into multiple conceptually similar but semantically different samples. The +augmented samples can then be used in downstream model training. Experiment +results on few-shot learning text classification tasks show the superior +performance of the proposed AugGPT approach over state-of-the-art text data +augmentation methods in terms of testing accuracy and distribution of the +augmented samples. +" +Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning,Ivona Najdenkoska,http://arxiv.org/pdf/2302.14794v1.pdf,2023-02-28,['cs.cv'],2302.14794v1.pdf," Multimodal few-shot learning is challenging due to the large domain gap +between vision and language modalities. Existing methods are trying to +communicate visual concepts as prompts to frozen language models, but rely on +hand-engineered task induction to reduce the hypothesis space. To make the +whole process learnable, we introduce a multimodal meta-learning approach. +Specifically, our approach decomposes the training of the model into a set of +related multimodal few-shot tasks. We define a meta-mapper network, acting as a +meta-learner, to efficiently bridge frozen large-scale vision and language +models and leverage their already learned capacity. By updating the learnable +parameters only of the meta-mapper, it learns to accrue shared meta-knowledge +among these tasks. Thus, it can rapidly adapt to newly presented samples with +only a few gradient updates. Importantly, it induces the task in a completely +data-driven manner, with no need for a hand-engineered task induction. We +evaluate our approach on recently proposed multimodal few-shot benchmarks, +measuring how rapidly the model can bind novel visual concepts to words and +answer visual questions by observing only a limited set of labeled examples. +The experimental results show that our meta-learning approach outperforms the +baseline across multiple datasets and various training settings while being +computationally more efficient. +" +Semantic Prompt for Few-Shot Image Recognition,Wentao Chen,http://arxiv.org/pdf/2303.14123v1.pdf,2023-03-24,['cs.cv'],2303.14123v1.pdf," Few-shot learning is a challenging problem since only a few examples are +provided to recognize a new class. Several recent studies exploit additional +semantic information, e.g. text embeddings of class names, to address the issue +of rare samples through combining semantic prototypes with visual prototypes. +However, these methods still suffer from the spurious visual features learned +from the rare support samples, resulting in limited benefits. In this paper, we +propose a novel Semantic Prompt (SP) approach for few-shot learning. Instead of +the naive exploitation of semantic information for remedying classifiers, we +explore leveraging semantic information as prompts to tune the visual feature +extraction network adaptively. Specifically, we design two complementary +mechanisms to insert semantic prompts into the feature extractor: one is to +enable the interaction between semantic prompts and patch embeddings along the +spatial dimension via self-attention, another is to supplement visual features +with the transformed semantic prompts along the channel dimension. By combining +these two mechanisms, the feature extractor presents a better ability to attend +to the class-specific features and obtains more generalized image +representations with merely a few support samples. Through extensive +experiments on four datasets, the proposed approach achieves promising results, +improving the 1-shot learning accuracy by 3.67% on average. +" +RPLKG: Robust Prompt Learning with Knowledge Graph,Yewon Kim,http://arxiv.org/pdf/2304.10805v1.pdf,2023-04-21,"['cs.ai', 'cs.lg']",2304.10805v1.pdf," Large-scale pre-trained models have been known that they are transferable, +and they generalize well on the unseen dataset. Recently, multimodal +pre-trained models such as CLIP show significant performance improvement in +diverse experiments. However, when the labeled dataset is limited, the +generalization of a new dataset or domain is still challenging. To improve the +generalization performance on few-shot learning, there have been diverse +efforts, such as prompt learning and adapter. However, the current few-shot +adaptation methods are not interpretable, and they require a high computation +cost for adaptation. In this study, we propose a new method, robust prompt +learning with knowledge graph (RPLKG). Based on the knowledge graph, we +automatically design diverse interpretable and meaningful prompt sets. Our +model obtains cached embeddings of prompt sets after one forwarding from a +large pre-trained model. After that, model optimizes the prompt selection +processes with GumbelSoftmax. In this way, our model is trained using +relatively little memory and learning time. Also, RPLKG selects the optimal +interpretable prompt automatically, depending on the dataset. In summary, RPLKG +is i) interpretable, ii) requires small computation resources, and iii) easy to +incorporate prior human knowledge. To validate the RPLKG, we provide +comprehensive experimental results on few-shot learning, domain generalization +and new class generalization setting. RPLKG shows a significant performance +improvement compared to zero-shot learning and competitive performance against +several prompt learning methods using much lower resources. +" +The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning,Seungone Kim,http://arxiv.org/pdf/2305.14045v2.pdf,2023-05-23,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14045v2.pdf," Language models (LMs) with less than 100B parameters are known to perform +poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when +solving unseen tasks. In this work, we aim to equip smaller LMs with the +step-by-step reasoning capability by instruction tuning with CoT rationales. In +order to achieve this goal, we first introduce a new instruction-tuning dataset +called the CoT Collection, which augments the existing Flan Collection +(including only 9 CoT tasks) with additional 1.84 million rationales across +1,060 tasks. We show that CoT fine-tuning Flan-T5 (3B & 11B) with CoT +Collection enables smaller LMs to have better CoT capabilities on unseen tasks. +On the BIG-Bench-Hard (BBH) benchmark, we report an average improvement of ++4.34% (Flan-T5 3B) and +2.60% (Flan-T5 11B), in terms of zero-shot task +accuracy. Furthermore, we show that instruction tuning with CoT Collection +allows LMs to possess stronger few-shot learning capabilities on 4 +domain-specific tasks, resulting in an improvement of +2.24% (Flan-T5 3B) and ++2.37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until +the max length by a +13.98% margin. Our code, the CoT Collection data, and +model checkpoints are publicly available. +" +Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding,Venkata Prabhakara Sarath Nookala,http://arxiv.org/pdf/2306.11066v2.pdf,2023-06-19,"['cs.cl', 'cs.lg']",2306.11066v2.pdf," State-of-the-art few-shot learning (FSL) methods leverage prompt-based +fine-tuning to obtain remarkable results for natural language understanding +(NLU) tasks. While much of the prior FSL methods focus on improving downstream +task performance, there is a limited understanding of the adversarial +robustness of such methods. In this work, we conduct an extensive study of +several state-of-the-art FSL methods to assess their robustness to adversarial +perturbations. To better understand the impact of various factors towards +robustness (or the lack of it), we evaluate prompt-based FSL methods against +fully fine-tuned models for aspects such as the use of unlabeled data, multiple +prompts, number of few-shot examples, model size and type. Our results on six +GLUE tasks indicate that compared to fully fine-tuned models, vanilla FSL +methods lead to a notable relative drop in task performance (i.e., are less +robust) in the face of adversarial perturbations. However, using (i) unlabeled +data for prompt-based FSL and (ii) multiple prompts flip the trend. We further +demonstrate that increasing the number of few-shot examples and model size lead +to increased adversarial robustness of vanilla FSL methods. Broadly, our work +sheds light on the adversarial robustness evaluation of prompt-based FSL +methods for NLU tasks. +" +Few-shot Learning for Inference in Medical Imaging with Subspace Feature Representations,Jiahui Liu,http://arxiv.org/pdf/2306.11152v1.pdf,2023-06-19,"['math.na', 'cs.na']",2306.11152v1.pdf," Unlike the field of visual scene recognition where tremendous advances have +taken place due to the availability of very large datasets to train deep neural +networks, inference from medical images is often hampered by the fact that only +small amounts of data may be available. When working with very small dataset +problems, of the order of a few hundred items of data, the power of deep +learning may still be exploited by using a model pre-trained on natural images +as a feature extractor and carrying out classic pattern recognition techniques +in this feature space, the so-called few-shot learning problem. In regimes +where the dimension of this feature space is comparable to or even larger than +the number of items of data, dimensionality reduction is a necessity and is +often achieved by principal component analysis, i.e., singular value +decomposition (SVD). In this paper, noting the inappropriateness of using SVD +for this setting, we usher in and explore two alternatives based on +discriminant analysis and non-negative matrix factorization (NMF). Using 14 +different datasets spanning $11$ distinct disease types, we demonstrate that +discriminant subspaces at low dimensions achieve significant improvements over +SVD-based subspaces and the original feature space. We also show that NMF at +modest dimensions is a competitive alternative to SVD in this setting. +" +Visually grounded few-shot word learning in low-resource settings,Leanne Nortje,http://arxiv.org/pdf/2306.11371v2.pdf,2023-06-20,"['eess.as', 'cs.cl']",2306.11371v2.pdf," We propose a visually grounded speech model that learns new words and their +visual depictions from just a few word-image example pairs. Given a set of test +images and a spoken query, we ask the model which image depicts the query word. +Previous work has simplified this few-shot learning problem by either using an +artificial setting with digit word-image pairs or by using a large number of +examples per class. Moreover, all previous studies were performed using English +speech-image data. We propose an approach that can work on natural word-image +pairs but with less examples, i.e. fewer shots, and then illustrate how this +approach can be applied for multimodal few-shot learning in a real low-resource +language, Yoruba. Our approach involves using the given word-image example +pairs to mine new unsupervised word-image training pairs from large collections +of unlabelledspeech and images. Additionally, we use a word-to-image attention +mechanism to determine word-image similarity. With this new model, we achieve +better performance with fewer shots than previous approaches on an existing +English benchmark. Many of the model's mistakes are due to confusion between +visual concepts co-occurring in similar contexts. The experiments on Yoruba +show the benefit of transferring knowledge from a multimodal model trained on a +larger set of English speech-image data. +" +Cross-Modal Concept Learning and Inference for Vision-Language Models,Yi Zhang,http://arxiv.org/pdf/2307.15460v1.pdf,2023-07-28,"['cs.cv', 'cs.cl']",2307.15460v1.pdf," Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP, +establish the correlation between texts and images, achieving remarkable +success on various downstream tasks with fine-tuning. In existing fine-tuning +methods, the class-specific text description is matched against the whole +image. We recognize that this whole image matching is not effective since +images from the same class often contain a set of different semantic objects, +and an object further consists of a set of semantic parts or concepts. +Individual semantic parts or concepts may appear in image samples from +different classes. To address this issue, in this paper, we develop a new +method called cross-model concept learning and inference (CCLI). Using the +powerful text-image correlation capability of CLIP, our method automatically +learns a large set of distinctive visual concepts from images using a set of +semantic text concepts. Based on these visual concepts, we construct a +discriminative representation of images and learn a concept inference network +to perform downstream image classification tasks, such as few-shot learning and +domain generalization. Extensive experimental results demonstrate that our CCLI +method is able to improve the performance upon the current state-of-the-art +methods by large margins, for example, by up to 8.0% improvement on few-shot +learning and by up to 1.3% for domain generalization. +" +Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension,Leilei Su,http://arxiv.org/pdf/2308.06454v1.pdf,2023-08-12,['cs.cl'],2308.06454v1.pdf," Although deep learning techniques have shown significant achievements, they +frequently depend on extensive amounts of hand-labeled data and tend to perform +inadequately in few-shot scenarios. The objective of this study is to devise a +strategy that can improve the model's capability to recognize biomedical +entities in scenarios of few-shot learning. By redefining biomedical named +entity recognition (BioNER) as a machine reading comprehension (MRC) problem, +we propose a demonstration-based learning method to address few-shot BioNER, +which involves constructing appropriate task demonstrations. In assessing our +proposed method, we compared the proposed method with existing advanced methods +using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical, +BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA. We examined the models' +efficacy by reporting F1 scores from both the 25-shot and 50-shot learning +experiments. In 25-shot learning, we observed 1.1% improvements in the average +F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%, +50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further +improved the average F1 scores by 1.0% compared to the baseline method, +reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively. We +reported that in the realm of few-shot learning BioNER, MRC-based language +models are much more proficient in recognizing biomedical entities compared to +the sequence labeling approach. Furthermore, our MRC-language models can +compete successfully with fully-supervised learning methodologies that rely +heavily on the availability of abundant annotated data. These results highlight +possible pathways for future advancements in few-shot BioNER methodologies. +" +Robustness Over Time: Understanding Adversarial Examples' Effectiveness on Longitudinal Versions of Large Language Models,Yugeng Liu,http://arxiv.org/pdf/2308.07847v1.pdf,2023-08-15,['cs.cr'],2308.07847v1.pdf," Large Language Models (LLMs) have led to significant improvements in many +tasks across various domains, such as code interpretation, response generation, +and ambiguity handling. These LLMs, however, when upgrading, primarily +prioritize enhancing user experience while neglecting security, privacy, and +safety implications. Consequently, unintended vulnerabilities or biases can be +introduced. Previous studies have predominantly focused on specific versions of +the models and disregard the potential emergence of new attack vectors +targeting the updated versions. Through the lens of adversarial examples within +the in-context learning framework, this longitudinal study addresses this gap +by conducting a comprehensive assessment of the robustness of successive +versions of LLMs, vis-\`a-vis GPT-3.5. We conduct extensive experiments to +analyze and understand the impact of the robustness in two distinct learning +categories: zero-shot learning and few-shot learning. Our findings indicate +that, in comparison to earlier versions of LLMs, the updated versions do not +exhibit the anticipated level of robustness against adversarial attacks. In +addition, our study emphasizes the increased effectiveness of synergized +adversarial queries in most zero-shot learning and few-shot learning cases. We +hope that our study can lead to a more refined assessment of the robustness of +LLMs over time and provide valuable insights of these models for both +developers and users. +" +UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning,Meiqi Sun,http://arxiv.org/pdf/2308.09953v1.pdf,2023-08-19,['cs.cv'],2308.09953v1.pdf," Animal visual perception is an important technique for automatically +monitoring animal health, understanding animal behaviors, and assisting +animal-related research. However, it is challenging to design a deep +learning-based perception model that can freely adapt to different animals +across various perception tasks, due to the varying poses of a large diversity +of animals, lacking data on rare species, and the semantic inconsistency of +different tasks. We introduce UniAP, a novel Universal Animal Perception model +that leverages few-shot learning to enable cross-species perception among +various visual tasks. Our proposed model takes support images and labels as +prompt guidance for a query image. Images and labels are processed through a +Transformer-based encoder and a lightweight label encoder, respectively. Then a +matching module is designed for aggregating information between prompt guidance +and the query image, followed by a multi-head label decoder to generate outputs +for various tasks. By capitalizing on the shared visual characteristics among +different animals and tasks, UniAP enables the transfer of knowledge from +well-studied species to those with limited labeled data or even unseen species. +We demonstrate the effectiveness of UniAP through comprehensive experiments in +pose estimation, segmentation, and classification tasks on diverse animal +species, showcasing its ability to generalize and adapt to new classes with +minimal labeled examples. +" +PaLM: Scaling Language Modeling with Pathways,Aakanksha Chowdhery,http://arxiv.org/pdf/2204.02311v5.pdf,2022-04-05,['cs.cl'],2204.02311v5.pdf," Large language models have been shown to achieve remarkable performance +across a variety of natural language tasks using few-shot learning, which +drastically reduces the number of task-specific training examples needed to +adapt the model to a particular application. To further our understanding of +the impact of scale on few-shot learning, we trained a 540-billion parameter, +densely activated, Transformer language model, which we call Pathways Language +Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML +system which enables highly efficient training across multiple TPU Pods. We +demonstrate continued benefits of scaling by achieving state-of-the-art +few-shot learning results on hundreds of language understanding and generation +benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough +performance, outperforming the finetuned state-of-the-art on a suite of +multi-step reasoning tasks, and outperforming average human performance on the +recently released BIG-bench benchmark. A significant number of BIG-bench tasks +showed discontinuous improvements from model scale, meaning that performance +steeply increased as we scaled to our largest model. PaLM also has strong +capabilities in multilingual tasks and source code generation, which we +demonstrate on a wide array of benchmarks. We additionally provide a +comprehensive analysis on bias and toxicity, and study the extent of training +data memorization with respect to model scale. Finally, we discuss the ethical +considerations related to large language models and discuss potential +mitigation strategies. +" +Few-Shot Electronic Health Record Coding through Graph Contrastive Learning,Shanshan Wang,http://arxiv.org/pdf/2106.15467v1.pdf,2021-06-29,"['cs.ai', 'cs.cl']",2106.15467v1.pdf," Electronic health record (EHR) coding is the task of assigning ICD codes to +each EHR. Most previous studies either only focus on the frequent ICD codes or +treat rare and frequent ICD codes in the same way. These methods perform well +on frequent ICD codes but due to the extremely unbalanced distribution of ICD +codes, the performance on rare ones is far from satisfactory. We seek to +improve the performance for both frequent and rare ICD codes by using a +contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR +coding as a few-shot learning task. First, we construct a heterogeneous EHR +word-entity (HEWE) graph for each EHR, where the words and entities extracted +from an EHR serve as nodes and the relations between them serve as edges. Then, +CoGraph learns similarities and dissimilarities between HEWE graphs from +different ICD codes so that information can be transferred among them. In a +few-shot learning scenario, the model only has access to frequent ICD codes +during training, which might force it to encode features that are useful for +frequent ICD codes only. To mitigate this risk, CoGraph devises two graph +contrastive learning schemes, GSCL and GECL, that exploit the HEWE graph +structures so as to encode transferable features. GSCL utilizes the +intra-correlation of different sub-graphs sampled from HEWE graphs while GECL +exploits the inter-correlation among HEWE graphs at different clinical stages. +Experiments on the MIMIC-III benchmark dataset show that CoGraph significantly +outperforms state-of-the-art methods on EHR coding, not only on frequent ICD +codes, but also on rare codes, in terms of several evaluation indicators. On +frequent ICD codes, GSCL and GECL improve the classification accuracy and F1 by +1.31% and 0.61%, respectively, and on rare ICD codes CoGraph has more obvious +improvements by 2.12% and 2.95%. +" +ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation,Yu Sun,http://arxiv.org/pdf/2107.02137v1.pdf,2021-07-05,['cs.cl'],2107.02137v1.pdf," Pre-trained models have achieved state-of-the-art results in various Natural +Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown +that scaling up pre-trained language models can improve their generalization +abilities. Particularly, the GPT-3 model with 175 billion parameters shows its +strong task-agnostic zero-shot/few-shot learning capabilities. Despite their +success, these large-scale models are trained on plain texts without +introducing knowledge such as linguistic knowledge and world knowledge. In +addition, most large-scale models are trained in an auto-regressive way. As a +result, this kind of traditional fine-tuning approach demonstrates relatively +weak performance when solving downstream language understanding tasks. In order +to solve the above problems, we propose a unified framework named ERNIE 3.0 for +pre-training large-scale knowledge enhanced models. It fuses auto-regressive +network and auto-encoding network, so that the trained model can be easily +tailored for both natural language understanding and generation tasks with +zero-shot learning, few-shot learning or fine-tuning. We trained the model with +10 billion parameters on a 4TB corpus consisting of plain texts and a +large-scale knowledge graph. Empirical results show that the model outperforms +the state-of-the-art models on 54 Chinese NLP tasks, and its English version +achieves the first place on the SuperGLUE benchmark (July 3, 2021), surpassing +the human performance by +0.8% (90.6% vs. 89.8%). +" +UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models,Tianbao Xie,http://arxiv.org/pdf/2201.05966v3.pdf,2022-01-16,['cs.cl'],2201.05966v3.pdf," Structured knowledge grounding (SKG) leverages structured knowledge to +complete user requests, such as semantic parsing over databases and question +answering over knowledge bases. Since the inputs and outputs of SKG tasks are +heterogeneous, they have been studied separately by different communities, +which limits systematic and compatible research on SKG. In this paper, we +overcome this limitation by proposing the UnifiedSKG framework, which unifies +21 SKG tasks into a text-to-text format, aiming to promote systematic SKG +research, instead of being exclusive to a single task, domain, or dataset. We +use UnifiedSKG to benchmark T5 with different sizes and show that T5, with +simple modifications when necessary, achieves state-of-the-art performance on +almost all of the 21 tasks. We further demonstrate that multi-task +prefix-tuning improves the performance on most tasks, largely improving the +overall performance. UnifiedSKG also facilitates the investigation of zero-shot +and few-shot learning, and we show that T0, GPT-3, and Codex struggle in +zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a +series of controlled experiments on structured knowledge encoding variants +across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is +open-sourced at https://github.com/hkunlp/unifiedskg. +" +A Prompt-based Few-shot Learning Approach to Software Conflict Detection,Robert K. Helmeczi,http://arxiv.org/pdf/2211.02709v1.pdf,2022-11-04,['cs.se'],2211.02709v1.pdf," A software requirement specification (SRS) document is an essential part of +the software development life cycle which outlines the requirements that a +software program in development must satisfy. This document is often specified +by a diverse group of stakeholders and is subject to continual change, making +the process of maintaining the document and detecting conflicts between +requirements an essential task in software development. Notably, projects that +do not address conflicts in the SRS document early on face considerable +problems later in the development life cycle. These problems incur substantial +costs in terms of time and money, and these costs often become insurmountable +barriers that ultimately result in the termination of a software project +altogether. As a result, early detection of SRS conflicts is critical to +project sustainability. The conflict detection task is approached in numerous +ways, many of which require a significant amount of manual intervention from +developers, or require access to a large amount of labeled, task-specific +training data. In this work, we propose using a prompt-based learning approach +to perform few-shot learning for conflict detection. We compare our results to +supervised learning approaches that use pretrained language models, such as +BERT and its variants. Our results show that prompting with just 32 labeled +examples can achieve a similar level of performance in many key metrics to that +of supervised learning on training sets that are magnitudes larger in size. In +contrast to many other conflict detection approaches, we make no assumptions +about the type of underlying requirements, allowing us to analyze pairings of +both functional and non-functional requirements. This allows us to omit the +potentially expensive task of filtering out non-functional requirements from +our dataset. +" +"Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing",Tal Schuster,http://arxiv.org/pdf/1902.09492v2.pdf,2019-02-25,"['cs.cl', 'cs.lg']",1902.09492v2.pdf," We introduce a novel method for multilingual transfer that utilizes deep +contextual embeddings, pretrained in an unsupervised fashion. While contextual +embeddings have been shown to yield richer representations of meaning compared +to their static counterparts, aligning them poses a challenge due to their +dynamic nature. To this end, we construct context-independent variants of the +original monolingual spaces and utilize their mapping to derive an alignment +for the context-dependent spaces. This mapping readily supports processing of a +target language, improving transfer by context-aware embeddings. Our +experimental results demonstrate the effectiveness of this approach for +zero-shot and few-shot learning of dependency parsing. Specifically, our method +consistently outperforms the previous state-of-the-art on 6 tested languages, +yielding an improvement of 6.8 LAS points on average. +" +Few-shot Natural Language Generation for Task-Oriented Dialog,Baolin Peng,http://arxiv.org/pdf/2002.12328v1.pdf,2020-02-27,['cs.cl'],2002.12328v1.pdf," As a crucial component in task-oriented dialog systems, the Natural Language +Generation (NLG) module converts a dialog act represented in a semantic form +into a response in natural language. The success of traditional template-based +or statistical models typically relies on heavily annotated data, which is +infeasible for new domains. Therefore, it is pivotal for an NLG system to +generalize well with limited labelled data in real applications. To this end, +we present FewShotWoz, the first NLG benchmark to simulate the few-shot +learning setting in task-oriented dialog systems. Further, we develop the +SC-GPT model. It is pre-trained on a large set of annotated NLG corpus to +acquire the controllable generation ability, and fine-tuned with only a few +domain-specific labels to adapt to new domains. Experiments on FewShotWoz and +the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly +outperforms existing methods, measured by various automatic metrics and human +evaluations. +" +Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification,Wei Zhu,http://arxiv.org/pdf/2004.09694v1.pdf,2020-04-21,"['eess.iv', 'cs.cv', 'cs.lg']",2004.09694v1.pdf," Skin disease classification from images is crucial to dermatological +diagnosis. However, identifying skin lesions involves a variety of aspects in +terms of size, color, shape, and texture. To make matters worse, many +categories only contain very few samples, posing great challenges to +conventional machine learning algorithms and even human experts. Inspired by +the recent success of Few-Shot Learning (FSL) in natural image classification, +we propose to apply FSL to skin disease identification to address the extreme +scarcity of training sample problem. However, directly applying FSL to this +task does not work well in practice, and we find that the problem can be +largely attributed to the incompatibility between Cross Entropy (CE) and +episode training, which are both commonly used in FSL. Based on a detailed +analysis, we propose the Query-Relative (QR) loss, which proves superior to CE +under episode training and is closely related to recently proposed mutual +information estimation. Moreover, we further strengthen the proposed QR loss +with a novel adaptive hard margin strategy. Comprehensive experiments validate +the effectiveness of the proposed FSL scheme and the possibility to diagnosis +rare skin disease with a few labeled samples. +" +Few-shot learning through contextual data augmentation,Farid Arthaud,http://arxiv.org/pdf/2103.16911v1.pdf,2021-03-31,['cs.cl'],2103.16911v1.pdf," Machine translation (MT) models used in industries with constantly changing +topics, such as translation or news agencies, need to adapt to new data to +maintain their performance over time. Our aim is to teach a pre-trained MT +model to translate previously unseen words accurately, based on very few +examples. We propose (i) an experimental setup allowing us to simulate novel +vocabulary appearing in human-submitted translations, and (ii) corresponding +evaluation metrics to compare our approaches. We extend a data augmentation +approach using a pre-trained language model to create training examples with +similar contexts for novel words. We compare different fine-tuning and data +augmentation approaches and show that adaptation on the scale of one to five +examples is possible. Combining data augmentation with randomly selected +training sentences leads to the highest BLEU score and accuracy improvements. +Impressively, with only 1 to 5 examples, our model reports better accuracy +scores than a reference system trained with on average 313 parallel examples. +" +Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction,Cuong Q. Nguyen,http://arxiv.org/pdf/2003.05996v2.pdf,2020-03-12,"['cs.lg', 'physics.chem-ph', 'stat.ml']",2003.05996v2.pdf," Building in silico models to predict chemical properties and activities is a +crucial step in drug discovery. However, limited labeled data often hinders the +application of deep learning in this setting. Meanwhile advances in +meta-learning have enabled state-of-the-art performances in few-shot learning +benchmarks, naturally prompting the question: Can meta-learning improve deep +learning performance in low-resource drug discovery projects? In this work, we +assess the transferability of graph neural networks initializations learned by +the Model-Agnostic Meta-Learning (MAML) algorithm - and its variants FO-MAML +and ANIL - for chemical properties and activities tasks. Using the ChEMBL20 +dataset to emulate low-resource settings, our benchmark shows that +meta-initializations perform comparably to or outperform multi-task +pre-training baselines on 16 out of 20 in-distribution tasks and on all +out-of-distribution tasks, providing an average improvement in AUPRC of 11.2% +and 26.9% respectively. Finally, we observe that meta-initializations +consistently result in the best performing models across fine-tuning sets with +$k \in \{16, 32, 64, 128, 256\}$ instances. +" +Neural Data Augmentation via Example Extrapolation,Kenton Lee,http://arxiv.org/pdf/2102.01335v1.pdf,2021-02-02,"['cs.cl', 'cs.ai']",2102.01335v1.pdf," In many applications of machine learning, certain categories of examples may +be underrepresented in the training data, causing systems to underperform on +such ""few-shot"" cases at test time. A common remedy is to perform data +augmentation, such as by duplicating underrepresented examples, or +heuristically synthesizing new examples. But these remedies often fail to cover +the full diversity and complexity of real examples. + We propose a data augmentation approach that performs neural Example +Extrapolation (Ex2). Given a handful of exemplars sampled from some +distribution, Ex2 synthesizes new examples that also belong to the same +distribution. The Ex2 model is learned by simulating the example generation +procedure on data-rich slices of the data, and it is applied to +underrepresented, few-shot slices. + We apply Ex2 to a range of language understanding tasks and significantly +improve over state-of-the-art methods on multiple few-shot learning benchmarks, +including for relation extraction (FewRel) and intent classification + slot +filling (SNIPS). +" +One-shot learning for the long term: consolidation with an artificial hippocampal algorithm,Gideon Kowadlo,http://arxiv.org/pdf/2102.07503v2.pdf,2021-02-15,"['cs.lg', 'cs.ai', 'cs.ne', 'i.2.6; i.5.0; i.5.1']",2102.07503v2.pdf," Standard few-shot experiments involve learning to efficiently match +previously unseen samples by class. We claim that few-shot learning should be +long term, assimilating knowledge for the future, without forgetting previous +concepts. In the mammalian brain, the hippocampus is understood to play a +significant role in this process, by learning rapidly and consolidating +knowledge to the neocortex incrementally over a short period. In this research +we tested whether an artificial hippocampal algorithm (AHA), could be used with +a conventional Machine Learning (ML) model that learns incrementally analogous +to the neocortex, to achieve one-shot learning both short and long term. The +results demonstrated that with the addition of AHA, the system could learn in +one-shot and consolidate the knowledge for the long term without catastrophic +forgetting. This study is one of the first examples of using a CLS model of +hippocampus to consolidate memories, and it constitutes a step toward few-shot +continual learning. +" +Calibrate Before Use: Improving Few-Shot Performance of Language Models,Tony Z. Zhao,http://arxiv.org/pdf/2102.09690v2.pdf,2021-02-19,"['cs.cl', 'cs.lg']",2102.09690v2.pdf," GPT-3 can perform numerous tasks when provided a natural language prompt that +contains a few training examples. We show that this type of few-shot learning +can be unstable: the choice of prompt format, training examples, and even the +order of the training examples can cause accuracy to vary from near chance to +near state-of-the-art. We demonstrate that this instability arises from the +bias of language models towards predicting certain answers, e.g., those that +are placed near the end of the prompt or are common in the pre-training data. +To mitigate this, we first estimate the model's bias towards each answer by +asking for its prediction when given the training prompt and a content-free +test input such as ""N/A"". We then fit calibration parameters that cause the +prediction for this input to be uniform across answers. On a diverse set of +tasks, this contextual calibration procedure substantially improves GPT-3 and +GPT-2's average accuracy (up to 30.0% absolute) and reduces variance across +different choices of the prompt. +" +The Power of Scale for Parameter-Efficient Prompt Tuning,Brian Lester,http://arxiv.org/pdf/2104.08691v2.pdf,2021-04-18,['cs.cl'],2104.08691v2.pdf," In this work, we explore ""prompt tuning"", a simple yet effective mechanism +for learning ""soft prompts"" to condition frozen language models to perform +specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft +prompts are learned through backpropagation and can be tuned to incorporate +signal from any number of labeled examples. Our end-to-end learned approach +outperforms GPT-3's ""few-shot"" learning by a large margin. More remarkably, +through ablations on model size using T5, we show that prompt tuning becomes +more competitive with scale: as models exceed billions of parameters, our +method ""closes the gap"" and matches the strong performance of model tuning +(where all model weights are tuned). This finding is especially relevant in +that large models are costly to share and serve, and the ability to reuse one +frozen model for multiple downstream tasks can ease this burden. Our method can +be seen as a simplification of the recently proposed ""prefix tuning"" of Li and +Liang (2021), and we provide a comparison to this and other similar approaches. +Finally, we show that conditioning a frozen model with soft prompts confers +benefits in robustness to domain transfer, as compared to full model tuning. +" +What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval,Curt Kohler,http://arxiv.org/pdf/2106.14720v1.pdf,2021-06-28,['cs.cl'],2106.14720v1.pdf," In the summer of 2020 OpenAI released its GPT-3 autoregressive language model +to much fanfare. While the model has shown promise on tasks in several areas, +it has not always been clear when the results were cherry-picked or when they +were the unvarnished output. We were particularly interested in what benefits +GPT-3 could bring to the SemEval 2021 MeasEval task - identifying measurements +and their associated attributes in scientific literature. We had already +experimented with multi-turn questions answering as a solution to this task. We +wanted to see if we could use GPT-3's few-shot learning capabilities to more +easily develop a solution that would have better performance than our prior +work. Unfortunately, we have not been successful in that effort. This paper +discusses the approach we used, challenges we encountered, and results we +observed. Some of the problems we encountered were simply due to the state of +the art. For example, the limits on the size of the prompt and answer limited +the amount of the training signal that could be offered. Others are more +fundamental. We are unaware of generative models that excel in retaining +factual information. Also, the impact of changes in the prompts is +unpredictable, making it hard to reliably improve performance. +" +FLEX: Unifying Evaluation for Few-Shot NLP,Jonathan Bragg,http://arxiv.org/pdf/2107.07170v2.pdf,2021-07-15,"['cs.cl', 'cs.lg', 'i.2.7']",2107.07170v2.pdf," Few-shot NLP research is highly active, yet conducted in disjoint research +threads with evaluation suites that lack challenging-yet-realistic testing +setups and fail to employ careful experimental design. Consequently, the +community does not know which techniques perform best or even if they +outperform simple baselines. In response, we formulate the FLEX Principles, a +set of requirements and best practices for unified, rigorous, valid, and +cost-sensitive few-shot NLP evaluation. These principles include Sample Size +Design, a novel approach to benchmark design that optimizes statistical +accuracy and precision while keeping evaluation costs manageable. Following the +principles, we release the FLEX benchmark, which includes four few-shot +transfer settings, zero-shot evaluation, and a public leaderboard that covers +diverse NLP tasks. In addition, we present UniFew, a prompt-based model for +few-shot learning that unifies pretraining and finetuning prompt formats, +eschewing complex machinery of recent prompt-based approaches in adapting +downstream task formats to language model pretraining objectives. We +demonstrate that despite simplicity, UniFew achieves results competitive with +both popular meta-learning and prompt-based approaches. +" +Wordcraft: a Human-AI Collaborative Editor for Story Writing,Andy Coenen,http://arxiv.org/pdf/2107.07430v1.pdf,2021-07-15,['cs.cl'],2107.07430v1.pdf," As neural language models grow in effectiveness, they are increasingly being +applied in real-world settings. However these applications tend to be limited +in the modes of interaction they support. In this extended abstract, we propose +Wordcraft, an AI-assisted editor for story writing in which a writer and a +dialog system collaborate to write a story. Our novel interface uses few-shot +learning and the natural affordances of conversation to support a variety of +interactions. Our editor provides a sandbox for writers to probe the boundaries +of transformer-based language models and paves the way for future +human-in-the-loop training pipelines and novel evaluation methods. +" +Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data,Christina Doty,http://arxiv.org/pdf/2107.10387v1.pdf,2021-07-21,"['cond-mat.mtrl-sci', 'cs.lg']",2107.10387v1.pdf," The recent growth in data volumes produced by modern electron microscopes +requires rapid, scalable, and flexible approaches to image segmentation and +analysis. Few-shot machine learning, which can richly classify images from a +handful of user-provided examples, is a promising route to high-throughput +analysis. However, current command-line implementations of such approaches can +be slow and unintuitive to use, lacking the real-time feedback necessary to +perform effective classification. Here we report on the development of a +Python-based graphical user interface that enables end users to easily conduct +and visualize the output of few-shot learning models. This interface is +lightweight and can be hosted locally or on the web, providing the opportunity +to reproducibly conduct, share, and crowd-source few-shot analyses. +" +Noisy Channel Language Model Prompting for Few-Shot Text Classification,Sewon Min,http://arxiv.org/pdf/2108.04106v3.pdf,2021-08-09,"['cs.cl', 'cs.ai']",2108.04106v3.pdf," We introduce a noisy channel approach for language model prompting in +few-shot text classification. Instead of computing the likelihood of the label +given the input (referred as direct models), channel models compute the +conditional probability of the input given the label, and are thereby required +to explain every word in the input. We use channel models for recently proposed +few-shot learning methods with no or very limited updates to the language model +parameters, via either in-context demonstration or prompt tuning. Our +experiments show that, for both methods, channel models significantly +outperform their direct counterparts, which we attribute to their stability, +i.e., lower variance and higher worst-case accuracy. We also present extensive +ablations that provide recommendations for when to use channel prompt tuning +instead of other competitive methods (e.g., direct head tuning): channel prompt +tuning is preferred when the number of training examples is small, labels in +the training data are imbalanced, or generalization to unseen labels is +required. +" +FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning,Jing Zhou,http://arxiv.org/pdf/2108.06332v2.pdf,2021-08-13,['cs.cl'],2108.06332v2.pdf," Most previous methods for text data augmentation are limited to simple tasks +and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot +natural language understanding) and strong baselines (i.e., pretrained models +with over one billion parameters). Under this setting, we reproduced a large +number of previous augmentation methods and found that these methods bring +marginal gains at best and sometimes degrade the performance much. To address +this challenge, we propose a novel data augmentation method FlipDA that jointly +uses a generative model and a classifier to generate label-flipped data. +Central to the idea of FlipDA is the discovery that generating label-flipped +data is more crucial to the performance than generating label-preserved data. +Experiments show that FlipDA achieves a good tradeoff between effectiveness and +robustness -- it substantially improves many tasks while not negatively +affecting the others. +" +On the Multilingual Capabilities of Very Large-Scale English Language Models,Jordi Armengol-Estapé,http://arxiv.org/pdf/2108.13349v1.pdf,2021-08-30,"['cs.cl', 'cs.ai']",2108.13349v1.pdf," Generative Pre-trained Transformers (GPTs) have recently been scaled to +unprecedented sizes in the history of machine learning. These models, solely +trained on the language modeling objective, have been shown to exhibit +outstanding few-shot learning capabilities in a number of different tasks. +Nevertheless, aside from anecdotal experiences, little is known regarding their +multilingual capabilities, given the fact that the pre-training corpus is +almost entirely composed of English text. In this work, we investigate the +multilingual skills of GPT-3, focusing on one language that barely appears in +the pre-training corpus, Catalan, which makes the results especially +meaningful; we assume that our results may be relevant for other languages as +well. We find that the model shows an outstanding performance, particularly in +generative tasks, with predictable limitations mostly in language understanding +tasks but still with remarkable results given the zero-shot scenario. We +investigate its potential and limits in extractive question-answering and +natural language generation, as well as the effect of scale in terms of model +size. +" +Want To Reduce Labeling Cost? GPT-3 Can Help,Shuohang Wang,http://arxiv.org/pdf/2108.13487v1.pdf,2021-08-30,"['cs.cl', 'cs.ai']",2108.13487v1.pdf," Data annotation is a time-consuming and labor-intensive process for many NLP +tasks. Although there exist various methods to produce pseudo data labels, they +are often task-specific and require a decent amount of labeled data to start +with. Recently, the immense language model GPT-3 with 175 billion parameters +has achieved tremendous improvement across many few-shot learning tasks. In +this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to +train other models. We find that, to make the downstream model achieve the same +performance on a variety of NLU and NLG tasks, it costs 50% to 96% less to use +labels from GPT-3 than using labels from humans. Furthermore, we propose a +novel framework of combining pseudo labels from GPT-3 with human labels, which +leads to even better performance with limited labeling budget. These results +present a cost-effective data labeling methodology that is generalizable to +many practical applications. +" +ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation,Eyup Halit Yilmaz,http://arxiv.org/pdf/2109.00729v1.pdf,2021-09-02,"['cs.cl', 'cs.ai']",2109.00729v1.pdf," Intent detection of spoken queries is a challenging task due to their noisy +structure and short length. To provide additional information regarding the +query and enhance the performance of intent detection, we propose a method for +semantic expansion of spoken queries, called ConQX, which utilizes the text +generation ability of an auto-regressive language model, GPT-2. To avoid +off-topic text generation, we condition the input query to a structured context +with prompt mining. We then apply zero-shot, one-shot, and few-shot learning. +We lastly use the expanded queries to fine-tune BERT and RoBERTa for intent +detection. The experimental results show that the performance of intent +detection can be improved by our semantic expansion method. +" +Do Prompt-Based Models Really Understand the Meaning of their Prompts?,Albert Webson,http://arxiv.org/pdf/2109.01247v2.pdf,2021-09-02,['cs.cl'],2109.01247v2.pdf," Recently, a boom of papers has shown extraordinary progress in zero-shot and +few-shot learning with various prompt-based models. It is commonly argued that +prompts help models to learn faster in the same way that humans learn faster +when provided with task instructions expressed in natural language. In this +study, we experiment with over 30 prompt templates manually written for natural +language inference (NLI). We find that models learn just as fast with many +prompts that are intentionally irrelevant or even pathologically misleading as +they do with instructively ""good"" prompts. Further, such patterns hold even for +models as large as 175 billion parameters (Brown et al., 2020) as well as the +recently proposed instruction-tuned models which are trained on hundreds of +prompts (Sanh et al., 2022). That is, instruction-tuned models often produce +good predictions with irrelevant and misleading prompts even at zero shots. In +sum, notwithstanding prompt-based models' impressive improvement, we find +evidence of serious limitations that question the degree to which such +improvement is derived from models understanding task instructions in ways +analogous to humans' use of task instructions. +" +Learning Opinion Summarizers by Selecting Informative Reviews,Arthur Bražinskas,http://arxiv.org/pdf/2109.04325v1.pdf,2021-09-09,"['cs.cl', 'cs.ai', 'cs.lg']",2109.04325v1.pdf," Opinion summarization has been traditionally approached with unsupervised, +weakly-supervised and few-shot learning techniques. In this work, we collect a +large dataset of summaries paired with user reviews for over 31,000 products, +enabling supervised training. However, the number of reviews per product is +large (320 on average), making summarization - and especially training a +summarizer - impractical. Moreover, the content of many reviews is not +reflected in the human-written summaries, and, thus, the summarizer trained on +random review subsets hallucinates. In order to deal with both of these +challenges, we formulate the task as jointly learning to select informative +subsets of reviews and summarizing the opinions expressed in these subsets. The +choice of the review subset is treated as a latent variable, predicted by a +small and simple selector. The subset is then fed into a more powerful +summarizer. For joint training, we use amortized variational inference and +policy gradient methods. Our experiments demonstrate the importance of +selecting informative reviews resulting in improved quality of summaries and +reduced hallucinations. +" +STraTA: Self-Training with Task Augmentation for Better Few-shot Learning,Tu Vu,http://arxiv.org/pdf/2109.06270v2.pdf,2021-09-13,['cs.cl'],2109.06270v2.pdf," Despite their recent successes in tackling many NLP tasks, large-scale +pre-trained language models do not perform as well in few-shot settings where +only a handful of training examples are available. To address this shortcoming, +we propose STraTA, which stands for Self-Training with Task Augmentation, an +approach that builds on two key ideas for effective leverage of unlabeled data. +First, STraTA uses task augmentation, a novel technique that synthesizes a +large amount of data for auxiliary-task fine-tuning from target-task unlabeled +texts. Second, STraTA performs self-training by further fine-tuning the strong +base model created by task augmentation on a broad distribution of +pseudo-labeled data. Our experiments demonstrate that STraTA can substantially +improve sample efficiency across 12 few-shot benchmarks. Remarkably, on the +SST-2 sentiment dataset, STraTA, with only 8 training examples per class, +achieves comparable results to standard fine-tuning with 67K training examples. +Our analyses reveal that task augmentation and self-training are both +complementary and independently effective. +" +Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks,Gaël Guibon,http://arxiv.org/pdf/2109.09366v1.pdf,2021-09-20,"['cs.cl', 'cs.lg']",2109.09366v1.pdf," Several recent studies on dyadic human-human interactions have been done on +conversations without specific business objectives. However, many companies +might benefit from studies dedicated to more precise environments such as after +sales services or customer satisfaction surveys. In this work, we place +ourselves in the scope of a live chat customer service in which we want to +detect emotions and their evolution in the conversation flow. This context +leads to multiple challenges that range from exploiting restricted, small and +mostly unlabeled datasets to finding and adapting methods for such context.We +tackle these challenges by using Few-Shot Learning while making the hypothesis +it can serve conversational emotion classification for different languages and +sparse labels. We contribute by proposing a variation of Prototypical Networks +for sequence labeling in conversation that we name ProtoSeq. We test this +method on two datasets with different languages: daily conversations in English +and customer service chat conversations in French. When applied to emotion +classification in conversations, our method proved to be competitive even when +compared to other ones. +" +UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis,Fatemehsadat Mireshghallah,http://arxiv.org/pdf/2110.00135v2.pdf,2021-10-01,"['cs.lg', 'cs.ai', 'cs.cl']",2110.00135v2.pdf," Global models are trained to be as generalizable as possible, with user +invariance considered desirable since the models are shared across multitudes +of users. As such, these models are often unable to produce personalized +responses for individual users, based on their data. Contrary to widely-used +personalization techniques based on few-shot learning, we propose +UserIdentifier, a novel scheme for training a single shared model for all +users. Our approach produces personalized responses by adding fixed, +non-trainable user identifiers to the input data. We empirically demonstrate +that this proposed method outperforms the prefix-tuning based state-of-the-art +approach by up to 13%, on a suite of sentiment analysis datasets. We also show +that, unlike prior work, this method needs neither any additional model +parameters nor any extra rounds of few-shot fine-tuning. +" +Instance-aware Prompt Learning for Language Understanding and Generation,Feihu Jin,http://arxiv.org/pdf/2201.07126v1.pdf,2022-01-18,['cs.cl'],2201.07126v1.pdf," Recently, prompt learning has become a new paradigm to utilize pre-trained +language models (PLMs) and achieves promising results in downstream tasks with +a negligible increase of parameters. The current usage of discrete and +continuous prompts assumes that the prompt is fixed for a specific task and all +samples in the task share the same prompt. However, a task may contain quite +diverse samples in which some are easy and others are difficult, and diverse +prompts are desirable. In this paper, we propose an instance-aware prompt +learning method that learns a different prompt for each instance. Specifically, +we suppose that each learnable prompt token has a different contribution to +different instances, and we learn the contribution by calculating the relevance +score between an instance and each prompt token. The contribution weighted +prompt would be instance aware. We apply our method to both unidirectional and +bidirectional PLMs on both language understanding and generation tasks. +Extensive experiments demonstrate that our method obtains considerable +improvements compared to strong baselines. Especially, our method achieves the +state-of-the-art on the SuperGLUE few-shot learning benchmark. +" +Generating Training Data with Language Models: Towards Zero-Shot Language Understanding,Yu Meng,http://arxiv.org/pdf/2202.04538v2.pdf,2022-02-09,"['cs.cl', 'cs.lg']",2202.04538v2.pdf," Pretrained language models (PLMs) have demonstrated remarkable performance in +various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are +well known for their superior text generation capabilities; bidirectional PLMs +(e.g., BERT) have been the prominent choice for natural language understanding +(NLU) tasks. While both types of models have achieved promising few-shot +learning performance, their potential for zero-shot learning has been +underexplored. In this paper, we present a simple approach that uses both types +of PLMs for fully zero-shot learning of NLU tasks without requiring any +task-specific data: A unidirectional PLM generates class-conditioned texts +guided by prompts, which are used as the training data for fine-tuning a +bidirectional PLM. With quality training data selected based on the generation +probability and regularization techniques (label smoothing and temporal +ensembling) applied to the fine-tuning stage for better generalization and +stability, our approach demonstrates strong performance across seven +classification tasks of the GLUE benchmark (e.g., 72.3/73.8 on MNLI-m/mm and +92.8 on SST-2), significantly outperforming zero-shot prompting methods and +achieving even comparable results to strong few-shot approaches using 32 +training samples per class. +" +Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation,Zhuang Li,http://arxiv.org/pdf/2202.13363v3.pdf,2022-02-27,['cs.cl'],2202.13363v3.pdf," In this paper, we propose a variational autoencoder with disentanglement +priors, VAE-DPRIOR, for task-specific natural language generation with none or +a handful of task-specific labeled examples. In order to tackle compositional +generalization across tasks, our model performs disentangled representation +learning by introducing a conditional prior for the latent content space and +another conditional prior for the latent label space. Both types of priors +satisfy a novel property called $\epsilon$-disentangled. We show both +empirically and theoretically that the novel priors can disentangle +representations even without specific regularizations as in the prior work. The +content prior enables directly sampling diverse content representations from +the content space learned from the seen tasks, and fuse them with the +representations of novel tasks for generating semantically diverse texts in the +low-resource settings. Our extensive experiments demonstrate the superior +performance of our model over competitive baselines in terms of i) data +augmentation in continuous zero/few-shot learning, and ii) text style transfer +in the few-shot setting. +" +ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification,Yucheng Zhou,http://arxiv.org/pdf/2203.02225v2.pdf,2022-03-04,['cs.cl'],2203.02225v2.pdf," Generating new events given context with correlated ones plays a crucial role +in many event-centric reasoning tasks. Existing works either limit their scope +to specific scenarios or overlook event-level correlations. In this paper, we +propose to pre-train a general Correlation-aware context-to-Event Transformer +(ClarET) for event-centric reasoning. To achieve this, we propose three novel +event-centric objectives, i.e., whole event recovering, contrastive +event-correlation encoding and prompt-based event locating, which highlight +event-level correlations with effective training. The proposed ClarET is +applicable to a wide range of event-centric reasoning scenarios, considering +its versatility of (i) event-correlation types (e.g., causal, temporal, +contrast), (ii) application formulations (i.e., generation and classification), +and (iii) reasoning types (e.g., abductive, counterfactual and ending +reasoning). Empirical fine-tuning results, as well as zero- and few-shot +learning, on 9 benchmarks (5 generation and 4 classification tasks covering 4 +reasoning types with diverse event correlations), verify its effectiveness and +generalization ability. +" +Pre-trained Token-replaced Detection Model as Few-shot Learner,Zicheng Li,http://arxiv.org/pdf/2203.03235v2.pdf,2022-03-07,"['cs.cl', 'cs.ai']",2203.03235v2.pdf," Pre-trained masked language models have demonstrated remarkable ability as +few-shot learners. In this paper, as an alternative, we propose a novel +approach to few-shot learning with pre-trained token-replaced detection models +like ELECTRA. In this approach, we reformulate a classification or a regression +task as a token-replaced detection problem. Specifically, we first define a +template and label description words for each task and put them into the input +to form a natural language prompt. Then, we employ the pre-trained +token-replaced detection model to predict which label description word is the +most original (i.e., least replaced) among all label description words in the +prompt. A systematic evaluation on 16 datasets demonstrates that our approach +outperforms few-shot learners with pre-trained masked language models in both +one-sentence and two-sentence learning tasks. +" +InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER,Liwen Wang,http://arxiv.org/pdf/2203.03903v1.pdf,2022-03-08,['cs.cl'],2203.03903v1.pdf," Recently, prompt-based methods have achieved significant performance in +few-shot learning scenarios by bridging the gap between language model +pre-training and fine-tuning for downstream tasks. However, existing prompt +templates are mostly designed for sentence-level tasks and are inappropriate +for sequence labeling objectives. To address the above issue, we propose a +multi-task instruction-based generative framework, named InstructionNER, for +low-resource named entity recognition. Specifically, we reformulate the NER +task as a generation problem, which enriches source sentences with +task-specific instructions and answer options, then inferences the entities and +types in natural language. We further propose two auxiliary tasks, including +entity extraction and entity typing, which enable the model to capture more +boundary information of entities and deepen the understanding of entity type +semantics, respectively. Experimental results show that our method consistently +outperforms other baselines on five datasets in few-shot settings. +" +Prototypical Verbalizer for Prompt-based Few-shot Tuning,Ganqu Cui,http://arxiv.org/pdf/2203.09770v1.pdf,2022-03-18,"['cs.cl', 'cs.lg']",2203.09770v1.pdf," Prompt-based tuning for pre-trained language models (PLMs) has shown its +effectiveness in few-shot learning. Typically, prompt-based tuning wraps the +input text into a cloze question. To make predictions, the model maps the +output words to labels via a verbalizer, which is either manually designed or +automatically built. However, manual verbalizers heavily depend on +domain-specific prior knowledge and human efforts, while finding appropriate +label words automatically still remains challenging.In this work, we propose +the prototypical verbalizer (ProtoVerb) which is built directly from training +data. Specifically, ProtoVerb learns prototype vectors as verbalizers by +contrastive learning. In this way, the prototypes summarize training instances +and are able to enclose rich class-level semantics. We conduct experiments on +both topic classification and entity typing tasks, and the results demonstrate +that ProtoVerb significantly outperforms current automatic verbalizers, +especially when training data is extremely scarce. More surprisingly, ProtoVerb +consistently boosts prompt-based tuning even on untuned PLMs, indicating an +elegant non-tuning way to utilize PLMs. Our codes are avaliable at +https://github.com/thunlp/OpenPrompt. +" +Few-Shot Learning with Siamese Networks and Label Tuning,Thomas Müller,http://arxiv.org/pdf/2203.14655v2.pdf,2022-03-28,"['cs.cl', 'cs.lg']",2203.14655v2.pdf," We study the problem of building text classifiers with little or no training +data, commonly known as zero and few-shot text classification. In recent years, +an approach based on neural textual entailment models has been found to give +strong results on a diverse range of tasks. In this work, we show that with +proper pre-training, Siamese Networks that embed texts and labels offer a +competitive alternative. These models allow for a large reduction in inference +cost: constant in the number of labels rather than linear. Furthermore, we +introduce label tuning, a simple and computationally efficient approach that +allows to adapt the models in a few-shot setup by only changing the label +embeddings. While giving lower performance than model fine-tuning, this +approach has the architectural advantage that a single encoder can be shared by +many different tasks. +" +Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging,Yutai Hou,http://arxiv.org/pdf/2204.00885v1.pdf,2022-04-02,"['cs.cl', 'cs.ai']",2204.00885v1.pdf," Prompting methods recently achieve impressive success in few-shot learning. +These methods modify input samples with prompt sentence pieces, and decode +label tokens to map samples to corresponding labels. However, such a paradigm +is very inefficient for the task of slot tagging. Since slot tagging samples +are multiple consecutive words in a sentence, the prompting methods have to +enumerate all n-grams token spans to find all the possible slots, which greatly +slows down the prediction. To tackle this, we introduce an inverse paradigm for +prompting. Different from the classic prompts mapping tokens to labels, we +reversely predict slot values given slot types. Such inverse prompting only +requires a one-turn prediction for each slot type and greatly speeds up the +prediction. Besides, we propose a novel Iterative Prediction Strategy, from +which the model learns to refine predictions by considering the relations +between different slot types. We find, somewhat surprisingly, the proposed +method not only predicts faster but also significantly improves the effect +(improve over 6.1 F1-scores on 10-shot setting) and achieves new +state-of-the-art performance. +" +Leveraging pre-trained language models for conversational information seeking from text,Patrizio Bellan,http://arxiv.org/pdf/2204.03542v1.pdf,2022-03-31,"['cs.cl', 'cs.ai']",2204.03542v1.pdf," Recent advances in Natural Language Processing, and in particular on the +construction of very large pre-trained language representation models, is +opening up new perspectives on the construction of conversational information +seeking (CIS) systems. In this paper we investigate the usage of in-context +learning and pre-trained language representation models to address the problem +of information extraction from process description documents, in an incremental +question and answering oriented fashion. In particular we investigate the usage +of the native GPT-3 (Generative Pre-trained Transformer 3) model, together with +two in-context learning customizations that inject conceptual definitions and a +limited number of samples in a few shot-learning fashion. The results highlight +the potential of the approach and the usefulness of the in-context learning +customizations, which can substantially contribute to address the ""training +data challenge"" of deep learning based NLP techniques the BPM field. It also +highlight the challenge posed by control flow relations for which further +training needs to be devised. +" +MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification,Jianhai Zhang,http://arxiv.org/pdf/2204.04952v3.pdf,2022-04-11,['cs.cl'],2204.04952v3.pdf," Text classification struggles to generalize to unseen classes with very few +labeled text instances per class. In such a few-shot learning (FSL) setting, +metric-based meta-learning approaches have shown promising results. Previous +studies mainly aim to derive a prototype representation for each class. +However, they neglect that it is challenging-yet-unnecessary to construct a +compact representation which expresses the entire meaning for each class. They +also ignore the importance to capture the inter-dependency between query and +the support set for few-shot text classification. To deal with these issues, we +propose a meta-learning based method MGIMN which performs instance-wise +comparison followed by aggregation to generate class-wise matching vectors +instead of prototype learning. The key of instance-wise comparison is the +interactive matching within the class-specific context and episode-specific +context. Extensive experiments demonstrate that the proposed method +significantly outperforms the existing state-of-the-art approaches, under both +the standard FSL and generalized FSL settings. +" +Zero and Few-shot Learning for Author Profiling,Mara Chinea-Rios,http://arxiv.org/pdf/2204.10543v2.pdf,2022-04-22,['cs.cl'],2204.10543v2.pdf," Author profiling classifies author characteristics by analyzing how language +is shared among people. In this work, we study that task from a low-resource +viewpoint: using little or no training data. We explore different zero and +few-shot models based on entailment and evaluate our systems on several +profiling tasks in Spanish and English. In addition, we study the effect of +both the entailment hypothesis and the size of the few-shot training sample. We +find that entailment-based models out-perform supervised text classifiers based +on roberta-XLM and that we can reach 80% of the accuracy of previous approaches +using less than 50\% of the training data on average. +" +Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks,Navid Rezaei,http://arxiv.org/pdf/2204.11922v1.pdf,2022-04-25,"['cs.cl', 'cs.ai']",2204.11922v1.pdf," Pre-trained language models have shown excellent results in few-shot learning +scenarios using in-context learning. Although it is impressive, the size of +language models can be prohibitive to make them usable in on-device +applications, such as sensors or smartphones. With smaller language models, +task-specific data annotation is needed to fine-tune the language model for a +specific purpose. However, data annotation can have a substantial financial and +time burden for small research groups, startups, and even companies. In this +paper, we analyze different prompt-based fine-tuning techniques to improve +results on both language and multimodal causal transformer models. To evaluate +our results, we use a dataset focusing on visual commonsense reasoning in time. +Our results show that by simple model-agnostic prompt-based fine-tuning, +comparable results can be reached by only using 35%-40% of the fine-tuning +training dataset. The proposed approaches result in significant time and +financial savings. As the proposed methods make minimal architectural +assumptions, other researchers can use the results in their transformer models +with minimal adaptations. We plan to release the source code freely to make it +easier for the community to use and contribute to our work. +" +Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models,Sanghwan Bae,http://arxiv.org/pdf/2205.00176v1.pdf,2022-04-30,['cs.cl'],2205.00176v1.pdf," Recent open-domain dialogue models have brought numerous breakthroughs. +However, building a chat system is not scalable since it often requires a +considerable volume of human-human dialogue data, especially when enforcing +features such as persona, style, or safety. In this work, we study the +challenge of imposing roles on open-domain dialogue systems, with the goal of +making the systems maintain consistent roles while conversing naturally with +humans. To accomplish this, the system must satisfy a role specification that +includes certain conditions on the stated features as well as a system policy +on whether or not certain types of utterances are allowed. For this, we propose +an efficient data collection framework leveraging in-context few-shot learning +of large-scale language models for building role-satisfying dialogue dataset +from scratch. We then compare various architectures for open-domain dialogue +systems in terms of meeting role specifications while maintaining +conversational abilities. Automatic and human evaluations show that our models +return few out-of-bounds utterances, keeping competitive performance on general +metrics. We release a Korean dialogue dataset we built for further research. +" +EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing,Chengyu Wang,http://arxiv.org/pdf/2205.00258v2.pdf,2022-04-30,['cs.cl'],2205.00258v2.pdf," The success of Pre-Trained Models (PTMs) has reshaped the development of +Natural Language Processing (NLP). Yet, it is not easy to obtain +high-performing models and deploy them online for industrial practitioners. To +bridge this gap, EasyNLP is designed to make it easy to build NLP applications, +which supports a comprehensive suite of NLP algorithms. It further features +knowledge-enhanced pre-training, knowledge distillation and few-shot learning +functionalities for large-scale PTMs, and provides a unified framework of model +training, inference and deployment for real-world applications. Currently, +EasyNLP has powered over ten business units within Alibaba Group and is +seamlessly integrated to the Platform of AI (PAI) products on Alibaba Cloud. +The source code of our EasyNLP toolkit is released at GitHub +(https://github.com/alibaba/EasyNLP). +" +POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection,Yujian Liu,http://arxiv.org/pdf/2205.00619v1.pdf,2022-05-02,['cs.cl'],2205.00619v1.pdf," Ideology is at the core of political science research. Yet, there still does +not exist general-purpose tools to characterize and predict ideology across +different genres of text. To this end, we study Pretrained Language Models +using novel ideology-driven pretraining objectives that rely on the comparison +of articles on the same story written by media of different ideologies. We +further collect a large-scale dataset, consisting of more than 3.6M political +news articles, for pretraining. Our model POLITICS outperforms strong baselines +and the previous state-of-the-art models on ideology prediction and stance +detection tasks. Further analyses show that POLITICS is especially good at +understanding long or formally written texts, and is also robust in few-shot +learning scenarios. +" +KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering,Jianing Wang,http://arxiv.org/pdf/2205.03071v1.pdf,2022-05-06,"['cs.cl', 'cs.ai']",2205.03071v1.pdf," Extractive Question Answering (EQA) is one of the most important tasks in +Machine Reading Comprehension (MRC), which can be solved by fine-tuning the +span selecting heads of Pre-trained Language Models (PLMs). However, most +existing approaches for MRC may perform poorly in the few-shot learning +scenario. To solve this issue, we propose a novel framework named Knowledge +Enhanced Contrastive Prompt-tuning (KECP). Instead of adding pointer heads to +PLMs, we introduce a seminal paradigm for EQA that transform the task into a +non-autoregressive Masked Language Modeling (MLM) generation problem. +Simultaneously, rich semantics from the external knowledge base (KB) and the +passage context are support for enhancing the representations of the query. In +addition, to boost the performance of PLMs, we jointly train the model by the +MLM and contrastive learning objectives. Experiments on multiple benchmarks +demonstrate that our method consistently outperforms state-of-the-art +approaches in few-shot settings by a large margin. +" +ProQA: Structural Prompt-based Pre-training for Unified Question Answering,Wanjun Zhong,http://arxiv.org/pdf/2205.04040v2.pdf,2022-05-09,['cs.cl'],2205.04040v2.pdf," Question Answering (QA) is a longstanding challenge in natural language +processing. Existing QA works mostly focus on specific question types, +knowledge domains, or reasoning skills. The specialty in QA research hinders +systems from modeling commonalities between tasks and generalization for wider +applications. To address this issue, we present ProQA, a unified QA paradigm +that solves various tasks through a single model. ProQA takes a unified +structural prompt as the bridge and improves the QA-centric ability by +structural prompt-based pre-training. Through a structurally designed +prompt-based input schema, ProQA concurrently models the knowledge +generalization for all QA tasks while keeping the knowledge customization for +every specific QA task. Furthermore, ProQA is pre-trained with structural +prompt-formatted large-scale synthesized corpus, which empowers the model with +the commonly-required QA ability. Experimental results on 11 QA benchmarks +demonstrate that ProQA consistently boosts performance on both full data +fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore, +ProQA exhibits strong ability in both continual learning and transfer learning +by taking the advantages of the structural prompt. +" +ALLSH: Active Learning Guided by Local Sensitivity and Hardness,Shujian Zhang,http://arxiv.org/pdf/2205.04980v2.pdf,2022-05-10,"['cs.cl', 'cs.ai', 'cs.lg']",2205.04980v2.pdf," Active learning, which effectively collects informative unlabeled data for +annotation, reduces the demand for labeled data. In this work, we propose to +retrieve unlabeled samples with a local sensitivity and hardness-aware +acquisition function. The proposed method generates data copies through local +perturbations and selects data points whose predictive likelihoods diverge the +most from their copies. We further empower our acquisition function by +injecting the select-worst case perturbation. Our method achieves consistent +gains over the commonly used active learning strategies in various +classification tasks. Furthermore, we observe consistent improvements over the +baselines on the study of prompt selection in prompt-based few-shot learning. +These experiments demonstrate that our acquisition guided by local sensitivity +and hardness can be effective and beneficial for many NLP tasks. +" +Prototypical Calibration for Few-shot Learning of Language Models,Zhixiong Han,http://arxiv.org/pdf/2205.10183v2.pdf,2022-05-20,['cs.cl'],2205.10183v2.pdf," In-context learning of GPT-like models has been recognized as fragile across +different hand-crafted templates, and demonstration permutations. In this work, +we propose prototypical calibration to adaptively learn a more robust decision +boundary for zero- and few-shot classification, instead of greedy decoding. +Concretely, our method first adopts Gaussian mixture distribution to estimate +the prototypical clusters for all categories. Then we assign each cluster to +the corresponding label by solving a weighted bipartite matching problem. Given +an example, its prediction is calibrated by the likelihood of prototypical +clusters. Experimental results show that prototypical calibration yields a +substantial improvement on a diverse set of tasks. Extensive analysis across +different scales also indicates that our method calibrates the decision +boundary as expected, greatly improving the robustness of GPT to templates, +permutations, and class imbalance. +" +BBTv2: Towards a Gradient-Free Future with Large Language Models,Tianxiang Sun,http://arxiv.org/pdf/2205.11200v2.pdf,2022-05-23,"['cs.cl', 'cs.ai']",2205.11200v2.pdf," Most downstream adaptation methods tune all or part of the parameters of +pre-trained models (PTMs) through gradient descent, where the tuning cost +increases linearly with the growth of the model size. By contrast, +gradient-free methods only require the forward computation of the PTM to tune +the prompt, retaining the benefits of efficient tuning and deployment. Though, +past work on gradient-free tuning often introduces gradient descent to seek a +good initialization of prompt and lacks versatility across tasks and PTMs. In +this paper, we present BBTv2, an improved version of Black-Box Tuning, to drive +PTMs for few-shot learning. We prepend continuous prompts to every layer of the +PTM and propose a divide-and-conquer gradient-free algorithm to optimize the +prompts at different layers alternately. Extensive experiments across various +tasks and PTMs show that BBTv2 can achieve comparable performance to full model +tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA, +BitFit, etc.) under few-shot settings while maintaining much fewer tunable +parameters. +" +Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer,Fu-Ming Guo,http://arxiv.org/pdf/2205.15290v2.pdf,2022-05-30,"['cs.cv', 'cs.ai', 'cs.lg']",2205.15290v2.pdf," Lung cancer is the leading cause of cancer-related death worldwide. Lung +adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most +common histologic subtypes of non-small-cell lung cancer (NSCLC). Histology is +an essential tool for lung cancer diagnosis. Pathologists make classifications +according to the dominant subtypes. Although morphology remains the standard +for diagnosis, significant tool needs to be developed to elucidate the +diagnosis. In our study, we utilize the pre-trained Vision Transformer (ViT) +model to classify multiple label lung cancer on histologic slices (from dataset +LC25000), in both Zero-Shot and Few-Shot settings. Then we compare the +performance of Zero-Shot and Few-Shot ViT on accuracy, precision, recall, +sensitivity and specificity. Our study show that the pre-trained ViT model has +a good performance in Zero-Shot setting, a competitive accuracy ($99.87\%$) in +Few-Shot setting ({epoch = 1}) and an optimal result ($100.00\%$ on both +validation set and test set) in Few-Shot seeting ({epoch = 5}). +" +Neural Prompt Search,Yuanhan Zhang,http://arxiv.org/pdf/2206.04673v2.pdf,2022-06-09,"['cs.cv', 'cs.ai', 'cs.lg']",2206.04673v2.pdf," The size of vision models has grown exponentially over the last few years, +especially after the emergence of Vision Transformer. This has motivated the +development of parameter-efficient tuning methods, such as learning adapter +layers or visual prompt tokens, which allow a tiny portion of model parameters +to be trained whereas the vast majority obtained from pre-training are frozen. +However, designing a proper tuning method is non-trivial: one might need to try +out a lengthy list of design choices, not to mention that each downstream +dataset often requires custom designs. In this paper, we view the existing +parameter-efficient tuning methods as ""prompt modules"" and propose Neural +prOmpt seArcH (NOAH), a novel approach that learns, for large vision models, +the optimal design of prompt modules through a neural architecture search +algorithm, specifically for each downstream dataset. By conducting extensive +experiments on over 20 vision datasets, we demonstrate that NOAH (i) is +superior to individual prompt modules, (ii) has a good few-shot learning +ability, and (iii) is domain-generalizable. The code and models are available +at https://github.com/Davidzhangyuanhan/NOAH. +" +Low Resource Pipeline for Spoken Language Understanding via Weak Supervision,Ayush Kumar,http://arxiv.org/pdf/2206.10559v1.pdf,2022-06-21,['cs.cl'],2206.10559v1.pdf," In Weak Supervised Learning (WSL), a model is trained over noisy labels +obtained from semantic rules and task-specific pre-trained models. Rules offer +limited generalization over tasks and require significant manual efforts while +pre-trained models are available only for limited tasks. In this work, we +propose to utilize prompt-based methods as weak sources to obtain the noisy +labels on unannotated data. We show that task-agnostic prompts are +generalizable and can be used to obtain noisy labels for different Spoken +Language Understanding (SLU) tasks such as sentiment classification, disfluency +detection and emotion classification. These prompts could additionally be +updated to add task-specific contexts, thus providing flexibility to design +task-specific prompts. We demonstrate that prompt-based methods generate +reliable labels for the above SLU tasks and thus can be used as a universal +weak source to train a weak-supervised model (WSM) in absence of labeled data. +Our proposed WSL pipeline trained over prompt-based weak source outperforms +other competitive low-resource benchmarks on zero and few-shot learning by more +than 4% on Macro-F1 on all of the three benchmark SLU datasets. The proposed +method also outperforms a conventional rule based WSL pipeline by more than 5% +on Macro-F1. +" +Prompting Decision Transformer for Few-Shot Policy Generalization,Mengdi Xu,http://arxiv.org/pdf/2206.13499v1.pdf,2022-06-27,"['cs.lg', 'cs.ai', 'cs.cv', 'cs.ro']",2206.13499v1.pdf," Humans can leverage prior experience and learn novel tasks from a handful of +demonstrations. In contrast to offline meta-reinforcement learning, which aims +to achieve quick adaptation through better algorithm design, we investigate the +effect of architecture inductive bias on the few-shot learning capability. We +propose a Prompt-based Decision Transformer (Prompt-DT), which leverages the +sequential modeling ability of the Transformer architecture and the prompt +framework to achieve few-shot adaptation in offline RL. We design the +trajectory prompt, which contains segments of the few-shot demonstrations, and +encodes task-specific information to guide policy generation. Our experiments +in five MuJoCo control benchmarks show that Prompt-DT is a strong few-shot +learner without any extra finetuning on unseen target tasks. Prompt-DT +outperforms its variants and strong meta offline RL baselines by a large margin +with a trajectory prompt containing only a few timesteps. Prompt-DT is also +robust to prompt length changes and can generalize to out-of-distribution (OOD) +environments. +" +Few-shot training LLMs for project-specific code-summarization,Toufique Ahmed,http://arxiv.org/pdf/2207.04237v2.pdf,2022-07-09,"['cs.se', 'cs.lg']",2207.04237v2.pdf," Very large language models (LLMs), such as GPT-3 and Codex have achieved +state-of-the-art performance on several natural-language tasks, and show great +promise also for code. A particularly exciting aspect of LLMs is their knack +for few-shot and zero-shot learning: they can learn to perform a task with very +few examples. Few-shotting has particular synergies in software engineering, +where there are a lot of phenomena (identifier names, APIs, terminology, coding +patterns) that are known to be highly project-specific. However, +project-specific data can be quite limited, especially early in the history of +a project; thus the few-shot learning capacity of LLMs might be very relevant. +In this paper, we investigate the use few-shot training with the very large GPT +(Generative Pre-trained Transformer) Codex model, and find evidence suggesting +that one can significantly surpass state-of-the-art models for +code-summarization, leveraging project-specific training. +" +Convolutional Bypasses Are Better Vision Transformer Adapters,Shibo Jie,http://arxiv.org/pdf/2207.07039v3.pdf,2022-07-14,['cs.cv'],2207.07039v3.pdf," The pretrain-then-finetune paradigm has been widely adopted in computer +vision. But as the size of Vision Transformer (ViT) grows exponentially, the +full finetuning becomes prohibitive in view of the heavier storage overhead. +Motivated by parameter-efficient transfer learning (PETL) on language +transformers, recent studies attempt to insert lightweight adaptation modules +(e.g., adapter layers or prompt tokens) to pretrained ViT and only finetune +these modules while the pretrained weights are frozen. However, these modules +were originally proposed to finetune language models and did not take into +account the prior knowledge specifically for visual tasks. In this paper, we +propose to construct Convolutional Bypasses (Convpass) in ViT as adaptation +modules, introducing only a small amount (less than 0.5% of model parameters) +of trainable parameters to adapt the large ViT. Different from other PETL +methods, Convpass benefits from the hard-coded inductive bias of convolutional +layers and thus is more suitable for visual tasks, especially in the low-data +regime. Experimental results on VTAB-1K benchmark and few-shot learning +datasets show that Convpass outperforms current language-oriented adaptation +modules, demonstrating the necessity to tailor vision-oriented adaptation +modules for adapting vision models. +" +STT: Soft Template Tuning for Few-Shot Adaptation,Ping Yu,http://arxiv.org/pdf/2207.08408v1.pdf,2022-07-18,"['cs.cl', 'cs.ai']",2207.08408v1.pdf," Prompt tuning has been an extremely effective tool to adapt a pre-trained +model to downstream tasks. However, standard prompt-based methods mainly +consider the case of sufficient data of downstream tasks. It is still unclear +whether the advantage can be transferred to the few-shot regime, where only +limited data are available for each downstream task. Although some works have +demonstrated the potential of prompt-tuning under the few-shot setting, the +main stream methods via searching discrete prompts or tuning soft prompts with +limited data are still very challenging. Through extensive empirical studies, +we find that there is still a gap between prompt tuning and fully fine-tuning +for few-shot learning. To bridge the gap, we propose a new prompt-tuning +framework, called Soft Template Tuning (STT). STT combines manual and auto +prompts, and treats downstream classification tasks as a masked language +modeling task. Comprehensive evaluation on different settings suggests STT can +close the gap between fine-tuning and prompt-based methods without introducing +additional parameters. Significantly, it can even outperform the time- and +resource-consuming fine-tuning method on sentiment classification tasks. +" +Self-Supervision Can Be a Good Few-Shot Learner,Yuning Lu,http://arxiv.org/pdf/2207.09176v1.pdf,2022-07-19,['cs.cv'],2207.09176v1.pdf," Existing few-shot learning (FSL) methods rely on training with a large +labeled dataset, which prevents them from leveraging abundant unlabeled data. +From an information-theoretic perspective, we propose an effective unsupervised +FSL method, learning representations with self-supervision. Following the +InfoMax principle, our method learns comprehensive representations by capturing +the intrinsic structure of the data. Specifically, we maximize the mutual +information (MI) of instances and their representations with a low-bias MI +estimator to perform self-supervised pre-training. Rather than supervised +pre-training focusing on the discriminable features of the seen classes, our +self-supervised model has less bias toward the seen classes, resulting in +better generalization for unseen classes. We explain that supervised +pre-training and self-supervised pre-training are actually maximizing different +MI objectives. Extensive experiments are further conducted to analyze their FSL +performance with various training settings. Surprisingly, the results show that +self-supervised pre-training can outperform supervised pre-training under the +appropriate conditions. Compared with state-of-the-art FSL methods, our +approach achieves comparable performance on widely used FSL benchmarks without +any labels of the base classes. +" +Language Model Cascades,David Dohan,http://arxiv.org/pdf/2207.10342v2.pdf,2022-07-21,"['cs.cl', 'cs.ai']",2207.10342v2.pdf," Prompted models have demonstrated impressive few-shot learning abilities. +Repeated interactions at test-time with a single model, or the composition of +multiple models together, further expands capabilities. These compositions are +probabilistic models, and may be expressed in the language of graphical models +with random variables whose values are complex data types such as strings. +Cases with control flow and dynamic structure require techniques from +probabilistic programming, which allow implementing disparate model structures +and inference strategies in a unified language. We formalize several existing +techniques from this perspective, including scratchpads / chain of thought, +verifiers, STaR, selection-inference, and tool use. We refer to the resulting +programs as language model cascades. +" +Few-shot Adaptation Works with UnpredicTable Data,Jun Shern Chan,http://arxiv.org/pdf/2208.01009v2.pdf,2022-08-01,"['cs.cl', 'cs.ai', 'cs.lg']",2208.01009v2.pdf," Prior work on language models (LMs) shows that training on a large number of +diverse tasks improves few-shot learning (FSL) performance on new tasks. We +take this to the extreme, automatically extracting 413,299 tasks from internet +tables - orders of magnitude more than the next-largest public datasets. +Finetuning on the resulting dataset leads to improved FSL performance on +Natural Language Processing (NLP) tasks, but not proportionally to dataset +scale. In fact, we find that narrow subsets of our dataset sometimes outperform +more diverse datasets. For example, finetuning on software documentation from +support.google.com raises FSL performance by a mean of +7.5% on 52 downstream +tasks, which beats training on 40 human-curated NLP datasets (+6.7%). +Finetuning on various narrow datasets leads to similar broad improvements +across test tasks, suggesting that the gains are not from domain adaptation but +adapting to FSL in general. We do not observe clear patterns between the +datasets that lead to FSL gains, leaving open questions about why certain data +helps with FSL. +" +Robotic Interestingness via Human-Informed Few-Shot Object Detection,Seungchan Kim,http://arxiv.org/pdf/2208.01084v1.pdf,2022-08-01,['cs.ro'],2208.01084v1.pdf," Interestingness recognition is crucial for decision making in autonomous +exploration for mobile robots. Previous methods proposed an unsupervised online +learning approach that can adapt to environments and detect interesting scenes +quickly, but lack the ability to adapt to human-informed interesting objects. +To solve this problem, we introduce a human-interactive framework, +AirInteraction, that can detect human-informed objects via few-shot online +learning. To reduce the communication bandwidth, we first apply an online +unsupervised learning algorithm on the unmanned vehicle for interestingness +recognition and then only send the potential interesting scenes to a +base-station for human inspection. The human operator is able to draw and +provide bounding box annotations for particular interesting objects, which are +sent back to the robot to detect similar objects via few-shot learning. Only +using few human-labeled examples, the robot can learn novel interesting object +categories during the mission and detect interesting scenes that contain the +objects. We evaluate our method on various interesting scene recognition +datasets. To the best of our knowledge, it is the first human-informed few-shot +object detection framework for autonomous exploration. +" +Atlas: Few-shot Learning with Retrieval Augmented Language Models,Gautier Izacard,http://arxiv.org/pdf/2208.03299v3.pdf,2022-08-05,['cs.cl'],2208.03299v3.pdf," Large language models have shown impressive few-shot results on a wide range +of tasks. However, when knowledge is key for such results, as is the case for +tasks such as question answering and fact checking, massive parameter counts to +store knowledge seem to be needed. Retrieval augmented models are known to +excel at knowledge intensive tasks without the need for as many parameters, but +it is unclear whether they work in few-shot settings. In this work we present +Atlas, a carefully designed and pre-trained retrieval augmented language model +able to learn knowledge intensive tasks with very few training examples. We +perform evaluations on a wide range of tasks, including MMLU, KILT and +NaturalQuestions, and study the impact of the content of the document index, +showing that it can easily be updated. Notably, Atlas reaches over 42% accuracy +on Natural Questions using only 64 examples, outperforming a 540B parameters +model by 3% despite having 50x fewer parameters. +" +Limits of an AI program for solving college math problems,Ernest Davis,http://arxiv.org/pdf/2208.06906v1.pdf,2022-08-14,['cs.ai'],2208.06906v1.pdf," Drori et al. (2022) report that ""A neural network solves, explains, and +generates university math problems by program synthesis and few-shot learning +at human level ... [It] automatically answers 81\% of university-level +mathematics problems."" The system they describe is indeed impressive; however, +the above description is very much overstated. The work of solving the problems +is done, not by a neural network, but by the symbolic algebra package Sympy. +Problems of various formats are excluded from consideration. The so-called +""explanations"" are just rewordings of lines of code. Answers are marked as +correct that are not in the form specified in the problem. Most seriously, it +seems that in many cases the system uses the correct answer given in the test +corpus to guide its path to solving the problem. +" +Efficient Few-Shot Learning Without Prompts,Lewis Tunstall,http://arxiv.org/pdf/2209.11055v1.pdf,2022-09-22,['cs.cl'],2209.11055v1.pdf," Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and +pattern exploiting training (PET), have achieved impressive results in +label-scarce settings. However, they are difficult to employ since they are +subject to high variability from manually crafted prompts, and typically +require billion-parameter language models to achieve high accuracy. To address +these shortcomings, we propose SetFit (Sentence Transformer Fine-tuning), an +efficient and prompt-free framework for few-shot fine-tuning of Sentence +Transformers (ST). SetFit works by first fine-tuning a pretrained ST on a small +number of text pairs, in a contrastive Siamese manner. The resulting model is +then used to generate rich text embeddings, which are used to train a +classification head. This simple framework requires no prompts or verbalizers, +and achieves high accuracy with orders of magnitude less parameters than +existing techniques. Our experiments show that SetFit obtains comparable +results with PEFT and PET techniques, while being an order of magnitude faster +to train. We also show that SetFit can be applied in multilingual settings by +simply switching the ST body. Our code is available at +https://github.com/huggingface/setfit and our datasets at +https://huggingface.co/setfit . +" +CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation,Tanay Dixit,http://arxiv.org/pdf/2210.04873v2.pdf,2022-10-10,['cs.cl'],2210.04873v2.pdf," Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed +inputs during training -- helps reduce model reliance on spurious correlations +and improves generalization to out-of-distribution (OOD) data. Prior work on +generating counterfactuals only considered restricted classes of perturbations, +limiting their effectiveness. We present COunterfactual Generation via +Retrieval and Editing (CORE), a retrieval-augmented generation framework for +creating diverse counterfactual perturbations for CDA. For each training +example, CORE first performs a dense retrieval over a task-related unlabeled +text corpus using a learned bi-encoder and extracts relevant counterfactual +excerpts. CORE then incorporates these into prompts to a large language model +with few-shot learning capabilities, for counterfactual editing. Conditioning +language model edits on naturally occurring data results in diverse +perturbations. Experiments on natural language inference and sentiment analysis +benchmarks show that CORE counterfactuals are more effective at improving +generalization to OOD data compared to other DA approaches. We also show that +the CORE retrieval framework can be used to encourage diversity in manually +authored perturbations +" +Continual Training of Language Models for Few-Shot Learning,Zixuan Ke,http://arxiv.org/pdf/2210.05549v1.pdf,2022-10-11,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.ne']",2210.05549v1.pdf," Recent work on applying large language models (LMs) achieves impressive +performance in many NLP applications. Adapting or posttraining an LM using an +unlabeled domain corpus can produce even better performance for end-tasks in +the domain. This paper proposes the problem of continually extending an LM by +incrementally post-train the LM with a sequence of unlabeled domain corpora to +expand its knowledge without forgetting its previous skills. The goal is to +improve the few-shot end-task learning in these domains. The resulting system +is called CPT (Continual PostTraining), which to our knowledge, is the first +continual post-training system. Experimental results verify its effectiveness. +" +Knowledge-grounded Dialog State Tracking,Dian Yu,http://arxiv.org/pdf/2210.06656v1.pdf,2022-10-13,['cs.cl'],2210.06656v1.pdf," Knowledge (including structured knowledge such as schema and ontology, and +unstructured knowledge such as web corpus) is a critical part of dialog +understanding, especially for unseen tasks and domains. Traditionally, such +domain-specific knowledge is encoded implicitly into model parameters for the +execution of downstream tasks, which makes training inefficient. In addition, +such models are not easily transferable to new tasks with different schemas. In +this work, we propose to perform dialog state tracking grounded on knowledge +encoded externally. We query relevant knowledge of various forms based on the +dialog context where such information can ground the prediction of dialog +states. We demonstrate superior performance of our proposed method over strong +baselines, especially in the few-shot learning setting. +" +Unified Vision and Language Prompt Learning,Yuhang Zang,http://arxiv.org/pdf/2210.07225v1.pdf,2022-10-13,"['cs.cv', 'cs.ai']",2210.07225v1.pdf," Prompt tuning, a parameter- and data-efficient transfer learning paradigm +that tunes only a small number of parameters in a model's input space, has +become a trend in the vision community since the emergence of large +vision-language models like CLIP. We present a systematic study on two +representative prompt tuning methods, namely text prompt tuning and visual +prompt tuning. A major finding is that none of the unimodal prompt tuning +methods performs consistently well: text prompt tuning fails on data with high +intra-class visual variances while visual prompt tuning cannot handle low +inter-class variances. To combine the best from both worlds, we propose a +simple approach called Unified Prompt Tuning (UPT), which essentially learns a +tiny neural network to jointly optimize prompts across different modalities. +Extensive experiments on over 11 vision datasets show that UPT achieves a +better trade-off than the unimodal counterparts on few-shot learning +benchmarks, as well as on domain generalization benchmarks. Code and models +will be released to facilitate future research. +" +"Vision-Language Pre-training: Basics, Recent Advances, and Future Trends",Zhe Gan,http://arxiv.org/pdf/2210.09263v1.pdf,2022-10-17,"['cs.cv', 'cs.cl']",2210.09263v1.pdf," This paper surveys vision-language pre-training (VLP) methods for multimodal +intelligence that have been developed in the last few years. We group these +approaches into three categories: ($i$) VLP for image-text tasks, such as image +captioning, image-text retrieval, visual question answering, and visual +grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image +classification, object detection, and segmentation; and ($iii$) VLP for +video-text tasks, such as video captioning, video-text retrieval, and video +question answering. For each category, we present a comprehensive review of +state-of-the-art methods, and discuss the progress that has been made and +challenges still being faced, using specific systems and models as case +studies. In addition, for each category, we discuss advanced topics being +actively explored in the research community, such as big foundation models, +unified modeling, in-context few-shot learning, knowledge, robustness, and +computer vision in the wild, to name a few. +" +Better Few-Shot Relation Extraction with Label Prompt Dropout,Peiyuan Zhang,http://arxiv.org/pdf/2210.13733v1.pdf,2022-10-25,['cs.cl'],2210.13733v1.pdf," Few-shot relation extraction aims to learn to identify the relation between +two entities based on very limited training examples. Recent efforts found that +textual labels (i.e., relation names and relation descriptions) could be +extremely useful for learning class representations, which will benefit the +few-shot learning task. However, what is the best way to leverage such label +information in the learning process is an important research question. Existing +works largely assume such textual labels are always present during both +learning and prediction. In this work, we argue that such approaches may not +always lead to optimal results. Instead, we present a novel approach called +label prompt dropout, which randomly removes label descriptions in the learning +process. Our experiments show that our approach is able to lead to improved +class representations, yielding significantly better results on the few-shot +relation extraction task. +" +STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification,Jinta Weng,http://arxiv.org/pdf/2210.16489v1.pdf,2022-10-29,"['cs.cl', 'cs.ai']",2210.16489v1.pdf," The effectiveness of prompt learning has been demonstrated in different +pre-trained language models. By formulating suitable template and choosing +representative label mapping, prompt learning can be used as an efficient +knowledge probe. However, finding suitable prompt in existing methods requires +multiple experimental attempts or appropriate vector initialization on +formulating suitable template and choosing representative label mapping, which +it is more common in few-shot learning tasks. Motivating by PLM working +process, we try to construct the prompt from task semantic perspective and thus +propose the STPrompt -Semantic-guided and Task-driven Prompt model. +Specifically, two novel prompts generated from the semantic dependency tree +(Dep-prompt) and task-specific metadata description (Meta-prompt), are firstly +constructed in a prompt augmented pool, and the proposed model would +automatically select a suitable semantic prompt to motivating the prompt +learning process. Our results show that the proposed model achieves the +state-of-the-art performance in five different datasets of few-shot text +classification tasks, which prove that more semantic and significant prompts +could assume as a better knowledge proving tool. +" +ConsPrompt: Easily Exploiting Contrastive Samples for Few-shot Prompt Learning,Jinta Weng,http://arxiv.org/pdf/2211.04118v1.pdf,2022-11-08,"['cs.cl', 'cs.ai']",2211.04118v1.pdf," Prompt learning recently become an effective linguistic tool to motivate the +PLMs' knowledge on few-shot-setting tasks. However, studies have shown the lack +of robustness still exists in prompt learning, since suitable initialization of +continuous prompt and expert-first manual prompt are essential in fine-tuning +process. What is more, human also utilize their comparative ability to motivate +their existing knowledge for distinguishing different examples. Motivated by +this, we explore how to use contrastive samples to strengthen prompt learning. +In detail, we first propose our model ConsPrompt combining with prompt encoding +network, contrastive sampling module, and contrastive scoring module. +Subsequently, two sampling strategies, similarity-based and label-based +strategies, are introduced to realize differential contrastive learning. The +effectiveness of proposed ConsPrompt is demonstrated in five different few-shot +learning tasks and shown the similarity-based sampling strategy is more +effective than label-based in combining contrastive learning. Our results also +exhibits the state-of-the-art performance and robustness in different few-shot +settings, which proves that the ConsPrompt could be assumed as a better +knowledge probe to motivate PLMs. +" +Retrieval-Augmented Generative Question Answering for Event Argument Extraction,Xinya Du,http://arxiv.org/pdf/2211.07067v1.pdf,2022-11-14,['cs.cl'],2211.07067v1.pdf," Event argument extraction has long been studied as a sequential prediction +problem with extractive-based methods, tackling each argument in isolation. +Although recent work proposes generation-based methods to capture +cross-argument dependency, they require generating and post-processing a +complicated target sequence (template). Motivated by these observations and +recent pretrained language models' capabilities of learning from +demonstrations. We propose a retrieval-augmented generative QA model (R-GQA) +for event argument extraction. It retrieves the most similar QA pair and +augments it as prompt to the current example's context, then decodes the +arguments as answers. Our approach outperforms substantially prior methods +across various settings (i.e. fully supervised, domain transfer, and fewshot +learning). Finally, we propose a clustering-based sampling strategy (JointEnc) +and conduct a thorough analysis of how different strategies influence the +few-shot learning performance. The implementations are available at https:// +github.com/xinyadu/RGQA +" +ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation,Yining Lu,http://arxiv.org/pdf/2211.09855v1.pdf,2022-11-17,['cs.cl'],2211.09855v1.pdf," Subjective answer evaluation is a time-consuming and tedious task, and the +quality of the evaluation is heavily influenced by a variety of subjective +personal characteristics. Instead, machine evaluation can effectively assist +educators in saving time while also ensuring that evaluations are fair and +realistic. However, most existing methods using regular machine learning and +natural language processing techniques are generally hampered by a lack of +annotated answers and poor model interpretability, making them unsuitable for +real-world use. To solve these challenges, we propose ProtSi Network, a unique +semi-supervised architecture that for the first time uses few-shot learning to +subjective answer evaluation. To evaluate students' answers by similarity +prototypes, ProtSi Network simulates the natural process of evaluator scoring +answers by combining Siamese Network which consists of BERT and encoder layers +with Prototypical Network. We employed an unsupervised diverse paraphrasing +model ProtAugment, in order to prevent overfitting for effective few-shot text +classification. By integrating contrastive learning, the discriminative text +issue can be mitigated. Experiments on the Kaggle Short Scoring Dataset +demonstrate that the ProtSi Network outperforms the most recent baseline models +in terms of accuracy and quadratic weighted kappa. +" +TEMPERA: Test-Time Prompting via Reinforcement Learning,Tianjun Zhang,http://arxiv.org/pdf/2211.11890v1.pdf,2022-11-21,"['cs.cl', 'cs.ai']",2211.11890v1.pdf," Careful prompt design is critical to the use of large language models in +zero-shot or few-shot learning. As a consequence, there is a growing interest +in automated methods to design optimal prompts. In this work, we propose +Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to +prior prompt generation methods, TEMPERA can efficiently leverage prior +knowledge, is adaptive to different queries and provides an interpretable +prompt for every query. To achieve this, we design a novel action space that +allows flexible editing of the initial prompts covering a wide set of +commonly-used components like instructions, few-shot exemplars, and +verbalizers. The proposed method achieves significant gains compared with +recent SoTA approaches like prompt tuning, AutoPrompt, and RLPrompt, across a +variety of tasks including sentiment analysis, topic classification, natural +language inference, and reading comprehension. Our method achieves 5.33x on +average improvement in sample efficiency when compared to the traditional +fine-tuning methods. +" +Towards Practical Few-shot Federated NLP,Dongqi Cai,http://arxiv.org/pdf/2212.00192v2.pdf,2022-12-01,"['cs.cl', 'cs.lg']",2212.00192v2.pdf," Transformer-based pre-trained models have emerged as the predominant solution +for natural language processing (NLP). Fine-tuning such pre-trained models for +downstream tasks often requires a considerable amount of labeled private data. +In practice, private data is often distributed across heterogeneous mobile +devices and may be prohibited from being uploaded. Moreover, well-curated +labeled data is often scarce, presenting an additional challenge. To address +these challenges, we first introduce a data generator for federated few-shot +learning tasks, which encompasses the quantity and skewness of scarce labeled +data in a realistic setting. Subsequently, we propose AUG-FedPrompt, a +prompt-based federated learning system that exploits abundant unlabeled data +for data augmentation. Our experiments indicate that AUG-FedPrompt can perform +on par with full-set fine-tuning with a limited amount of labeled data. +However, such competitive performance comes at a significant system cost. +" +Few-Shot Nested Named Entity Recognition,Hong Ming,http://arxiv.org/pdf/2212.00953v1.pdf,2022-12-02,"['cs.cl', 'cs.ai']",2212.00953v1.pdf," While Named Entity Recognition (NER) is a widely studied task, making +inferences of entities with only a few labeled data has been challenging, +especially for entities with nested structures. Unlike flat entities, entities +and their nested entities are more likely to have similar semantic feature +representations, drastically increasing difficulties in classifying different +entity categories in the few-shot setting. Although prior work has briefly +discussed nested structures in the context of few-shot learning, to our best +knowledge, this paper is the first one specifically dedicated to studying the +few-shot nested NER task. Leveraging contextual dependency to distinguish +nested entities, we propose a Biaffine-based Contrastive Learning (BCL) +framework. We first design a Biaffine span representation module for learning +the contextual span dependency representation for each entity span rather than +only learning its semantic representation. We then merge these two +representations by the residual connection to distinguish nested entities. +Finally, we build a contrastive learning framework to adjust the representation +distribution for larger margin boundaries and more generalized domain transfer +learning ability. We conducted experimental studies on three English, German, +and Russian nested NER datasets. The results show that the BCL outperformed +three baseline models on the 1-shot and 5-shot tasks in terms of F1 score. +" +Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration,Feng Nie,http://arxiv.org/pdf/2212.02216v1.pdf,2022-12-05,['cs.cl'],2212.02216v1.pdf," Pre-trained language models (PLMs) have exhibited remarkable few-shot +learning capabilities when provided a few examples in a natural language prompt +as demonstrations of test instances, i.e., in-context learning. However, the +performance of in-context learning is susceptible to the choice of prompt +format, training examples and the ordering of the training examples. In this +paper, we propose a novel nearest-neighbor calibration framework for in-context +learning to ease this issue. It is inspired by a phenomenon that the in-context +learning paradigm produces incorrect labels when inferring training instances, +which provides a useful supervised signal to calibrate predictions. Thus, our +method directly augments the predictions with a $k$-nearest-neighbor ($k$NN) +classifier over a datastore of cached few-shot instance representations +obtained by PLMs and their corresponding labels. Then adaptive neighbor +selection and feature regularization modules are introduced to make full use of +a few support instances to reduce the $k$NN retrieval noise. Experiments on +various few-shot text classification tasks demonstrate that our method +significantly improves in-context learning, while even achieving comparable +performance with state-of-the-art tuning-based approaches in some sentiment +analysis tasks. +" +JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset,Ruth-Ann Armstrong,http://arxiv.org/pdf/2212.03419v1.pdf,2022-12-07,"['cs.cl', 'cs.lg', 'i.2.7']",2212.03419v1.pdf," JamPatoisNLI provides the first dataset for natural language inference in a +creole language, Jamaican Patois. Many of the most-spoken low-resource +languages are creoles. These languages commonly have a lexicon derived from a +major world language and a distinctive grammar reflecting the languages of the +original speakers and the process of language birth by creolization. This gives +them a distinctive place in exploring the effectiveness of transfer from large +monolingual or multilingual pretrained models. While our work, along with +previous work, shows that transfer from these models to low-resource languages +that are unrelated to languages in their training set is not very effective, we +would expect stronger results from transfer to creoles. Indeed, our experiments +show considerably better results from few-shot learning of JamPatoisNLI than +for such unrelated languages, and help us begin to understand how the unique +relationship between creoles and their high-resource base languages affect +cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring +premises and expert-written hypotheses, is a step towards steering research +into a traditionally underserved language and a useful benchmark for +understanding cross-lingual NLP. +" +Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning,Yukun Cao,http://arxiv.org/pdf/2212.03423v4.pdf,2022-12-07,"['cs.db', 'cs.ai']",2212.03423v4.pdf," Interactive data exploration (IDE) is an effective way of comprehending big +data, whose volume and complexity are beyond human abilities. The main goal of +IDE is to discover user interest regions from a database through multi-rounds +of user labelling. Existing IDEs adopt active-learning framework, where users +iteratively discriminate or label the interestingness of selected tuples. The +process of data exploration can be viewed as the process of training a +classifier, which determines whether a database tuple is interesting to a user. +An efficient exploration thus takes very few iterations of user labelling to +reach the data region of interest. In this work, we consider the data +exploration as the process of few-shot learning, where the classifier is +learned with only a few training examples, or exploration iterations. To this +end, we propose a learning-to-explore framework, based on meta-learning, which +learns how to learn a classifier with automatically generated meta-tasks, so +that the exploration process can be much shortened. Extensive experiments on +real datasets show that our proposal outperforms existing explore-by-example +solutions in terms of accuracy and efficiency. +" +Demystifying Prompts in Language Models via Perplexity Estimation,Hila Gonen,http://arxiv.org/pdf/2212.04037v1.pdf,2022-12-08,['cs.cl'],2212.04037v1.pdf," Language models can be prompted to perform a wide variety of zero- and +few-shot learning problems. However, performance varies significantly with the +choice of prompt, and we do not yet understand why this happens or how to pick +the best prompts. In this work, we analyze the factors that contribute to this +variance and establish a new empirical hypothesis: the performance of a prompt +is coupled with the extent to which the model is familiar with the language it +contains. Over a wide range of tasks, we show that the lower the perplexity of +the prompt is, the better the prompt is able to perform the task. As a result, +we devise a method for creating prompts: (1) automatically extend a small seed +set of manually written prompts by paraphrasing using GPT3 and backtranslation +and (2) choose the lowest perplexity prompts to get significant gains in +performance. +" +Technical Report -- Competition Solution for Prompt Tuning using Pretrained Language Model,Jiang-Long Song,http://arxiv.org/pdf/2212.06369v3.pdf,2022-12-13,['cs.cl'],2212.06369v3.pdf," Prompt tuning recently becomes a hot-spot in the applications of large +pretrained language models on specific downstream tasks. Regarding the Language +Model as a Service (LMaaS), black-box tuning using derivative-free optimization +(DFO) provides a novel approach to expand the practical scenarios of pretrained +models and enrich the researches of few-shot learning. In this report, we +present our solution in this competition that is based on the LMaaS scenario. +Our solution consists of several modifications to BBTv2, including multiple +label words, selection of P0, rolling update strategy, multi-task loss from MLP +classifier, and finally using the ensemble method to further improve +generalization ability. We also shared some strategies that we tried but didn't +use in the final submission for further discussion. In the end we raised a +question about the SNLI dataset and the impact on the results, as well as our +concerns about the competition. +" +Localized Latent Updates for Fine-Tuning Vision-Language Models,Moritz Ibing,http://arxiv.org/pdf/2212.06556v1.pdf,2022-12-13,"['cs.cv', 'cs.cl', 'cs.lg']",2212.06556v1.pdf," Although massive pre-trained vision-language models like CLIP show impressive +generalization capabilities for many tasks, still it often remains necessary to +fine-tune them for improved performance on specific datasets. When doing so, it +is desirable that updating the model is fast and that the model does not lose +its capabilities on data outside of the dataset, as is often the case with +classical fine-tuning approaches. In this work we suggest a lightweight +adapter, that only updates the models predictions close to seen datapoints. We +demonstrate the effectiveness and speed of this relatively simple approach in +the context of few-shot learning, where our results both on classes seen and +unseen during training are comparable with or improve on the state of the art. +" +ALERT: Adapting Language Models to Reasoning Tasks,Ping Yu,http://arxiv.org/pdf/2212.08286v2.pdf,2022-12-16,['cs.cl'],2212.08286v2.pdf," Current large language models can perform reasonably well on complex tasks +that require step-by-step reasoning with few-shot learning. Are these models +applying reasoning skills they have learnt during pre-training and reason +outside of their training context, or are they simply memorizing their training +corpus at finer granularity and have learnt to better understand their context? +To tease apart these possibilities, we introduce ALERT, a benchmark and suite +of analyses for assessing language models' reasoning ability comparing +pre-trained and finetuned models on complex tasks that require reasoning skills +to solve. ALERT provides a test bed to asses any language model on fine-grained +reasoning skills, which spans over 20 datasets and covers 10 different +reasoning skills. We leverage ALERT to further investigate the role of +finetuning. With extensive empirical analysis we find that language models +learn more reasoning skills such as textual entailment, abductive reasoning, +and analogical reasoning during finetuning stage compared to pretraining state. +We also find that when language models are finetuned they tend to overfit to +the prompt template, which hurts the robustness of models causing +generalization problems. +" +Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition,Jinhua Liang,http://arxiv.org/pdf/2212.08952v1.pdf,2022-12-17,"['cs.sd', 'eess.as']",2212.08952v1.pdf," Everyday sound recognition aims to infer types of sound events in audio +streams. While many works succeeded in training models with high performance in +a fully-supervised manner, they are still restricted to the demand of large +quantities of labelled data and the range of predefined classes. To overcome +these drawbacks, this work firstly curates a new database named FSD-FS for +multi-label few-shot audio classification. It then explores how to incorporate +audio taxonomy in few-shot learning. Specifically, this work proposes +label-dependent prototypical networks (LaD-protonet) to exploit parent-children +relationships between labels. Plus, it applies taxonomy-aware label smoothing +techniques to boost model performance. Experiments demonstrate that +LaD-protonet outperforms original prototypical networks as well as other +state-of-the-art methods. Moreover, its performance can be further boosted when +combined with taxonomy-aware label smoothing. +" +Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations,Xinxi Lyu,http://arxiv.org/pdf/2212.09865v2.pdf,2022-12-19,"['cs.cl', 'cs.ai']",2212.09865v2.pdf," Although large language models can be prompted for both zero- and few-shot +learning, performance drops significantly when no demonstrations are available. +In this paper, we introduce Z-ICL, a new zero-shot method that closes the gap +by constructing pseudo-demonstrations for a given test input using a raw text +corpus. Concretely, pseudo-demonstrations are constructed by (1) finding the +nearest neighbors to the test input from the corpus and pairing them with +random task labels, and (2) applying a set of techniques to reduce the amount +of direct copying the model does from the resulting demonstrations. Evaluation +on nine classification datasets shows that Z-ICL outperforms previous zero-shot +methods by a significant margin, and is on par with in-context learning with +labeled training data in the few-shot setting. Overall, Z-ICL provides a +significantly higher estimate of the zero-shot performance levels of a model, +and supports future efforts to develop better pseudo-demonstrations that +further improve zero-shot results. +" +A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge,Haodi Ma,http://arxiv.org/pdf/2301.01172v1.pdf,2023-01-03,"['cs.cl', 'cs.ai', 'cs.lg']",2301.01172v1.pdf," Knowledge graphs (KG) have served as the key component of various natural +language processing applications. Commonsense knowledge graphs (CKG) are a +special type of KG, where entities and relations are composed of free-form +text. However, previous works in KG completion and CKG completion suffer from +long-tail relations and newly-added relations which do not have many know +triples for training. In light of this, few-shot KG completion (FKGC), which +requires the strengths of graph representation learning and few-shot learning, +has been proposed to challenge the problem of limited annotated data. In this +paper, we comprehensively survey previous attempts on such tasks in the form of +a series of methods and applications. Specifically, we first introduce FKGC +challenges, commonly used KGs, and CKGs. Then we systematically categorize and +summarize existing works in terms of the type of KGs and the methods. Finally, +we present applications of FKGC models on prediction tasks in different areas +and share our thoughts on future research directions of FKGC. +" +Distillation of encoder-decoder transformers for sequence labelling,Marco Farina,http://arxiv.org/pdf/2302.05454v1.pdf,2023-02-10,"['cs.cl', 'cs.ir']",2302.05454v1.pdf," Driven by encouraging results on a wide range of tasks, the field of NLP is +experiencing an accelerated race to develop bigger language models. This race +for bigger models has also underscored the need to continue the pursuit of +practical distillation approaches that can leverage the knowledge acquired by +these big models in a compute-efficient manner. Having this goal in mind, we +build on recent work to propose a hallucination-free framework for sequence +tagging that is especially suited for distillation. We show empirical results +of new state-of-the-art performance across multiple sequence labelling datasets +and validate the usefulness of this framework for distilling a large model in a +few-shot learning scenario. +" +Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?,Chengwei Qin,http://arxiv.org/pdf/2302.08143v2.pdf,2023-02-16,"['cs.cl', 'cs.ai']",2302.08143v2.pdf," Prompt tuning (PT) which only tunes the embeddings of an additional sequence +of tokens per task, keeping the pre-trained language model (PLM) frozen, has +shown remarkable performance in few-shot learning. Despite this, PT has been +shown to rely heavily on good initialization of the prompt embeddings. In this +work, we study meta prompt tuning (MPT) to systematically explore how +meta-learning can help improve (if it can) cross-task generalization in PT +through learning to initialize the prompt embeddings from other relevant tasks. +We empirically analyze a representative set of meta learning algorithms in a +wide range of adaptation settings with different source/target task +configurations on a large set of few-shot tasks. With extensive experiments and +analysis, we demonstrate the effectiveness of MPT. We find the improvement to +be significant particularly on classification tasks. For other kinds of tasks +such as question answering, we observe that while MPT can outperform PT in most +cases, it does not always outperform multi-task learning. We further provide an +in-depth analysis from the perspective of task similarity. +" +Scalable Prompt Generation for Semi-supervised Learning with Language Models,Yuhang Zhou,http://arxiv.org/pdf/2302.09236v1.pdf,2023-02-18,"['cs.cl', 'cs.ai']",2302.09236v1.pdf," Prompt-based learning methods in semi-supervised learning (SSL) settings have +been shown to be effective on multiple natural language understanding (NLU) +datasets and tasks in the literature. However, manually designing multiple +prompts and verbalizers requires domain knowledge and human effort, making it +difficult and expensive to scale across different datasets. In this paper, we +propose two methods to automatically design multiple prompts and integrate +automatic verbalizer in SSL settings without sacrificing performance. The first +method uses various demonstration examples with learnable continuous prompt +tokens to create diverse prompt models. The second method uses a varying number +of soft prompt tokens to encourage language models to learn different prompts. +For the verbalizer, we use the prototypical verbalizer to replace the manual +one. In summary, we obtained the best average accuracy of 73.2% (a relative +improvement of 2.52% over even the previous state-of-the-art SSL method with +manual prompts and verbalizers) in different few-shot learning settings. +" +Language Models are Few-shot Learners for Prognostic Prediction,Zekai Chen,http://arxiv.org/pdf/2302.12692v4.pdf,2023-02-24,"['cs.cl', 'cs.ai', 'cs.lg', 'q-bio.qm']",2302.12692v4.pdf," Clinical prediction is an essential task in the healthcare industry. However, +the recent success of transformers, on which large language models are built, +has not been extended to this domain. In this research, we explore the use of +transformers and language models in prognostic prediction for immunotherapy +using real-world patients' clinical data and molecular profiles. This paper +investigates the potential of transformers to improve clinical prediction +compared to conventional machine learning approaches and addresses the +challenge of few-shot learning in predicting rare disease areas. The study +benchmarks the efficacy of baselines and language models on prognostic +prediction across multiple cancer types and investigates the impact of +different pretrained language models under few-shot regimes. The results +demonstrate significant improvements in accuracy and highlight the potential of +NLP in clinical research to improve early detection and intervention for +different diseases. +" +Pre-Finetuning for Few-Shot Emotional Speech Recognition,Maximillian Chen,http://arxiv.org/pdf/2302.12921v2.pdf,2023-02-24,"['cs.cl', 'cs.lg', 'cs.sd', 'eess.as']",2302.12921v2.pdf," Speech models have long been known to overfit individual speakers for many +classification tasks. This leads to poor generalization in settings where the +speakers are out-of-domain or out-of-distribution, as is common in production +environments. We view speaker adaptation as a few-shot learning problem and +propose investigating transfer learning approaches inspired by recent success +with pre-trained models in natural language tasks. We propose pre-finetuning +speech models on difficult tasks to distill knowledge into few-shot downstream +classification objectives. We pre-finetune Wav2Vec2.0 on every permutation of +four multiclass emotional speech recognition corpora and evaluate our +pre-finetuned models through 33,600 few-shot fine-tuning trials on the +Emotional Speech Dataset. +" +Mixture of Soft Prompts for Controllable Data Generation,Derek Chen,http://arxiv.org/pdf/2303.01580v2.pdf,2023-03-02,['cs.cl'],2303.01580v2.pdf," Large language models (LLMs) effectively generate fluent text when the target +output follows natural language patterns. However, structured prediction tasks +confine the output format to a limited ontology, causing even very large models +to struggle since they were never trained with such restrictions in mind. The +difficulty of using LLMs for direct prediction is exacerbated in few-shot +learning scenarios, which commonly arise due to domain shift and resource +limitations. We flip the problem on its head by leveraging the LLM as a tool +for data augmentation rather than direct prediction. Our proposed Mixture of +Soft Prompts (MSP) serves as a parameter-efficient procedure for generating +data in a controlled manner. Denoising mechanisms are further applied to +improve the quality of synthesized data. Automatic metrics show our method is +capable of producing diverse and natural text, while preserving label +semantics. Moreover, MSP achieves state-of-the-art results on three benchmarks +when compared against strong baselines. Our method offers an alternate +data-centric approach for applying LLMs to complex prediction tasks. +" +Prismer: A Vision-Language Model with An Ensemble of Experts,Shikun Liu,http://arxiv.org/pdf/2303.02506v2.pdf,2023-03-04,"['cs.lg', 'cs.ai', 'cs.cv']",2303.02506v2.pdf," Recent vision-language models have shown impressive multi-modal generation +capabilities. However, typically they require training huge models on massive +datasets. As a more scalable alternative, we introduce Prismer, a data- and +parameter-efficient vision-language model that leverages an ensemble of domain +experts. Prismer only requires training of a small number of components, with +the majority of network weights inherited from readily-available, pre-trained +domain experts, and kept frozen during training. By leveraging experts from a +wide range of domains, we show that Prismer can efficiently pool this expert +knowledge and adapt it to various vision-language reasoning tasks. In our +experiments, we show that Prismer achieves fine-tuned and few-shot learning +performance which is competitive with current state-of-the-art models, whilst +requiring up to two orders of magnitude less training data. Code is available +at https://github.com/NVlabs/prismer. +" +Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language,Philipp Seidl,http://arxiv.org/pdf/2303.03363v2.pdf,2023-03-06,"['q-bio.bm', 'cs.cl', 'cs.lg', 'stat.ml']",2303.03363v2.pdf," Activity and property prediction models are the central workhorses in drug +discovery and materials sciences, but currently they have to be trained or +fine-tuned for new tasks. Without training or fine-tuning, scientific language +models could be used for such low-data tasks through their announced zero- and +few-shot capabilities. However, their predictive quality at activity prediction +is lacking. In this work, we envision a novel type of activity prediction model +that is able to adapt to new prediction tasks at inference time, via +understanding textual information describing the task. To this end, we propose +a new architecture with separate modules for chemical and natural language +inputs, and a contrastive pre-training objective on data from large biochemical +databases. In extensive experiments, we show that our method CLAMP yields +improved predictive performance on few-shot learning benchmarks and zero-shot +problems in drug discovery. We attribute the advances of our method to the +modularized architecture and to our pre-training objective. +" +MenuCraft: Interactive Menu System Design with Large Language Models,Amir Hossein Kargaran,http://arxiv.org/pdf/2303.04496v2.pdf,2023-03-08,"['cs.cl', 'cs.ai', 'cs.hc']",2303.04496v2.pdf," Menu system design is a challenging task involving many design options and +various human factors. For example, one crucial factor that designers need to +consider is the semantic and systematic relation of menu commands. However, +capturing these relations can be challenging due to limited available +resources. With the advancement of neural language models, large language +models can utilize their vast pre-existing knowledge in designing and refining +menu systems. In this paper, we propose MenuCraft, an AI-assisted designer for +menu design that enables collaboration between the designer and a dialogue +system to design menus. MenuCraft offers an interactive language-based menu +design tool that simplifies the menu design process and enables easy +customization of design options. MenuCraft supports a variety of interactions +through dialog that allows performing zero/few-shot learning. +" +Consistency Analysis of ChatGPT,Myeongjun Erik Jang,http://arxiv.org/pdf/2303.06273v2.pdf,2023-03-11,"['cs.cl', 'cs.ai']",2303.06273v2.pdf," ChatGPT has gained a huge popularity since its introduction. Its positive +aspects have been reported through many media platforms, and some analyses even +showed that ChatGPT achieved a decent grade in professional exams, adding extra +support to the claim that AI can now assist and even replace humans in +industrial fields. Others, however, doubt its reliability and trustworthiness. +This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding +logically consistent behaviour, focusing specifically on semantic consistency +and the properties of negation, symmetric, and transitive consistency. Our +findings suggest that while both models appear to show an enhanced language +understanding and reasoning ability, they still frequently fall short of +generating logically consistent predictions. We also ascertain via experiments +that prompt designing, few-shot learning and employing larger large language +models (LLMs) are unlikely to be the ultimate solution to resolve the +inconsistency issue of LLMs. +" +Learning Expressive Prompting With Residuals for Vision Transformers,Rajshekhar Das,http://arxiv.org/pdf/2303.15591v1.pdf,2023-03-27,['cs.cv'],2303.15591v1.pdf," Prompt learning is an efficient approach to adapt transformers by inserting +learnable set of parameters into the input and intermediate representations of +a pre-trained model. In this work, we present Expressive Prompts with Residuals +(EXPRES) which modifies the prompt learning paradigm specifically for effective +adaptation of vision transformers (ViT). Out method constructs downstream +representations via learnable ``output'' tokens, that are akin to the learned +class tokens of the ViT. Further for better steering of the downstream +representation processed by the frozen transformer, we introduce residual +learnable tokens that are added to the output of various computations. We apply +EXPRES for image classification, few shot learning, and semantic segmentation, +and show our method is capable of achieving state of the art prompt tuning on +3/3 categories of the VTAB benchmark. In addition to strong performance, we +observe that our approach is an order of magnitude more prompt efficient than +existing visual prompting baselines. We analytically show the computational +benefits of our approach over weight space adaptation techniques like +finetuning. Lastly we systematically corroborate the architectural design of +our method via a series of ablation experiments. +" +Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement,Xiangyang Zhu,http://arxiv.org/pdf/2304.01195v1.pdf,2023-04-03,"['cs.cv', 'cs.ai', 'cs.mm']",2304.01195v1.pdf," The popularity of Contrastive Language-Image Pre-training (CLIP) has +propelled its application to diverse downstream vision tasks. To improve its +capacity on downstream tasks, few-shot learning has become a widely-adopted +technique. However, existing methods either exhibit limited performance or +suffer from excessive learnable parameters. In this paper, we propose APE, an +Adaptive Prior rEfinement method for CLIP's pre-trained knowledge, which +achieves superior accuracy with high computational efficiency. Via a prior +refinement module, we analyze the inter-class disparity in the downstream data +and decouple the domain-specific knowledge from the CLIP-extracted cache model. +On top of that, we introduce two model variants, a training-free APE and a +training-required APE-T. We explore the trilateral affinities between the test +image, prior cache model, and textual representations, and only enable a +lightweight category-residual module to be trained. For the average accuracy +over 11 benchmarks, both APE and APE-T attain state-of-the-art and respectively +outperform the second-best by +1.59% and +1.99% under 16 shots with x30 less +learnable parameters. +" +Sociocultural knowledge is needed for selection of shots in hate speech detection tasks,Antonis Maronikolakis,http://arxiv.org/pdf/2304.01890v4.pdf,2023-04-04,"['cs.cl', 'cs.ai', 'cs.lg']",2304.01890v4.pdf," We introduce HATELEXICON, a lexicon of slurs and targets of hate speech for +the countries of Brazil, Germany, India and Kenya, to aid training and +interpretability of models. We demonstrate how our lexicon can be used to +interpret model predictions, showing that models developed to classify extreme +speech rely heavily on target words when making predictions. Further, we +propose a method to aid shot selection for training in low-resource settings +via HATELEXICON. In few-shot learning, the selection of shots is of paramount +importance to model performance. In our work, we simulate a few-shot setting +for German and Hindi, using HASOC data for training and the Multilingual +HateCheck (MHC) as a benchmark. We show that selecting shots based on our +lexicon leads to models performing better on MHC than models trained on shots +sampled randomly. Thus, when given only a few training examples, using our +lexicon to select shots containing more sociocultural information leads to +better few-shot performance. +" +Revisiting Automated Prompting: Are We Actually Doing Better?,Yulin Zhou,http://arxiv.org/pdf/2304.03609v2.pdf,2023-04-07,"['cs.cl', 'cs.lg']",2304.03609v2.pdf," Current literature demonstrates that Large Language Models (LLMs) are great +few-shot learners, and prompting significantly increases their performance on a +range of downstream tasks in a few-shot learning setting. An attempt to +automate human-led prompting followed, with some progress achieved. In +particular, subsequent work demonstrates automation can outperform fine-tuning +in certain K-shot learning scenarios. + In this paper, we revisit techniques for automated prompting on six different +downstream tasks and a larger range of K-shot learning settings. We find that +automated prompting does not consistently outperform simple manual prompts. Our +work suggests that, in addition to fine-tuning, manual prompts should be used +as a baseline in this line of research. +" +MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning,Bohan Li,http://arxiv.org/pdf/2304.09402v1.pdf,2023-04-19,"['cs.cl', 'cs.lg']",2304.09402v1.pdf," Prompt-based learning reformulates downstream tasks as cloze problems by +combining the original input with a template. This technique is particularly +useful in few-shot learning, where a model is trained on a limited amount of +data. However, the limited templates and text used in few-shot prompt-based +learning still leave significant room for performance improvement. +Additionally, existing methods using model ensembles can constrain the model +efficiency. To address these issues, we propose an augmentation method called +MixPro, which augments both the vanilla input text and the templates through +token-level, sentence-level, and epoch-level Mixup strategies. We conduct +experiments on five few-shot datasets, and the results show that MixPro +outperforms other augmentation baselines, improving model performance by an +average of 5.08% compared to before augmentation. +" +Information Extraction from Documents: Question Answering vs Token Classification in real-world setups,Laurent Lam,http://arxiv.org/pdf/2304.10994v1.pdf,2023-04-21,['cs.cl'],2304.10994v1.pdf," Research in Document Intelligence and especially in Document Key Information +Extraction (DocKIE) has been mainly solved as Token Classification problem. +Recent breakthroughs in both natural language processing (NLP) and computer +vision helped building document-focused pre-training methods, leveraging a +multimodal understanding of the document text, layout and image modalities. +However, these breakthroughs also led to the emergence of a new DocKIE subtask +of extractive document Question Answering (DocQA), as part of the Machine +Reading Comprehension (MRC) research field. In this work, we compare the +Question Answering approach with the classical token classification approach +for document key information extraction. We designed experiments to benchmark +five different experimental setups : raw performances, robustness to noisy +environment, capacity to extract long entities, fine-tuning speed on Few-Shot +Learning and finally Zero-Shot Learning. Our research showed that when dealing +with clean and relatively short entities, it is still best to use token +classification-based approach, while the QA approach could be a good +alternative for noisy environment or long entities use-cases. +" +Discern and Answer: Mitigating the Impact of Misinformation in Retrieval-Augmented Models with Discriminators,Giwon Hong,http://arxiv.org/pdf/2305.01579v1.pdf,2023-05-02,"['cs.cl', 'cs.ai']",2305.01579v1.pdf," Most existing retrieval-augmented language models (LMs) for question +answering assume all retrieved information is factually correct. In this work, +we study a more realistic scenario in which retrieved documents may contain +misinformation, causing conflicts among them. We observe that the existing +models are highly brittle to such information in both fine-tuning and +in-context few-shot learning settings. We propose approaches to make +retrieval-augmented LMs robust to misinformation by explicitly fine-tuning a +discriminator or prompting to elicit discrimination capability in GPT-3. Our +empirical results on open-domain question answering show that these approaches +significantly improve LMs' robustness to knowledge conflicts. We also provide +our findings on interleaving the fine-tuned model's decision with the +in-context learning process, paving a new path to leverage the best of both +worlds. +" +Causal Interventions-based Few-Shot Named Entity Recognition,Zhen Yang,http://arxiv.org/pdf/2305.01914v1.pdf,2023-05-03,['cs.cl'],2305.01914v1.pdf," Few-shot named entity recognition (NER) systems aims at recognizing new +classes of entities based on a few labeled samples. A significant challenge in +the few-shot regime is prone to overfitting than the tasks with abundant +samples. The heavy overfitting in few-shot learning is mainly led by spurious +correlation caused by the few samples selection bias. To alleviate the problem +of the spurious correlation in the few-shot NER, in this paper, we propose a +causal intervention-based few-shot NER method. Based on the prototypical +network, the method intervenes in the context and prototype via backdoor +adjustment during training. In particular, intervening in the context of the +one-shot scenario is very difficult, so we intervene in the prototype via +incremental learning, which can also avoid catastrophic forgetting. Our +experiments on different benchmarks show that our approach achieves new +state-of-the-art results (achieving up to 29% absolute improvement and 12% on +average for all tasks). +" +Plug-and-Play Multilingual Few-shot Spoken Words Recognition,Aaqib Saeed,http://arxiv.org/pdf/2305.03058v1.pdf,2023-05-03,"['eess.as', 'cs.lg', 'cs.sd']",2305.03058v1.pdf," As technology advances and digital devices become prevalent, seamless +human-machine communication is increasingly gaining significance. The growing +adoption of mobile, wearable, and other Internet of Things (IoT) devices has +changed how we interact with these smart devices, making accurate spoken words +recognition a crucial component for effective interaction. However, building +robust spoken words detection system that can handle novel keywords remains +challenging, especially for low-resource languages with limited training data. +Here, we propose PLiX, a multilingual and plug-and-play keyword spotting system +that leverages few-shot learning to harness massive real-world data and enable +the recognition of unseen spoken words at test-time. Our few-shot deep models +are learned with millions of one-second audio clips across 20 languages, +achieving state-of-the-art performance while being highly efficient. Extensive +evaluations show that PLiX can generalize to novel spoken words given as few as +just one support example and performs well on unseen languages out of the box. +We release models and inference code to serve as a foundation for future +research and voice-enabled user interface development for emerging devices. +" +Data Curation for Image Captioning with Text-to-Image Generative Models,Wenyan Li,http://arxiv.org/pdf/2305.03610v1.pdf,2023-05-05,"['cs.cv', 'cs.ai', 'cs.cl']",2305.03610v1.pdf," Recent advances in image captioning are mainly driven by large-scale +vision-language pretraining, relying heavily on computational resources and +increasingly large multimodal datasets. Instead of scaling up pretraining data, +we ask whether it is possible to improve performance by improving the quality +of the samples in existing datasets. We pursue this question through two +approaches to data curation: one that assumes that some examples should be +avoided due to mismatches between the image and caption, and one that assumes +that the mismatch can be addressed by replacing the image, for which we use the +state-of-the-art Stable Diffusion model. These approaches are evaluated using +the BLIP model on MS COCO and Flickr30K in both finetuning and few-shot +learning settings. Our simple yet effective approaches consistently outperform +baselines, indicating that better image captioning models can be trained by +curating existing resources. Finally, we conduct a human study to understand +the errors made by the Stable Diffusion model and highlight directions for +future work in text-to-image generation. +" +Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives,Qiushi Sun,http://arxiv.org/pdf/2305.08088v1.pdf,2023-05-14,"['cs.cl', 'cs.ai']",2305.08088v1.pdf," Large language models (LLMs) have shown increasing power on various natural +language processing (NLP) tasks. However, tuning these models for downstream +tasks usually needs exorbitant costs or is unavailable due to commercial +considerations. Recently, black-box tuning has been proposed to address this +problem by optimizing task-specific prompts without accessing the gradients and +hidden representations. However, most existing works have yet fully exploited +the potential of gradient-free optimization under the scenario of few-shot +learning. In this paper, we describe BBT-RGB, a suite of straightforward and +complementary techniques for enhancing the efficiency and performance of +black-box optimization. Specifically, our method includes three plug-and-play +components: (1) Two-stage derivative-free optimization strategy that +facilitates fast convergence and mitigates overfitting; (2) Automatic +verbalizer construction with its novel usage under few-shot settings; (3) +Better prompt initialization policy based on instruction search and +auto-selected demonstration. Extensive experiments across various tasks on +natural language understanding and inference demonstrate the effectiveness of +our method. Our codes are publicly available at +https://github.com/QiushiSun/BBT-RGB. +" +CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online Communities,Zihao He,http://arxiv.org/pdf/2305.09846v2.pdf,2023-05-16,"['cs.cl', 'cs.si']",2305.09846v2.pdf," Detecting norm violations in online communities is critical to maintaining +healthy and safe spaces for online discussions. Existing machine learning +approaches often struggle to adapt to the diverse rules and interpretations +across different communities due to the inherent challenges of fine-tuning +models for such context-specific tasks. In this paper, we introduce +Context-aware Prompt-based Learning for Norm Violation Detection (CPL-NoViD), a +novel method that employs prompt-based learning to detect norm violations +across various types of rules. CPL-NoViD outperforms the baseline by +incorporating context through natural language prompts and demonstrates +improved performance across different rule types. Significantly, it not only +excels in cross-rule-type and cross-community norm violation detection but also +exhibits adaptability in few-shot learning scenarios. Most notably, it +establishes a new state-of-the-art in norm violation detection, surpassing +existing benchmarks. Our work highlights the potential of prompt-based learning +for context-sensitive norm violation detection and paves the way for future +research on more adaptable, context-aware models to better support online +community moderators. +" +A Weak Supervision Approach for Few-Shot Aspect Based Sentiment,Robert Vacareanu,http://arxiv.org/pdf/2305.11979v1.pdf,2023-05-19,['cs.cl'],2305.11979v1.pdf," We explore how weak supervision on abundant unlabeled data can be leveraged +to improve few-shot performance in aspect-based sentiment analysis (ABSA) +tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we +use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We +test the resulting model on three widely used ABSA datasets, before and after +fine-tuning. Our proposed method preserves the full fine-tuning performance +while showing significant improvements (15.84% absolute F1) in the few-shot +learning scenario for the harder tasks. In zero-shot (i.e., without +fine-tuning), our method outperforms the previous state of the art on the +aspect extraction sentiment classification (AESC) task and is, additionally, +capable of performing the harder aspect sentiment triplet extraction (ASTE) +task. +" +Efficient Open Domain Multi-Hop Question Answering with Few-Shot Data Synthesis,Mingda Chen,http://arxiv.org/pdf/2305.13691v1.pdf,2023-05-23,['cs.cl'],2305.13691v1.pdf," Few-shot learning for open domain multi-hop question answering typically +relies on large language models (LLMs). While powerful, LLMs are inefficient at +the inference time. We propose a data synthesis framework for multi-hop +question answering that allows for improving smaller language models with less +than 10 human-annotated question answer pairs. The framework is built upon the +data generation functions parameterized by LLMs and prompts, which requires +minimal hand-crafted features. Empirically, we synthesize millions of multi-hop +questions and claims. After finetuning language models on the synthetic data, +we evaluate the models on popular benchmarks on multi-hop question answering +and fact verification. Our experimental results show that finetuning on the +synthetic data improves model performance significantly, allowing our finetuned +models to be competitive with prior models while being almost one-third the +size in terms of parameter counts. +" +Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks,Sherzod Hakimov,http://arxiv.org/pdf/2305.13782v1.pdf,2023-05-23,['cs.cl'],2305.13782v1.pdf," Large language models have demonstrated robust performance on various +language tasks using zero-shot or few-shot learning paradigms. While being +actively researched, multimodal models that can additionally handle images as +input have yet to catch up in size and generality with language-only models. In +this work, we ask whether language-only models can be utilised for tasks that +require visual input -- but also, as we argue, often require a strong reasoning +component. Similar to some recent related work, we make visual information +accessible to the language model using separate verbalisation models. +Specifically, we investigate the performance of open-source, open-access +language models against GPT-3 on five vision-language tasks when given +textually-encoded visual information. Our results suggest that language models +are effective for solving vision-language tasks even with limited samples. This +approach also enhances the interpretability of a model's output by providing a +means of tracing the output back through the verbalised image content. +" +Improving Factuality and Reasoning in Language Models through Multiagent Debate,Yilun Du,http://arxiv.org/pdf/2305.14325v1.pdf,2023-05-23,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2305.14325v1.pdf," Large language models (LLMs) have demonstrated remarkable capabilities in +language generation, understanding, and few-shot learning in recent years. An +extensive body of work has explored how their performance may be further +improved through the tools of prompting, ranging from verification, +self-consistency, or intermediate scratchpads. In this paper, we present a +complementary approach to improve language responses where multiple language +model instances propose and debate their individual responses and reasoning +processes over multiple rounds to arrive at a common final answer. Our findings +indicate that this approach significantly enhances mathematical and strategic +reasoning across a number of tasks. We also demonstrate that our approach +improves the factual validity of generated content, reducing fallacious answers +and hallucinations that contemporary models are prone to. Our approach may be +directly applied to existing black-box models and uses identical procedure and +prompts for all tasks we investigate. Overall, our findings suggest that such +""society of minds"" approach has the potential to significantly advance the +capabilities of LLMs and pave the way for further breakthroughs in language +generation and understanding. +" +Are Large Language Models Robust Zero-shot Coreference Resolvers?,Nghia T. Le,http://arxiv.org/pdf/2305.14489v1.pdf,2023-05-23,['cs.cl'],2305.14489v1.pdf," Recent progress in domain adaptation for coreference resolution relies on +continued training using annotated data from target domains. At the same time, +pre-trained large language models (LMs) have exhibited strong zero- and +few-shot learning abilities across a wide range of NLP tasks including pronoun +resolution. While this demonstrates evidence of coreference ability, previous +work has mostly studied this ability using simple sentence-level datasets such +as the Winograd Schema Challenge. In this work, we assess the feasibility of +zero-shot learning for coreference resolution by evaluating instruction-tuned +language models on more difficult, linguistically-complex coreference +benchmarks (e.g., CoNLL-2012). We demonstrate that zero-shot prompting +outperforms current unsupervised coreference systems. Further investigations +reveal the robust zero-shot generalization ability of instruction-tuned LMs +across a wide range of domains, languages, and time periods, as well as a +strong reliance on high-quality mention detection systems. +" +Training on Thin Air: Improve Image Classification with Generated Data,Yongchao Zhou,http://arxiv.org/pdf/2305.15316v1.pdf,2023-05-24,"['cs.cv', 'cs.lg']",2305.15316v1.pdf," Acquiring high-quality data for training discriminative models is a crucial +yet challenging aspect of building effective predictive systems. In this paper, +we present Diffusion Inversion, a simple yet effective method that leverages +the pre-trained generative model, Stable Diffusion, to generate diverse, +high-quality training data for image classification. Our approach captures the +original data distribution and ensures data coverage by inverting images to the +latent space of Stable Diffusion, and generates diverse novel training images +by conditioning the generative model on noisy versions of these vectors. We +identify three key components that allow our generated images to successfully +supplant the original dataset, leading to a 2-3x enhancement in sample +complexity and a 6.5x decrease in sampling time. Moreover, our approach +consistently outperforms generic prompt-based steering methods and KNN +retrieval baseline across a wide range of datasets. Additionally, we +demonstrate the compatibility of our approach with widely-used data +augmentation techniques, as well as the reliability of the generated data in +supporting various neural architectures and enhancing few-shot learning. +" +ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation,Kuan-Hao Huang,http://arxiv.org/pdf/2305.16585v1.pdf,2023-05-26,['cs.cl'],2305.16585v1.pdf," Paraphrase generation is a long-standing task in natural language processing +(NLP). Supervised paraphrase generation models, which rely on human-annotated +paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand, +automatically annotated paraphrase pairs (e.g., by machine back-translation), +usually suffer from the lack of syntactic diversity -- the generated paraphrase +sentences are very similar to the source sentences in terms of syntax. In this +work, we present ParaAMR, a large-scale syntactically diverse paraphrase +dataset created by abstract meaning representation back-translation. Our +quantitative analysis, qualitative examples, and human evaluation demonstrate +that the paraphrases of ParaAMR are syntactically more diverse compared to +existing large-scale paraphrase datasets while preserving good semantic +similarity. In addition, we show that ParaAMR can be used to improve on three +NLP tasks: learning sentence embeddings, syntactically controlled paraphrase +generation, and data augmentation for few-shot learning. Our results thus +showcase the potential of ParaAMR for improving various NLP applications. +" +Adapting Language-Audio Models as Few-Shot Audio Learners,Jinhua Liang,http://arxiv.org/pdf/2305.17719v1.pdf,2023-05-28,"['eess.as', 'cs.sd']",2305.17719v1.pdf," We presented the Treff adapter, a training-efficient adapter for CLAP, to +boost zero-shot classification performance by making use of a small set of +labelled data. Specifically, we designed CALM to retrieve the probability +distribution of text-audio clips over classes using a set of audio-label pairs +and combined it with CLAP's zero-shot classification results. Furthermore, we +designed a training-free version of the Treff adapter by using CALM as a cosine +similarity measure. Experiments showed that the proposed Treff adapter is +comparable and even better than fully-supervised methods and adaptation methods +in low-shot and data-abundant scenarios. While the Treff adapter shows that +combining large-scale pretraining and rapid learning of domain-specific +knowledge is non-trivial for obtaining generic representations for few-shot +learning, it is still limited to audio classification tasks. In the future, we +will explore how to use audio-language models in diverse audio domains. +" +Transfer Learning for Power Outage Detection Task with Limited Training Data,Olukunle Owolabi,http://arxiv.org/pdf/2305.17817v1.pdf,2023-05-28,"['cs.cl', 'stat.ap']",2305.17817v1.pdf," Early detection of power outages is crucial for maintaining a reliable power +distribution system. This research investigates the use of transfer learning +and language models in detecting outages with limited labeled data. By +leveraging pretraining and transfer learning, models can generalize to unseen +classes. + Using a curated balanced dataset of social media tweets related to power +outages, we conducted experiments using zero-shot and few-shot learning. Our +hypothesis is that Language Models pretrained with limited data could achieve +high performance in outage detection tasks over baseline models. Results show +that while classical models outperform zero-shot Language Models, few-shot +fine-tuning significantly improves their performance. For example, with 10% +fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5% +accuracy (+8.5%). This has practical implications for analyzing and localizing +outages in scenarios with limited data availability. + Our evaluation provides insights into the potential of few-shot fine-tuning +with Language Models for power outage detection, highlighting their strengths +and limitations. This research contributes to the knowledge base of leveraging +advanced natural language processing techniques for managing critical +infrastructure. +" +Deeply Coupled Cross-Modal Prompt Learning,Xuejing Liu,http://arxiv.org/pdf/2305.17903v2.pdf,2023-05-29,['cs.cv'],2305.17903v2.pdf," Recent advancements in multimodal foundation models (e.g., CLIP) have +excelled in zero-shot generalization. Prompt tuning involved in the knowledge +transfer from foundation models to downstream tasks has gained significant +attention recently. Existing prompt-tuning methods in cross-modal learning, +however, either solely focus on language branch, or learn vision-language +interaction in a shallow mechanism. In this context, we propose a Deeply +coupled Cross-modal Prompt learning (DCP) method based on CLIP. DCP flexibly +accommodates the interplay between vision and language with a Cross-Modal +Prompt Attention (CMPA) mechanism, which enables the mutual exchange of +respective representation through a well-connected multi-head attention module +progressively and strongly. We then conduct comprehensive few-shot learning +experiments on 11 image classification datasets and analyze the robustness to +domain shift as well. Thorough experimental analysis evidently demonstrates the +superb few-shot generalization and compelling domain adaption capacity of a +well-executed DCP. The code can be found at https://github.com/GingL/CMPA. +" +"What does the Failure to Reason with ""Respectively"" in Zero/Few-Shot Settings Tell Us about Language Models?",Ruixiang Cui,http://arxiv.org/pdf/2305.19597v1.pdf,2023-05-31,"['cs.cl', 'cs.ai']",2305.19597v1.pdf," Humans can effortlessly understand the coordinate structure of sentences such +as ""Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, +respectively"". In the context of natural language inference (NLI), we examine +how language models (LMs) reason with respective readings (Gawron and Kehler, +2004) from two perspectives: syntactic-semantic and commonsense-world +knowledge. We propose a controlled synthetic dataset WikiResNLI and a naturally +occurring dataset NatResNLI to encompass various explicit and implicit +realizations of ""respectively"". We show that fine-tuned NLI models struggle +with understanding such readings without explicit supervision. While few-shot +learning is easy in the presence of explicit cues, longer training is required +when the reading is evoked implicitly, leaving models to rely on common sense +inferences. Furthermore, our fine-grained analysis indicates models fail to +generalize across different constructions. To conclude, we demonstrate that LMs +still lag behind humans in generalizing to the long tail of linguistic +constructions. +" +Measuring the Robustness of Natural Language Processing Models to Domain Shifts,Nitay Calderon,http://arxiv.org/pdf/2306.00168v2.pdf,2023-05-31,['cs.cl'],2306.00168v2.pdf," Existing research on Domain Robustness (DR) suffers from disparate setups, +lack of evaluation task variety, and reliance on challenge sets. In this paper, +we pose a fundamental question: What is the state of affairs of the DR +challenge in the era of Large Language Models (LLMs)? To this end, we construct +a DR benchmark comprising diverse NLP tasks, including sentence and token-level +classification, QA, and generation, each task consists of several domains. We +explore the DR challenge of fine-tuned and few-shot learning models in natural +domain shift settings and devise two diagnostic metrics of Out-of-Distribution +(OOD) performance degradation: The commonly used Source Drop (SD) and the +overlooked Target Drop (TD). Our findings reveal important insights: First, +despite their capabilities, zero-to-few shot LLMs and fine-tuning approaches +still fail to meet satisfactory performance in the OOD context; Second, TD +approximates better than SD the average OOD degradation; Third, in a +significant proportion of domain shifts, either SD or TD is positive, but not +both, and therefore disregarding one can lead to incorrect DR conclusions. +" +Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language,Kevin Ellis,http://arxiv.org/pdf/2306.02797v3.pdf,2023-06-05,"['cs.cl', 'cs.ai', 'cs.lg']",2306.02797v3.pdf," A core tension in models of concept learning is that the model must carefully +balance the tractability of inference against the expressivity of the +hypothesis class. Humans, however, can efficiently learn a broad range of +concepts. We introduce a model of inductive learning that seeks to be +human-like in that sense. It implements a Bayesian reasoning process where a +language model first proposes candidate hypotheses expressed in natural +language, which are then re-weighed by a prior and a likelihood. By estimating +the prior from human data, we can predict human judgments on learning problems +involving numbers and sets, spanning concepts that are generative, +discriminative, propositional, and higher-order. +" +Few Shot Rationale Generation using Self-Training with Dual Teachers,Aditya Srikanth Veerubhotla,http://arxiv.org/pdf/2306.03315v1.pdf,2023-06-05,"['cs.cl', 'cs.ai']",2306.03315v1.pdf," Self-rationalizing models that also generate a free-text explanation for +their predicted labels are an important tool to build trustworthy AI +applications. Since generating explanations for annotated labels is a laborious +and costly pro cess, recent models rely on large pretrained language models +(PLMs) as their backbone and few-shot learning. In this work we explore a +self-training approach leveraging both labeled and unlabeled data to further +improve few-shot models, under the assumption that neither human written +rationales nor annotated task labels are available at scale. We introduce a +novel dual-teacher learning framework, which learns two specialized teacher +models for task prediction and rationalization using self-training and distills +their knowledge into a multi-tasking student model that can jointly generate +the task label and rationale. Furthermore, we formulate a new loss function, +Masked Label Regularization (MLR) which promotes explanations to be strongly +conditioned on predicted labels. Evaluation on three public datasets +demonstrate that the proposed methods are effective in modeling task labels and +generating faithful rationales. +" +A New Dataset and Empirical Study for Sentence Simplification in Chinese,Shiping Yang,http://arxiv.org/pdf/2306.04188v1.pdf,2023-06-07,['cs.cl'],2306.04188v1.pdf," Sentence Simplification is a valuable technique that can benefit language +learners and children a lot. However, current research focuses more on English +sentence simplification. The development of Chinese sentence simplification is +relatively slow due to the lack of data. To alleviate this limitation, this +paper introduces CSS, a new dataset for assessing sentence simplification in +Chinese. We collect manual simplifications from human annotators and perform +data analysis to show the difference between English and Chinese sentence +simplifications. Furthermore, we test several unsupervised and zero/few-shot +learning methods on CSS and analyze the automatic evaluation and human +evaluation results. In the end, we explore whether Large Language Models can +serve as high-quality Chinese sentence simplification systems by evaluating +them on CSS. +" +Can AI Moderate Online Communities?,Henrik Axelsen,http://arxiv.org/pdf/2306.05122v1.pdf,2023-06-08,['cs.cy'],2306.05122v1.pdf," The task of cultivating healthy communication in online communities becomes +increasingly urgent, as gaming and social media experiences become +progressively more immersive and life-like. We approach the challenge of +moderating online communities by training student models using a large language +model (LLM). We use zero-shot learning models to distill and expand datasets +followed by a few-shot learning and a fine-tuning approach, leveraging +open-access generative pre-trained transformer models (GPT) from OpenAI. Our +preliminary findings suggest, that when properly trained, LLMs can excel in +identifying actor intentions, moderating toxic comments, and rewarding positive +contributions. The student models perform above-expectation in non-contextual +assignments such as identifying classically toxic behavior and perform +sufficiently on contextual assignments such as identifying positive +contributions to online discourse. Further, using open-access models like +OpenAI's GPT we experience a step-change in the development process for what +has historically been a complex modeling task. We contribute to the information +system (IS) discourse with a rapid development framework on the application of +generative AI in content online moderation and management of culture in +decentralized, pseudonymous communities by providing a sample model suite of +industrial-ready generative AI models based on open-access LLMs. +" +The ADAIO System at the BEA-2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues,Adaeze Adigwe,http://arxiv.org/pdf/2306.05360v1.pdf,2023-06-08,"['cs.cl', 'cs.ai', 'cs.cy']",2306.05360v1.pdf," This paper presents the ADAIO team's system entry in the Building Educational +Applications (BEA) 2023 Shared Task on Generating AI Teacher Responses in +Educational Dialogues. The task aims to assess the performance of +state-of-the-art generative models as AI teachers in producing suitable +responses within a student-teacher dialogue. Our system comprises evaluating +various baseline models using OpenAI GPT-3 and designing diverse prompts to +prompt the OpenAI models for teacher response generation. After the challenge, +our system achieved second place by employing a few-shot prompt-based approach +with the OpenAI text-davinci-003 model. The results highlight the few-shot +learning capabilities of large-language models, particularly OpenAI's GPT-3, in +the role of AI teachers. +" +Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning,Giridhar Kaushik Ramachandran,http://arxiv.org/pdf/2306.07170v1.pdf,2023-06-12,['cs.cl'],2306.07170v1.pdf," Social determinants of health (SDOH) documented in the electronic health +record through unstructured text are increasingly being studied to understand +how SDOH impacts patient health outcomes. In this work, we utilize the Social +History Annotation Corpus (SHAC), a multi-institutional corpus of de-identified +social history sections annotated for SDOH, including substance use, +employment, and living status information. We explore the automatic extraction +of SDOH information with SHAC in both standoff and inline annotation formats +using GPT-4 in a one-shot prompting setting. We compare GPT-4 extraction +performance with a high-performing supervised approach and perform thorough +error analyses. Our prompt-based GPT-4 method achieved an overall 0.652 F1 on +the SHAC test set, similar to the 7th best-performing system among all teams in +the n2c2 challenge with SHAC. +" +Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis,Zhengxiang Shi,http://arxiv.org/pdf/2306.07664v1.pdf,2023-06-13,"['cs.cl', 'cs.ai', 'cs.lg']",2306.07664v1.pdf," In recent years, language models (LMs) have made remarkable progress in +advancing the field of natural language processing (NLP). However, the impact +of data augmentation (DA) techniques on the fine-tuning (FT) performance of +these LMs has been a topic of ongoing debate. In this study, we evaluate the +effectiveness of three different FT methods in conjugation with +back-translation across an array of 7 diverse NLP tasks, including +classification and regression types, covering single-sentence and sentence-pair +tasks. Contrary to prior assumptions that DA does not contribute to the +enhancement of LMs' FT performance, our findings reveal that continued +pre-training on augmented data can effectively improve the FT performance of +the downstream tasks. In the most favourable case, continued pre-training +improves the performance of FT by more than 10% in the few-shot learning +setting. Our finding highlights the potential of DA as a powerful tool for +bolstering LMs' performance. +" +Neural Fine-Tuning Search for Few-Shot Learning,Panagiotis Eustratiadis,http://arxiv.org/pdf/2306.09295v1.pdf,2023-06-15,"['cs.cv', 'cs.lg']",2306.09295v1.pdf," In few-shot recognition, a classifier that has been trained on one set of +classes is required to rapidly adapt and generalize to a disjoint, novel set of +classes. To that end, recent studies have shown the efficacy of fine-tuning +with carefully crafted adaptation architectures. However this raises the +question of: How can one design the optimal adaptation strategy? In this paper, +we study this question through the lens of neural architecture search (NAS). +Given a pre-trained neural network, our algorithm discovers the optimal +arrangement of adapters, which layers to keep frozen and which to fine-tune. We +demonstrate the generality of our NAS method by applying it to both residual +networks and vision transformers and report state-of-the-art performance on +Meta-Dataset and Meta-Album. +" +Multilingual Few-Shot Learning via Language Model Retrieval,Genta Indra Winata,http://arxiv.org/pdf/2306.10964v1.pdf,2023-06-19,['cs.cl'],2306.10964v1.pdf," Transformer-based language models have achieved remarkable success in +few-shot in-context learning and drawn a lot of research interest. However, +these models' performance greatly depends on the choice of the example prompts +and also has high variability depending on how samples are chosen. In this +paper, we conduct a comprehensive study of retrieving semantically similar +few-shot samples and using them as the context, as it helps the model decide +the correct label without any gradient update in the multilingual and +cross-lingual settings. We evaluate the proposed method on five natural +language understanding datasets related to intent detection, question +classification, sentiment analysis, and topic classification. The proposed +method consistently outperforms random sampling in monolingual and +cross-lingual tasks in non-English languages. +" +Language models are weak learners,Hariharan Manikandan,http://arxiv.org/pdf/2306.14101v1.pdf,2023-06-25,"['cs.lg', 'cs.ai']",2306.14101v1.pdf," A central notion in practical and theoretical machine learning is that of a +$\textit{weak learner}$, classifiers that achieve better-than-random +performance (on any given distribution over data), even by a small margin. Such +weak learners form the practical basis for canonical machine learning methods +such as boosting. In this work, we illustrate that prompt-based large language +models can operate effectively as said weak learners. Specifically, we +illustrate the use of a large language model (LLM) as a weak learner in a +boosting algorithm applied to tabular data. We show that by providing (properly +sampled according to the distribution of interest) text descriptions of tabular +data samples, LLMs can produce a summary of the samples that serves as a +template for classification and achieves the aim of acting as a weak learner on +this task. We incorporate these models into a boosting approach, which in some +settings can leverage the knowledge within the LLM to outperform traditional +tree-based boosting. The model outperforms both few-shot learning and +occasionally even more involved fine-tuning procedures, particularly for tasks +involving small numbers of data points. The results illustrate the potential +for prompt-based LLMs to function not just as few-shot learners themselves, but +as components of larger machine learning pipelines. +" +RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations,Yilun Zhao,http://arxiv.org/pdf/2306.14321v1.pdf,2023-06-25,"['cs.cl', 'cs.ai']",2306.14321v1.pdf," Despite significant progress having been made in question answering on +tabular data (Table QA), it's unclear whether, and to what extent existing +Table QA models are robust to task-specific perturbations, e.g., replacing key +question entities or shuffling table columns. To systematically study the +robustness of Table QA models, we propose a benchmark called RobuT, which +builds upon existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and +includes human-annotated adversarial perturbations in terms of table header, +table content, and question. Our results indicate that both state-of-the-art +Table QA models and large language models (e.g., GPT-3) with few-shot learning +falter in these adversarial sets. We propose to address this problem by using +large language models to generate adversarial examples to enhance training, +which significantly improves the robustness of Table QA models. Our data and +code is publicly available at https://github.com/yilunzhao/RobuT. +" +Benchmarking Large Language Model Capabilities for Conditional Generation,Joshua Maynez,http://arxiv.org/pdf/2306.16793v1.pdf,2023-06-29,['cs.cl'],2306.16793v1.pdf," Pre-trained large language models (PLMs) underlie most new developments in +natural language processing. They have shifted the field from +application-specific model pipelines to a single model that is adapted to a +wide range of tasks. Autoregressive PLMs like GPT-3 or PaLM, alongside +techniques like few-shot learning, have additionally shifted the output +modality to generation instead of classification or regression. Despite their +ubiquitous use, the generation quality of language models is rarely evaluated +when these models are introduced. Additionally, it is unclear how existing +generation tasks--while they can be used to compare systems at a high +level--relate to the real world use cases for which people have been adopting +them. In this work, we discuss how to adapt existing application-specific +generation benchmarks to PLMs and provide an in-depth, empirical study of the +limitations and capabilities of PLMs in natural language generation tasks along +dimensions such as scale, architecture, input and output language. Our results +show that PLMs differ in their applicability to different data regimes and +their generalization to multiple languages and inform which PLMs to use for a +given generation task setup. We share best practices to be taken into +consideration when benchmarking generation capabilities during the development +of upcoming PLMs. +" +On Conditional and Compositional Language Model Differentiable Prompting,Jonathan Pilault,http://arxiv.org/pdf/2307.01446v1.pdf,2023-07-04,"['cs.cl', 'cs.lg']",2307.01446v1.pdf," Prompts have been shown to be an effective method to adapt a frozen +Pretrained Language Model (PLM) to perform well on downstream tasks. Prompts +can be represented by a human-engineered word sequence or by a learned +continuous embedding. In this work, we investigate conditional and +compositional differentiable prompting. We propose a new model, Prompt +Production System (PRopS), which learns to transform task instructions or input +metadata, into continuous prompts that elicit task-specific outputs from the +PLM. Our model uses a modular network structure based on our neural formulation +of Production Systems, which allows the model to learn discrete rules -- neural +functions that learn to specialize in transforming particular prompt input +patterns, making it suitable for compositional transfer learning and few-shot +learning. We present extensive empirical and theoretical analysis and show that +PRopS consistently surpasses other PLM adaptation techniques, and often +improves upon fully fine-tuned models, on compositional generalization tasks, +controllable summarization and multilingual translation, while needing fewer +trainable parameters. +" +Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking,Brendan King,http://arxiv.org/pdf/2307.01453v1.pdf,2023-07-04,['cs.cl'],2307.01453v1.pdf," There has been significant interest in zero and few-shot learning for +dialogue state tracking (DST) due to the high cost of collecting and annotating +task-oriented dialogues. Recent work has demonstrated that in-context learning +requires very little data and zero parameter updates, and even outperforms +trained methods in the few-shot setting (Hu et al. 2022). We propose RefPyDST, +which advances the state of the art with three advancements to in-context +learning for DST. First, we formulate DST as a Python programming task, +explicitly modeling language coreference as variable reference in Python. +Second, since in-context learning depends highly on the context examples, we +propose a method to retrieve a diverse set of relevant examples to improve +performance. Finally, we introduce a novel re-weighting method during decoding +that takes into account probabilities of competing surface forms, and produces +a more accurate dialogue state prediction. We evaluate our approach using +MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero +and few-shot settings. +" +Generating Efficient Training Data via LLM-based Attribute Manipulation,Letian Peng,http://arxiv.org/pdf/2307.07099v1.pdf,2023-07-14,['cs.cl'],2307.07099v1.pdf," In this paper, we propose a novel method, Chain-of-Thoughts Attribute +Manipulation (CoTAM), to guide few-shot learning by carefully crafted data from +Large Language Models (LLMs). The main idea is to create data with changes only +in the attribute targeted by the task. Inspired by facial attribute +manipulation, our approach generates label-switched data by leveraging LLMs to +manipulate task-specific attributes and reconstruct new sentences in a +controlled manner. Instead of conventional latent representation controlling, +we implement chain-of-thoughts decomposition and reconstruction to adapt the +procedure to LLMs. Extensive results on text classification and other tasks +verify the advantage of CoTAM over other LLM-based text generation methods with +the same number of training examples. Analysis visualizes the attribute +manipulation effectiveness of CoTAM and presents the potential of LLM-guided +learning with even less supervision. +" +Overthinking the Truth: Understanding how Language Models Process False Demonstrations,Danny Halawi,http://arxiv.org/pdf/2307.09476v1.pdf,2023-07-18,"['cs.lg', 'cs.ai', 'cs.cl']",2307.09476v1.pdf," Modern language models can imitate complex patterns through few-shot +learning, enabling them to complete challenging tasks without fine-tuning. +However, imitation can also lead models to reproduce inaccuracies or harmful +content if present in the context. We study harmful imitation through the lens +of a model's internal representations, and identify two related phenomena: +overthinking and false induction heads. The first phenomenon, overthinking, +appears when we decode predictions from intermediate layers, given correct vs. +incorrect few-shot demonstrations. At early layers, both demonstrations induce +similar model behavior, but the behavior diverges sharply at some ""critical +layer"", after which the accuracy given incorrect demonstrations progressively +decreases. The second phenomenon, false induction heads, are a possible +mechanistic cause of overthinking: these are heads in late layers that attend +to and copy false information from previous demonstrations, and whose ablation +reduces overthinking. Beyond scientific understanding, our results suggest that +studying intermediate model computations could be a promising avenue for +understanding and guarding against harmful model behaviors. +" +Does Correction Remain A Problem For Large Language Models?,Xiaowu Zhang,http://arxiv.org/pdf/2308.01776v2.pdf,2023-08-03,['cs.cl'],2308.01776v2.pdf," As large language models, such as GPT, continue to advance the capabilities +of natural language processing (NLP), the question arises: does the problem of +correction still persist? This paper investigates the role of correction in the +context of large language models by conducting two experiments. The first +experiment focuses on correction as a standalone task, employing few-shot +learning techniques with GPT-like models for error correction. The second +experiment explores the notion of correction as a preparatory task for other +NLP tasks, examining whether large language models can tolerate and perform +adequately on texts containing certain levels of noise or errors. By addressing +these experiments, we aim to shed light on the significance of correction in +the era of large language models and its implications for various NLP +applications. +" +Thespian: Multi-Character Text Role-Playing Game Agents,Christopher Cui,http://arxiv.org/pdf/2308.01872v1.pdf,2023-08-03,"['cs.ai', 'cs.cl']",2308.01872v1.pdf," Text-adventure games and text role-playing games are grand challenges for +reinforcement learning game playing agents. Text role-playing games are +open-ended environments where an agent must faithfully play a particular +character. We consider the distinction between characters and actors, where an +actor agent has the ability to play multiple characters. We present a framework +we call a thespian agent that can learn to emulate multiple characters along +with a soft prompt that can be used to direct it as to which character to play +at any time. We further describe an attention mechanism that allows the agent +to learn new characters that are based on previously learned characters in a +few-shot fashion. We show that our agent outperforms the state of the art agent +framework in multi-character learning and few-shot learning. +" +Meta-learning in healthcare: A survey,Alireza Rafiei,http://arxiv.org/pdf/2308.02877v1.pdf,2023-08-05,"['cs.lg', 'cs.ai']",2308.02877v1.pdf," As a subset of machine learning, meta-learning, or learning to learn, aims at +improving the model's capabilities by employing prior knowledge and experience. +A meta-learning paradigm can appropriately tackle the conventional challenges +of traditional learning approaches, such as insufficient number of samples, +domain shifts, and generalization. These unique characteristics position +meta-learning as a suitable choice for developing influential solutions in +various healthcare contexts, where the available data is often insufficient, +and the data collection methodologies are different. This survey discusses +meta-learning broad applications in the healthcare domain to provide insight +into how and where it can address critical healthcare challenges. We first +describe the theoretical foundations and pivotal methods of meta-learning. We +then divide the employed meta-learning approaches in the healthcare domain into +two main categories of multi/single-task learning and many/few-shot learning +and survey the studies. Finally, we highlight the current challenges in +meta-learning research, discuss the potential solutions and provide future +perspectives on meta-learning in healthcare. +" +AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models,Siheng Li,http://arxiv.org/pdf/2308.06507v1.pdf,2023-08-12,['cs.cl'],2308.06507v1.pdf," Information-seeking conversation, which aims to help users gather information +through conversation, has achieved great progress in recent years. However, the +research is still stymied by the scarcity of training data. To alleviate this +problem, we propose AutoConv for synthetic conversation generation, which takes +advantage of the few-shot learning ability and generation capacity of large +language models (LLM). Specifically, we formulate the conversation generation +problem as a language modeling task, then finetune an LLM with a few human +conversations to capture the characteristics of the information-seeking process +and use it for generating synthetic conversations with high quality. +Experimental results on two frequently-used datasets verify that AutoConv has +substantial improvements over strong baselines and alleviates the dependence on +human annotation. In addition, we also provide several analysis studies to +promote future research. +" +Few-shot Class-incremental Learning: A Survey,Jinghua Zhang,http://arxiv.org/pdf/2308.06764v1.pdf,2023-08-13,"['cs.lg', 'cs.ai']",2308.06764v1.pdf," Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in +machine learning, as it necessitates the continuous learning of new classes +from sparse labeled training samples without forgetting previous knowledge. +While this field has seen recent progress, it remains an active area of +exploration. This paper aims to provide a comprehensive and systematic review +of FSCIL. In our in-depth examination, we delve into various facets of FSCIL, +encompassing the problem definition, the discussion of primary challenges of +unreliable empirical risk minimization and the stability-plasticity dilemma, +general schemes, and relevant problems of incremental learning and few-shot +learning. Besides, we offer an overview of benchmark datasets and evaluation +metrics. Furthermore, we introduce the classification methods in FSCIL from +data-based, structure-based, and optimization-based approaches and the object +detection methods in FSCIL from anchor-free and anchor-based approaches. Beyond +these, we illuminate several promising research directions within FSCIL that +merit further investigation. +" +Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation,William Shen,http://arxiv.org/pdf/2308.07931v1.pdf,2023-07-27,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.ro']",2308.07931v1.pdf," Self-supervised and language-supervised image models contain rich knowledge +of the world that is important for generalization. Many robotic tasks, however, +require a detailed understanding of 3D geometry, which is often lacking in 2D +image features. This work bridges this 2D-to-3D gap for robotic manipulation by +leveraging distilled feature fields to combine accurate 3D geometry with rich +semantics from 2D foundation models. We present a few-shot learning method for +6-DOF grasping and placing that harnesses these strong spatial and semantic +priors to achieve in-the-wild generalization to unseen objects. Using features +distilled from a vision-language model, CLIP, we present a way to designate +novel objects for manipulation via free-text natural language, and demonstrate +its ability to generalize to unseen expressions and novel categories of +objects. +" +Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models,Zixing Zhang,http://arxiv.org/pdf/2308.11578v1.pdf,2023-08-21,"['cs.cl', 'cs.ai', 'cs.lg']",2308.11578v1.pdf," After the inception of emotion recognition or affective computing, it has +increasingly become an active research topic due to its broad applications. +Over the past couple of decades, emotion recognition models have gradually +migrated from statistically shallow models to neural network-based deep models, +which can significantly boost the performance of emotion recognition models and +consistently achieve the best results on different benchmarks. Therefore, in +recent years, deep models have always been considered the first option for +emotion recognition. However, the debut of large language models (LLMs), such +as ChatGPT, has remarkably astonished the world due to their emerged +capabilities of zero/few-shot learning, in-context learning, chain-of-thought, +and others that are never shown in previous deep models. In the present paper, +we comprehensively investigate how the LLMs perform in emotion recognition in +terms of diverse aspects, including in-context learning, few-short learning, +accuracy, generalisation, and explanation. Moreover, we offer some insights and +pose other potential challenges, hoping to ignite broader discussions about +enhancing emotion recognition in the new era of advanced and generalised large +models. +" +Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles,Georgi Pachov,http://arxiv.org/pdf/2309.06844v1.pdf,2023-09-13,"['cs.cl', 'cs.ai', 'cs.mm']",2309.06844v1.pdf," The wide-spread use of social networks has given rise to subjective, +misleading, and even false information on the Internet. Thus, subjectivity +detection can play an important role in ensuring the objectiveness and the +quality of a piece of information. This paper presents the solution built by +the Gpachov team for the CLEF-2023 CheckThat! lab Task~2 on subjectivity +detection. Three different research directions are explored. The first one is +based on fine-tuning a sentence embeddings encoder model and dimensionality +reduction. The second one explores a sample-efficient few-shot learning model. +The third one evaluates fine-tuning a multilingual transformer on an altered +dataset, using data from multiple languages. Finally, the three approaches are +combined in a simple majority voting ensemble, resulting in 0.77 macro F1 on +the test set and achieving 2nd place on the English subtask. +" +"An Empathy-Based Sandbox Approach to Bridge Attitudes, Goals, Knowledge, and Behaviors in the Privacy Paradox",Chaoran Chen,http://arxiv.org/pdf/2309.14510v1.pdf,2023-09-25,['cs.hc'],2309.14510v1.pdf," The ""privacy paradox"" describes the discrepancy between users' privacy +attitudes and their actual behaviors. Mitigating this discrepancy requires +solutions that account for both system opaqueness and users' hesitations in +testing different privacy settings due to fears of unintended data exposure. We +introduce an empathy-based approach that allows users to experience how privacy +behaviors may alter system outcomes in a risk-free sandbox environment from the +perspective of artificially generated personas. To generate realistic personas, +we introduce a novel pipeline that augments the outputs of large language +models using few-shot learning, contextualization, and chain of thoughts. Our +empirical studies demonstrated the adequate quality of generated personas and +highlighted the changes in privacy-related applications (e.g., online +advertising) caused by different personas. Furthermore, users demonstrated +cognitive and emotional empathy towards the personas when interacting with our +sandbox. We offered design implications for downstream applications in +improving user privacy literacy and promoting behavior changes. +" +Boosting In-Context Learning with Factual Knowledge,Jianing Wang,http://arxiv.org/pdf/2309.14771v1.pdf,2023-09-26,"['cs.cl', 'cs.ai']",2309.14771v1.pdf," In-Context Learning (ICL) over Large language models (LLMs) aims at solving +previously unseen tasks by conditioning on a few training examples, eliminating +the need for parameter updates and achieving competitive performance. In this +paper, we demonstrate that factual knowledge is imperative for the performance +of ICL in three core facets, i.e., the inherent knowledge learned in LLMs, the +factual knowledge derived from the selected in-context examples, and the +knowledge biases in LLMs for output generation. To unleash the power of LLMs in +few-shot learning scenarios, we introduce a novel Knowledgeable In-Context +Tuning (KICT) framework to further improve the performance of ICL: 1) injecting +factual knowledge to LLMs during continual self-supervised pre-training, 2) +judiciously selecting the examples with high knowledge relevance, and 3) +calibrating the prediction results based on prior knowledge. We evaluate the +proposed approaches on auto-regressive LLMs (e.g., GPT-style models) over +multiple text classification and question answering tasks. Experimental results +demonstrate that KICT substantially outperforms strong baselines, and improves +by more than 13% and 7% of accuracy on text classification and question +answering tasks, respectively. +" +Small Visual Language Models can also be Open-Ended Few-Shot Learners,Mohammad Mahdi Derakhshani,http://arxiv.org/pdf/2310.00500v1.pdf,2023-09-30,['cs.cv'],2310.00500v1.pdf," We present Self-Context Adaptation (SeCAt), a self-supervised approach that +unlocks open-ended few-shot abilities of small visual language models. Our +proposed adaptation algorithm explicitly learns from symbolic, yet +self-supervised training tasks. Specifically, our approach imitates image +captions in a self-supervised way based on clustering a large pool of images +followed by assigning semantically-unrelated names to clusters. By doing so, we +construct the `self-context', a training signal consisting of interleaved +sequences of image and pseudo-caption pairs and a query image for which the +model is trained to produce the right pseudo-caption. We demonstrate the +performance and flexibility of SeCAt on several multimodal few-shot datasets, +spanning various granularities. By using models with approximately 1B +parameters we outperform the few-shot abilities of much larger models, such as +Frozen and FROMAGe. SeCAt opens new possibilities for research in open-ended +few-shot learning that otherwise requires access to large or proprietary +models. +" +Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation,Matthias Lindemann,http://arxiv.org/pdf/2310.00796v1.pdf,2023-10-01,['cs.cl'],2310.00796v1.pdf," Strong inductive biases enable learning from little data and help +generalization outside of the training distribution. Popular neural +architectures such as Transformers lack strong structural inductive biases for +seq2seq NLP tasks on their own. Consequently, they struggle with systematic +generalization beyond the training distribution, e.g. with extrapolating to +longer inputs, even when pre-trained on large amounts of text. We show how a +structural inductive bias can be injected into a seq2seq model by pre-training +it to simulate structural transformations on synthetic data. Specifically, we +inject an inductive bias towards Finite State Transducers (FSTs) into a +Transformer by pre-training it to simulate FSTs given their descriptions. Our +experiments show that our method imparts the desired inductive bias, resulting +in improved systematic generalization and better few-shot learning for FST-like +tasks. +" +TRAM: Benchmarking Temporal Reasoning for Large Language Models,Yuqing Wang,http://arxiv.org/pdf/2310.00835v2.pdf,2023-10-02,['cs.cl'],2310.00835v2.pdf," Reasoning about time is essential for understanding the nuances of events +described in natural language. Previous research on this topic has been limited +in scope, characterized by a lack of standardized benchmarks that would allow +for consistent evaluations across different studies. In this paper, we +introduce TRAM, a temporal reasoning benchmark composed of ten datasets, +encompassing various temporal aspects of events such as order, arithmetic, +frequency, and duration, designed to facilitate a comprehensive evaluation of +the temporal reasoning capabilities of large language models (LLMs). We conduct +an extensive evaluation using popular LLMs, such as GPT-4 and Llama2, in both +zero-shot and few-shot learning scenarios. Additionally, we employ BERT-based +models to establish the baseline evaluations. Our findings indicate that these +models still trail human performance in temporal reasoning tasks. It is our +aspiration that TRAM will spur further progress in enhancing the temporal +reasoning abilities of LLMs. +" +Procedural Text Mining with Large Language Models,Anisa Rula,http://arxiv.org/pdf/2310.03376v1.pdf,2023-10-05,"['cs.cl', 'cs.ai', 'cs.it', 'math.it']",2310.03376v1.pdf," Recent advancements in the field of Natural Language Processing, particularly +the development of large-scale language models that are pretrained on vast +amounts of knowledge, are creating novel opportunities within the realm of +Knowledge Engineering. In this paper, we investigate the usage of large +language models (LLMs) in both zero-shot and in-context learning settings to +tackle the problem of extracting procedures from unstructured PDF text in an +incremental question-answering fashion. In particular, we leverage the current +state-of-the-art GPT-4 (Generative Pre-trained Transformer 4) model, +accompanied by two variations of in-context learning that involve an ontology +with definitions of procedures and steps and a limited number of samples of +few-shot learning. The findings highlight both the promise of this approach and +the value of the in-context learning customisations. These modifications have +the potential to significantly address the challenge of obtaining sufficient +training data, a hurdle often encountered in deep learning-based Natural +Language Processing techniques for procedure extraction. +" +PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification,Feihong He,http://arxiv.org/pdf/2310.03517v1.pdf,2023-10-05,['cs.cv'],2310.03517v1.pdf," Few-shot image classification has received considerable attention for +addressing the challenge of poor classification performance with limited +samples in novel classes. However, numerous studies have employed sophisticated +learning strategies and diversified feature extraction methods to address this +issue. In this paper, we propose our method called PrototypeFormer, which aims +to significantly advance traditional few-shot image classification approaches +by exploring prototype relationships. Specifically, we utilize a transformer +architecture to build a prototype extraction module, aiming to extract class +representations that are more discriminative for few-shot classification. +Additionally, during the model training process, we propose a contrastive +learning-based optimization approach to optimize prototype features in few-shot +learning scenarios. Despite its simplicity, the method performs remarkably +well, with no bells and whistles. We have experimented with our approach on +several popular few-shot image classification benchmark datasets, which shows +that our method outperforms all current state-of-the-art methods. In +particular, our method achieves 97.07% and 90.88% on 5-way 5-shot and 5-way +1-shot tasks of miniImageNet, which surpasses the state-of-the-art results with +accuracy of 7.27% and 8.72%, respectively. The code will be released later. +" +A Holistic Evaluation of Piano Sound Quality,Monan Zhou,http://arxiv.org/pdf/2310.04722v1.pdf,2023-10-07,"['cs.sd', 'cs.ai', 'eess.as']",2310.04722v1.pdf," This paper aims to develop a holistic evaluation method for piano sound +quality to assist in purchasing decisions. Unlike previous studies that focused +on the effect of piano performance techniques on sound quality, this study +evaluates the inherent sound quality of different pianos. To derive quality +evaluation systems, the study uses subjective questionnaires based on a piano +sound quality dataset. The method selects the optimal piano classification +models by comparing the fine-tuning results of different pre-training models of +Convolutional Neural Networks (CNN). To improve the interpretability of the +models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The +results reveal that musically trained individuals are better able to +distinguish between the sound quality differences of different pianos. The best +fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3\% as the +piano classifier. However, the dataset is limited, and the audio is sliced to +increase its quantity, resulting in a lack of diversity and balance, so we use +focal loss to reduce the impact of data imbalance. To optimize the method, the +dataset will be expanded, or few-shot learning techniques will be employed in +future research. +" +Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning,Arushi Sharma,http://arxiv.org/pdf/2310.07093v1.pdf,2023-10-11,['cs.cl'],2310.07093v1.pdf," To advance argumentative stance prediction as a multimodal problem, the First +Shared Task in Multimodal Argument Mining hosted stance prediction in crucial +social topics of gun control and abortion. Our exploratory study attempts to +evaluate the necessity of images for stance prediction in tweets and compare +out-of-the-box text-based large-language models (LLM) in few-shot settings +against fine-tuned unimodal and multimodal models. Our work suggests an +ensemble of fine-tuned text-based language models (0.817 F1-score) outperforms +both the multimodal (0.677 F1-score) and text-based few-shot prediction using a +recent state-of-the-art LLM (0.550 F1-score). In addition to the differences in +performance, our findings suggest that the multimodal models tend to perform +better when image content is summarized as natural language over their native +pixel structure and, using in-context examples improves few-shot performance of +LLMs. +" +LLM-augmented Preference Learning from Natural Language,Inwon Kang,http://arxiv.org/pdf/2310.08523v1.pdf,2023-10-12,['cs.cl'],2310.08523v1.pdf," Finding preferences expressed in natural language is an important but +challenging task. State-of-the-art(SotA) methods leverage transformer-based +models such as BERT, RoBERTa, etc. and graph neural architectures such as graph +attention networks. Since Large Language Models (LLMs) are equipped to deal +with larger context lengths and have much larger model sizes than the +transformer-based model, we investigate their ability to classify comparative +text directly. This work aims to serve as a first step towards using LLMs for +the CPC task. We design and conduct a set of experiments that format the +classification task into an input prompt for the LLM and a methodology to get a +fixed-format response that can be automatically evaluated. Comparing +performances with existing methods, we see that pre-trained LLMs are able to +outperform the previous SotA models with no fine-tuning involved. Our results +show that the LLMs can consistently outperform the SotA when the target text is +large -- i.e. composed of multiple sentences --, and are still comparable to +the SotA performance in shorter text. We also find that few-shot learning +yields better performance than zero-shot learning. +" +In-Context Learning for Few-Shot Molecular Property Prediction,Christopher Fifty,http://arxiv.org/pdf/2310.08863v1.pdf,2023-10-13,['cs.lg'],2310.08863v1.pdf," In-context learning has become an important approach for few-shot learning in +Large Language Models because of its ability to rapidly adapt to new tasks +without fine-tuning model parameters. However, it is restricted to applications +in natural language and inapplicable to other domains. In this paper, we adapt +the concepts underpinning in-context learning to develop a new algorithm for +few-shot molecular property prediction. Our approach learns to predict +molecular properties from a context of (molecule, property measurement) pairs +and rapidly adapts to new properties without fine-tuning. On the FS-Mol and +BACE molecular property prediction benchmarks, we find this method surpasses +the performance of recent meta-learning algorithms at small support sizes and +is competitive with the best methods at large support sizes. +" +In-Context Few-Shot Relation Extraction via Pre-Trained Language Models,Yilmazcan Ozyurt,http://arxiv.org/pdf/2310.11085v1.pdf,2023-10-17,"['cs.cl', 'cs.ai', 'cs.lg']",2310.11085v1.pdf," Relation extraction aims at inferring structured human knowledge from textual +documents. State-of-the-art methods based on language models commonly have two +limitations: (1) they require named entities to be either given as input or +infer them, which introduces additional noise, and (2) they require human +annotations of documents. As a remedy, we present a novel framework for +in-context few-shot relation extraction via pre-trained language models. To the +best of our knowledge, we are the first to reformulate the relation extraction +task as a tailored in-context few-shot learning paradigm. Thereby, we achieve +crucial benefits in that we eliminate the need for both named entity +recognition and human annotation of documents. Unlike existing methods based on +fine-tuning, our framework is flexible in that it can be easily updated for a +new set of relations without re-training. We evaluate our framework using +DocRED, the largest publicly available dataset for document-level relation +extraction, and demonstrate that our framework achieves state-of-the-art +performance. Finally, our framework allows us to identify missing annotations, +and we thus show that our framework actually performs much better than the +original labels from the development set of DocRED. +" +Group Preference Optimization: Few-Shot Alignment of Large Language Models,Siyan Zhao,http://arxiv.org/pdf/2310.11523v1.pdf,2023-10-17,"['cs.lg', 'cs.ai', 'cs.cl']",2310.11523v1.pdf," Many applications of large language models (LLMs), ranging from chatbots to +creative writing, require nuanced subjective judgments that can differ +significantly across different groups. Existing alignment algorithms can be +expensive to align for each group, requiring prohibitive amounts of +group-specific preference data and computation for real-world use cases. We +introduce Group Preference Optimization (GPO), an alignment framework that +steers language models to preferences of individual groups in a few-shot +manner. In GPO, we augment the base LLM with an independent transformer module +trained to predict the preferences of a group for the LLM generations. For +few-shot learning, we parameterize this module as an in-context autoregressive +transformer and train it via meta-learning on several groups. We empirically +validate the efficacy of GPO through rigorous evaluations using LLMs with +varied sizes on three human opinion adaptation tasks. These tasks involve +adapting to the preferences of US demographic groups, global countries, and +individual users. Our results demonstrate that GPO not only aligns models more +accurately but also requires fewer group-specific preferences, and less +training and inference computing resources, outperforming existing strategies +such as in-context steering and fine-tuning methods. +" +CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition,Kari A Noriy,http://arxiv.org/pdf/2310.11830v2.pdf,2023-10-18,"['cs.sd', 'cs.lg', 'cs.mm', 'eess.as']",2310.11830v2.pdf," Multilingual speech processing requires understanding emotions, a task made +difficult by limited labelled data. CLARA, minimizes reliance on labelled data, +enhancing generalization across languages. It excels at fostering shared +representations, aiding cross-lingual transfer of speech and emotions, even +with little data. Our approach adeptly captures emotional nuances in speech, +overcoming subjective assessment issues. Using a large multilingual audio +corpus and self-supervised learning, CLARA develops speech representations +enriched with emotions, advancing emotion-aware multilingual speech processing. + Our method expands the data range using data augmentation, textual embedding +for visual understanding, and transfers knowledge from high- to low-resource +languages. CLARA demonstrates excellent performance in emotion recognition, +language comprehension, and audio benchmarks, excelling in zero-shot and +few-shot learning. It adapts to low-resource languages, marking progress in +multilingual speech representation learning. +" +A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation,Giuseppe Attanasio,http://arxiv.org/pdf/2310.12127v2.pdf,2023-10-18,"['cs.cl', 'cs.lg']",2310.12127v2.pdf," Recent instruction fine-tuned models can solve multiple NLP tasks when +prompted to do so, with machine translation (MT) being a prominent use case. +However, current research often focuses on standard performance benchmarks, +leaving compelling fairness and ethical considerations behind. In MT, this +might lead to misgendered translations, resulting, among other harms, in the +perpetuation of stereotypes and prejudices. In this work, we address this gap +by investigating whether and to what extent such models exhibit gender bias in +machine translation and how we can mitigate it. Concretely, we compute +established gender bias metrics on the WinoMT corpus from English to German and +Spanish. We discover that IFT models default to male-inflected translations, +even disregarding female occupational stereotypes. Next, using interpretability +methods, we unveil that models systematically overlook the pronoun indicating +the gender of a target occupation in misgendered translations. Finally, based +on this finding, we propose an easy-to-implement and effective bias mitigation +solution based on few-shot learning that leads to significantly fairer +translations. +" +An Exploration of In-Context Learning for Speech Language Model,Ming-Hao Hsu,http://arxiv.org/pdf/2310.12477v1.pdf,2023-10-19,"['eess.as', 'cs.ai', 'cs.cl']",2310.12477v1.pdf," Ever since the development of GPT-3 in the natural language processing (NLP) +field, in-context learning (ICL) has played an important role in utilizing +large language models (LLMs). By presenting the LM utterance-label +demonstrations at the input, the LM can accomplish few-shot learning without +relying on gradient descent or requiring explicit modification of its +parameters. This enables the LM to learn and adapt in a black-box manner. +Despite the success of ICL in NLP, little work is exploring the possibility of +ICL in speech processing. This study proposes the first exploration of ICL with +a speech LM without text supervision. We first show that the current speech LM +does not have the ICL capability. With the proposed warmup training, the speech +LM can, therefore, perform ICL on unseen tasks. In this work, we verify the +feasibility of ICL for speech LM on speech classification tasks. +" +Large Language Models are biased to overestimate profoundness,Eugenio Herrera-Berg,http://arxiv.org/pdf/2310.14422v1.pdf,2023-10-22,['cs.cl'],2310.14422v1.pdf," Recent advancements in natural language processing by large language models +(LLMs), such as GPT-4, have been suggested to approach Artificial General +Intelligence. And yet, it is still under dispute whether LLMs possess similar +reasoning abilities to humans. This study evaluates GPT-4 and various other +LLMs in judging the profoundness of mundane, motivational, and pseudo-profound +statements. We found a significant statement-to-statement correlation between +the LLMs and humans, irrespective of the type of statements and the prompting +technique used. However, LLMs systematically overestimate the profoundness of +nonsensical statements, with the exception of Tk-instruct, which uniquely +underestimates the profoundness of statements. Only few-shot learning prompts, +as opposed to chain-of-thought prompting, draw LLMs ratings closer to humans. +Furthermore, this work provides insights into the potential biases induced by +Reinforcement Learning from Human Feedback (RLHF), inducing an increase in the +bias to overestimate the profoundness of statements. +" +Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning,Ananth Balashankar,http://arxiv.org/pdf/2310.16959v1.pdf,2023-10-25,['cs.lg'],2310.16959v1.pdf," As large language models (LLMs) are widely adopted, new safety issues and +policies emerge, to which existing safety classifiers do not generalize well. +If we have only observed a few examples of violations of a new safety rule, how +can we build a classifier to detect violations? In this paper, we study the +novel setting of domain-generalized few-shot learning for LLM-based text safety +classifiers. Unlike prior few-shot work, these new safety issues can be hard to +uncover and we do not get to choose the few examples. We demonstrate that +existing few-shot techniques do not perform well in this setting, and rather we +propose to do parameter-efficient fine-tuning (PEFT) combined with augmenting +training data based on similar examples in prior existing rules. We empirically +show that our approach of similarity-based data-augmentation + prompt-tuning +(DAPT) consistently outperforms baselines that either do not rely on data +augmentation or on PEFT by 7-17% F1 score in the Social Chemistry moral +judgement and 9-13% AUC in the Toxicity detection tasks, even when the new rule +is loosely correlated with existing ones. +" +Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning,Sapan Shah,http://arxiv.org/pdf/2310.18930v1.pdf,2023-10-29,['cs.cl'],2310.18930v1.pdf," We present a novel retrofitting method to induce emotion aspects into +pre-trained language models (PLMs) such as BERT and RoBERTa. Our method updates +pre-trained network weights using contrastive learning so that the text +fragments exhibiting similar emotions are encoded nearby in the representation +space, and the fragments with different emotion content are pushed apart. While +doing so, it also ensures that the linguistic knowledge already present in PLMs +is not inadvertently perturbed. The language models retrofitted by our method, +i.e., BERTEmo and RoBERTaEmo, produce emotion-aware text representations, as +evaluated through different clustering and retrieval metrics. For the +downstream tasks on sentiment analysis and sarcasm detection, they perform +better than their pre-trained counterparts (about 1% improvement in F1-score) +and other existing approaches. Additionally, a more significant boost in +performance is observed for the retrofitted models over pre-trained ones in +few-shot learning setting. +" +Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection,Yunze Xiao,http://arxiv.org/pdf/2311.03184v1.pdf,2023-11-06,"['cs.cl', 'cs.ai', 'cs.si', '68t50', 'f.2.2; i.2.7']",2311.03184v1.pdf," The spread of disinformation and propagandistic content poses a threat to +societal harmony, undermining informed decision-making and trust in reliable +sources. Online platforms often serve as breeding grounds for such content, and +malicious actors exploit the vulnerabilities of audiences to shape public +opinion. Although there have been research efforts aimed at the automatic +identification of disinformation and propaganda in social media content, there +remain challenges in terms of performance. The ArAIEval shared task aims to +further research on these particular issues within the context of the Arabic +language. In this paper, we discuss our participation in these shared tasks. We +competed in subtasks 1A and 2A, where our submitted system secured positions +9th and 10th, respectively. Our experiments consist of fine-tuning transformer +models and using zero- and few-shot learning with GPT-4. +" +Multilingual Mathematical Autoformalization,Albert Q. Jiang,http://arxiv.org/pdf/2311.03755v1.pdf,2023-11-07,"['cs.cl', 'cs.lg']",2311.03755v1.pdf," Autoformalization is the task of translating natural language materials into +machine-verifiable formalisations. Progress in autoformalization research is +hindered by the lack of a sizeable dataset consisting of informal-formal pairs +expressing the same essence. Existing methods tend to circumvent this challenge +by manually curating small corpora or using few-shot learning with large +language models. But these methods suffer from data scarcity and formal +language acquisition difficulty. In this work, we create $\texttt{MMA}$, a +large, flexible, multilingual, and multi-domain dataset of informal-formal +pairs, by using a language model to translate in the reverse direction, that +is, from formal mathematical statements into corresponding informal ones. +Experiments show that language models fine-tuned on $\texttt{MMA}$ produce +$16-18\%$ of statements acceptable with minimal corrections on the +$\texttt{miniF2F}$ and $\texttt{ProofNet}$ benchmarks, up from $0\%$ with the +base model. We demonstrate that fine-tuning on multilingual formal data results +in more capable autoformalization models even when deployed on monolingual +tasks. +" +Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks,Igor Shalyminov,http://arxiv.org/pdf/1910.01302v1.pdf,2019-10-03,"['cs.cl', 'i.2.7']",1910.01302v1.pdf," Goal-oriented dialogue systems are now being widely adopted in industry where +it is of key importance to maintain a rapid prototyping cycle for new products +and domains. Data-driven dialogue system development has to be adapted to meet +this requirement --- therefore, reducing the amount of data and annotations +necessary for training such systems is a central research problem. + In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet), +a state-of-the-art approach to goal-oriented dialogue generation which only +uses a few example dialogues (i.e. few-shot learning), none of which has to be +annotated. We achieve this by performing a 2-stage training. Firstly, we +perform unsupervised dialogue representation pre-training on a large source of +goal-oriented dialogues in multiple domains, the MetaLWOz corpus. Secondly, at +the transfer stage, we train DiKTNet using this representation together with 2 +other textual knowledge sources with different levels of generality: ELMo +encoder and the main dataset's source domains. + Our main dataset is the Stanford Multi-Domain dialogue corpus. We evaluate +our model on it in terms of BLEU and Entity F1 scores, and show that our +approach significantly and consistently improves upon a series of baseline +models as well as over the previous state-of-the-art dialogue generation model, +ZSDG. The improvement upon the latter --- up to 10% in Entity F1 and the +average of 3% in BLEU score --- is achieved using only the equivalent of 10% of +ZSDG's in-domain training data. +" +Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection,Shumin Deng,http://arxiv.org/pdf/1910.11621v2.pdf,2019-10-25,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",1910.11621v2.pdf," Event detection (ED), a sub-task of event extraction, involves identifying +triggers and categorizing event mentions. Existing methods primarily rely upon +supervised learning and require large-scale labeled event datasets which are +unfortunately not readily available in many real-life applications. In this +paper, we consider and reformulate the ED task with limited labeled data as a +Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical +Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn +better prototypes for event types, but also produce more robust sentence +encodings for event mentions. Differing from vanilla prototypical networks +simply computing event prototypes by averaging, which only consume event +mentions once, our model is more robust and is capable of distilling contextual +information from event mentions for multiple times due to the multi-hop +mechanism of DMNs. The experiments show that DMB-PN not only deals with sample +scarcity better than a series of baseline models but also performs more +robustly when the variety of event types is relatively large and the instance +quantity is extremely small. +" +Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data,Zhiyuan Wu,http://arxiv.org/pdf/2103.13733v2.pdf,2021-03-25,"['cs.cv', 'cs.ai', 'cs.lg']",2103.13733v2.pdf," Semantic segmentation of road scenes is one of the key technologies for +realizing autonomous driving scene perception, and the effectiveness of deep +Convolutional Neural Networks(CNNs) for this task has been demonstrated. +State-of-art CNNs for semantic segmentation suffer from excessive computations +as well as large-scale training data requirement. Inspired by the ideas of +Fine-tuning-based Transfer Learning (FTT) and feature-based knowledge +distillation, we propose a new knowledge distillation method for cross-domain +knowledge transference and efficient data-insufficient network training, named +Spirit Distillation(SD), which allow the student network to mimic the teacher +network to extract general features, so that a compact and accurate student +network can be trained for real-time semantic segmentation of road scenes. +Then, in order to further alleviate the trouble of insufficient data and +improve the robustness of the student, an Enhanced Spirit Distillation (ESD) +method is proposed, which commits to exploit a more comprehensive general +features extraction capability by considering images from both the target and +the proximity domains as input. To our knowledge, this paper is a pioneering +work on the application of knowledge distillation to few-shot learning. +Persuasive experiments conducted on Cityscapes semantic segmentation with the +prior knowledge transferred from COCO2017 and KITTI demonstrate that our +methods can train a better student network (mIOU and high-precision accuracy +boost by 1.4% and 8.2% respectively, with 78.2% segmentation variance) with +only 41.8% FLOPs (see Fig. 1). +" +AMP0: Species-Specific Prediction of Anti-microbial Peptides using Zero and Few Shot Learning,Sadaf Gull,http://arxiv.org/pdf/1911.06106v1.pdf,2019-10-28,"['q-bio.bm', 'cs.lg', 'stat.ml']",1911.06106v1.pdf," The evolution of drug-resistant microbial species is one of the major +challenges to global health. The development of new antimicrobial treatments +such as antimicrobial peptides needs to be accelerated to combat this threat. +However, the discovery of novel antimicrobial peptides is hampered by +low-throughput biochemical assays. Computational techniques can be used for +rapid screening of promising antimicrobial peptide candidates prior to testing +in the wet lab. The vast majority of existing antimicrobial peptide predictors +are non-targeted in nature, i.e., they can predict whether a given peptide +sequence is antimicrobial, but they are unable to predict whether the sequence +can target a particular microbial species. In this work, we have developed a +targeted antimicrobial peptide activity predictor that can predict whether a +peptide is effective against a given microbial species or not. This has been +made possible through zero-shot and few-shot machine learning. The proposed +predictor called AMP0 takes in the peptide amino acid sequence and any +N/C-termini modifications together with the genomic sequence of a target +microbial species to generate targeted predictions. It is important to note +that the proposed method can generate predictions for species that are not part +of its training set. The accuracy of predictions for novel test species can be +further improved by providing a few example peptides for that species. Our +computational cross-validation results show that the pro-posed scheme is +particularly effective for targeted antimicrobial prediction in comparison to +existing approaches and can be used for screening potential antimicrobial +peptides in a targeted manner especially for cases in which the number of +training examples is small. The webserver of the method is available at +http://ampzero.pythonanywhere.com. +" +Brain-inspired global-local learning incorporated with neuromorphic computing,Yujie Wu,http://arxiv.org/pdf/2006.03226v3.pdf,2020-06-05,"['cs.ne', 'cs.ai', 'q-bio.nc']",2006.03226v3.pdf," Two main routes of learning methods exist at present including error-driven +global learning and neuroscience-oriented local learning. Integrating them into +one network may provide complementary learning capabilities for versatile +learning scenarios. At the same time, neuromorphic computing holds great +promise, but still needs plenty of useful algorithms and algorithm-hardware +co-designs for exploiting the advantages. Here, we report a neuromorphic hybrid +learning model by introducing a brain-inspired meta-learning paradigm and a +differentiable spiking model incorporating neuronal dynamics and synaptic +plasticity. It can meta-learn local plasticity and receive top-down supervision +information for multiscale synergic learning. We demonstrate the advantages of +this model in multiple different tasks, including few-shot learning, continual +learning, and fault-tolerance learning in neuromorphic vision sensors. It +achieves significantly higher performance than single-learning methods, and +shows promise in empowering neuromorphic applications revolution. We further +implemented the hybrid model in the Tianjic neuromorphic platform by exploiting +algorithm-hardware co-designs and proved that the model can fully utilize +neuromorphic many-core architecture to develop hybrid computation paradigm. +" +Direct multimodal few-shot learning of speech and images,Leanne Nortje,http://arxiv.org/pdf/2012.05680v2.pdf,2020-12-10,"['cs.cl', 'cs.sd', 'eess.as']",2012.05680v2.pdf," We propose direct multimodal few-shot models that learn a shared embedding +space of spoken words and images from only a few paired examples. Imagine an +agent is shown an image along with a spoken word describing the object in the +picture, e.g. pen, book and eraser. After observing a few paired examples of +each class, the model is asked to identify the ""book"" in a set of unseen +pictures. Previous work used a two-step indirect approach relying on learned +unimodal representations: speech-speech and image-image comparisons are +performed across the support set of given speech-image pairs. We propose two +direct models which instead learn a single multimodal space where inputs from +different modalities are directly comparable: a multimodal triplet network +(MTriplet) and a multimodal correspondence autoencoder (MCAE). To train these +direct models, we mine speech-image pairs: the support set is used to pair up +unlabelled in-domain speech and images. In a speech-to-image digit matching +task, direct models outperform indirect models, with the MTriplet achieving the +best multimodal five-shot accuracy. We show that the improvements are due to +the combination of unsupervised and transfer learning in the direct models, and +the absence of two-step compounding errors. +" +What Makes Good In-Context Examples for GPT-$3$?,Jiachang Liu,http://arxiv.org/pdf/2101.06804v1.pdf,2021-01-17,['cs.cl'],2101.06804v1.pdf," GPT-$3$ has attracted lots of attention due to its superior performance +across a wide range of NLP tasks, especially with its powerful and versatile +in-context few-shot learning ability. Despite its success, we found that the +empirical results of GPT-$3$ depend heavily on the choice of in-context +examples. In this work, we investigate whether there are more effective +strategies for judiciously selecting in-context examples (relative to random +sampling) that better leverage GPT-$3$'s few-shot capabilities. Inspired by the +recent success of leveraging a retrieval module to augment large-scale neural +network models, we propose to retrieve examples that are semantically-similar +to a test sample to formulate its corresponding prompt. Intuitively, the +in-context examples selected with such a strategy may serve as more informative +inputs to unleash GPT-$3$'s extensive knowledge. We evaluate the proposed +approach on several natural language understanding and generation benchmarks, +where the retrieval-based prompt selection approach consistently outperforms +the random baseline. Moreover, it is observed that the sentence encoders +fine-tuned on task-related datasets yield even more helpful retrieval results. +Notably, significant gains are observed on tasks such as table-to-text +generation (41.9% on the ToTTo dataset) and open-domain question answering +(45.5% on the NQ dataset). We hope our investigation could help understand the +behaviors of GPT-$3$ and large-scale pre-trained LMs in general and enhance +their few-shot capabilities. +" +Modelling Latent Translations for Cross-Lingual Transfer,Edoardo Maria Ponti,http://arxiv.org/pdf/2107.11353v1.pdf,2021-07-23,['cs.cl'],2107.11353v1.pdf," While achieving state-of-the-art results in multiple tasks and languages, +translation-based cross-lingual transfer is often overlooked in favour of +massively multilingual pre-trained encoders. Arguably, this is due to its main +limitations: 1) translation errors percolating to the classification phase and +2) the insufficient expressiveness of the maximum-likelihood translation. To +remedy this, we propose a new technique that integrates both steps of the +traditional pipeline (translation and classification) into a single model, by +treating the intermediate translations as a latent random variable. As a +result, 1) the neural machine translation system can be fine-tuned with a +variant of Minimum Risk Training where the reward is the accuracy of the +downstream task classifier. Moreover, 2) multiple samples can be drawn to +approximate the expected loss across all possible translations during +inference. We evaluate our novel latent translation-based model on a series of +multilingual NLU tasks, including commonsense reasoning, paraphrase +identification, and natural language inference. We report gains for both +zero-shot and few-shot learning setups, up to 2.7 accuracy points on average, +which are even more prominent for low-resource languages (e.g., Haitian +Creole). Finally, we carry out in-depth analyses comparing different underlying +NMT models and assessing the impact of alternative translations on the +downstream performance. +" +ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback,Mike Wu,http://arxiv.org/pdf/2107.14035v2.pdf,2021-07-23,"['cs.cy', 'cs.lg']",2107.14035v2.pdf," High-quality computer science education is limited by the difficulty of +providing instructor feedback to students at scale. While this feedback could +in principle be automated, supervised approaches to predicting the correct +feedback are bottlenecked by the intractability of annotating large quantities +of student code. In this paper, we instead frame the problem of providing +feedback as few-shot classification, where a meta-learner adapts to give +feedback to student code on a new programming question from just a few examples +annotated by instructors. Because data for meta-training is limited, we propose +a number of amendments to the typical few-shot learning framework, including +task augmentation to create synthetic tasks, and additional side information to +build stronger priors about each task. These additions are combined with a +transformer architecture to embed discrete sequences (e.g. code) to a +prototypical representation of a feedback class label. On a suite of few-shot +natural language processing tasks, we match or outperform state-of-the-art +performance. Then, on a collection of student solutions to exam questions from +an introductory university course, we show that our approach reaches an average +precision of 88% on unseen questions, surpassing the 82% precision of teaching +assistants. Our approach was successfully deployed to deliver feedback to +16,000 student exam-solutions in a programming course offered by a tier 1 +university. This is, to the best of our knowledge, the first successful +deployment of a machine learning based feedback to open-ended student code. +" +Robust Retrieval Augmented Generation for Zero-shot Slot Filling,Michael Glass,http://arxiv.org/pdf/2108.13934v2.pdf,2021-08-31,"['cs.cl', 'cs.ai', 'cs.ir']",2108.13934v2.pdf," Automatically inducing high quality knowledge graphs from a given collection +of documents still remains a challenging problem in AI. One way to make headway +for this problem is through advancements in a related task known as slot +filling. In this task, given an entity query in form of [Entity, Slot, ?], a +system is asked to fill the slot by generating or extracting the missing value +exploiting evidence extracted from relevant passage(s) in the given document +collection. The recent works in the field try to solve this task in an +end-to-end fashion using retrieval-based language models. In this paper, we +present a novel approach to zero-shot slot filling that extends dense passage +retrieval with hard negatives and robust training procedures for retrieval +augmented generation models. Our model reports large improvements on both T-REx +and zsRE slot filling datasets, improving both passage retrieval and slot value +generation, and ranking at the top-1 position in the KILT leaderboard. +Moreover, we demonstrate the robustness of our system showing its domain +adaptation capability on a new variant of the TACRED dataset for slot filling, +through a combination of zero/few-shot learning. We release the source code and +pre-trained models. +" +Template-free Prompt Tuning for Few-shot NER,Ruotian Ma,http://arxiv.org/pdf/2109.13532v3.pdf,2021-09-28,"['cs.cl', 'cs.ai']",2109.13532v3.pdf," Prompt-based methods have been successfully applied in sentence-level +few-shot learning tasks, mostly owing to the sophisticated design of templates +and label words. However, when applied to token-level labeling tasks such as +NER, it would be time-consuming to enumerate the template queries over all +potential entity spans. In this work, we propose a more elegant method to +reformulate NER tasks as LM problems without any templates. Specifically, we +discard the template construction process while maintaining the word prediction +paradigm of pre-training models to predict a class-related pivot word (or label +word) at the entity position. Meanwhile, we also explore principled ways to +automatically search for appropriate label words that the pre-trained models +can easily adapt to. While avoiding complicated template-based process, the +proposed LM objective also reduces the gap between different objectives used in +pre-training and fine-tuning, thus it can better benefit the few-shot +performance. Experimental results demonstrate the effectiveness of the proposed +method over bert-tagger and template-based method under few-shot setting. +Moreover, the decoding speed of the proposed method is up to 1930.12 times +faster than the template-based method. +" +RAFT: A Real-World Few-Shot Text Classification Benchmark,Neel Alex,http://arxiv.org/pdf/2109.14076v3.pdf,2021-09-28,"['cs.cl', 'cs.ai', 'cs.lg']",2109.14076v3.pdf," Large pre-trained language models have shown promise for few-shot learning, +completing text-based tasks given only a few task-specific examples. Will +models soon solve classification tasks that have so far been reserved for human +research assistants? Existing benchmarks are not designed to measure progress +in applied settings, and so don't directly answer this question. The RAFT +benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring +tasks and uses an evaluation setup that mirrors deployment. Baseline +evaluations on RAFT reveal areas current techniques struggle with: reasoning +over long texts and tasks with many classes. Human baselines show that some +classification tasks are difficult for non-expert humans, reflecting that +real-world value sometimes depends on domain expertise. Yet even non-expert +human baseline F1 scores exceed GPT-3 by an average of 0.11. The RAFT datasets +and leaderboard will track which model improvements translate into real-world +benefits at https://raft.elicit.org . +" +LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5,Chengwei Qin,http://arxiv.org/pdf/2110.07298v3.pdf,2021-10-14,['cs.cl'],2110.07298v3.pdf," Existing approaches to lifelong language learning rely on plenty of labeled +data for learning a new task, which is hard to obtain in most real scenarios. +Considering that humans can continually learn new tasks from a handful of +examples, we expect the models also to be able to generalize well on new +few-shot tasks without forgetting the previous ones. In this work, we define +this more challenging yet practical problem as Lifelong Few-shot Language +Learning (LFLL) and propose a unified framework for it based on prompt tuning +of T5. Our framework called LFPT5 takes full advantage of PT's strong few-shot +learning ability, and simultaneously trains the model as a task solver and a +data generator. Before learning a new domain of the same task type, LFPT5 +generates pseudo (labeled) samples of previously learned domains, and later +gets trained on those samples to alleviate forgetting of previous knowledge as +it learns the new domain. In addition, a KL divergence loss is minimized to +achieve label consistency between the previous and the current model. While +adapting to a new task type, LFPT5 includes and tunes additional prompt +embeddings for the new task. With extensive experiments, we demonstrate that +LFPT5 can be applied to various different types of tasks and significantly +outperform previous methods in different LFLL settings. +" +MetaICL: Learning to Learn In Context,Sewon Min,http://arxiv.org/pdf/2110.15943v2.pdf,2021-10-29,"['cs.cl', 'cs.ai']",2110.15943v2.pdf," We introduce MetaICL (Meta-training for In-Context Learning), a new +meta-training framework for few-shot learning where a pretrained language model +is tuned to do in-context learning on a large set of training tasks. This +meta-training enables the model to more effectively learn a new task in context +at test time, by simply conditioning on a few training examples with no +parameter updates or task-specific templates. We experiment on a large, diverse +collection of tasks consisting of 142 NLP datasets including classification, +question answering, natural language inference, paraphrase detection and more, +across seven different meta-training/target splits. MetaICL outperforms a range +of baselines including in-context learning without meta-training and multi-task +learning followed by zero-shot transfer. We find that the gains are +particularly significant for target tasks that have domain shifts from the +meta-training tasks, and that using a diverse set of the meta-training tasks is +key to improvements. We also show that MetaICL approaches (and sometimes beats) +the performance of models fully finetuned on the target task, and outperforms +much bigger models with nearly 8x parameters. Finally, we show that MetaICL is +complementary to human-written instructions, and the best performance can be +achieved by combining both approaches. +" +Scaling ASR Improves Zero and Few Shot Learning,Alex Xiao,http://arxiv.org/pdf/2111.05948v3.pdf,2021-11-10,"['cs.cl', 'cs.sd', 'eess.as']",2111.05948v3.pdf," With 4.5 million hours of English speech from 10 different sources across 120 +countries and models of up to 10 billion parameters, we explore the frontiers +of scale for automatic speech recognition. We propose data selection techniques +to efficiently scale training data to find the most valuable samples in massive +datasets. To efficiently scale model sizes, we leverage various optimizations +such as sparse transducer loss and model sharding. By training 1-10B parameter +universal English ASR models, we push the limits of speech recognition +performance across many domains. Furthermore, our models learn powerful speech +representations with zero and few-shot capabilities on novel domains and styles +of speech, exceeding previous results across multiple in-house and public +benchmarks. For speakers with disorders due to brain damage, our best zero-shot +and few-shot models achieve 22% and 60% relative improvement on the AphasiaBank +test set, respectively, while realizing the best performance on public social +media videos. Furthermore, the same universal model reaches equivalent +performance with 500x less in-domain data on the SPGISpeech financial-domain +dataset. +" +PointCLIP: Point Cloud Understanding by CLIP,Renrui Zhang,http://arxiv.org/pdf/2112.02413v1.pdf,2021-12-04,"['cs.cv', 'cs.ai', 'cs.ro']",2112.02413v1.pdf," Recently, zero-shot and few-shot learning via Contrastive Vision-Language +Pre-training (CLIP) have shown inspirational performance on 2D visual +recognition, which learns to match images with their corresponding texts in +open-vocabulary settings. However, it remains under explored that whether CLIP, +pre-trained by large-scale image-text pairs in 2D, can be generalized to 3D +recognition. In this paper, we identify such a setting is feasible by proposing +PointCLIP, which conducts alignment between CLIP-encoded point cloud and 3D +category texts. Specifically, we encode a point cloud by projecting it into +multi-view depth maps without rendering, and aggregate the view-wise zero-shot +prediction to achieve knowledge transfer from 2D to 3D. On top of that, we +design an inter-view adapter to better extract the global feature and +adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in +2D. By just fine-tuning the lightweight adapter in the few-shot settings, the +performance of PointCLIP could be largely improved. In addition, we observe the +complementary property between PointCLIP and classical 3D-supervised networks. +By simple ensembling, PointCLIP boosts baseline's performance and even +surpasses state-of-the-art models. Therefore, PointCLIP is a promising +alternative for effective 3D point cloud understanding via CLIP under low +resource cost and data regime. We conduct thorough experiments on +widely-adopted ModelNet10, ModelNet40 and the challenging ScanObjectNN to +demonstrate the effectiveness of PointCLIP. The code is released at +https://github.com/ZrrSkywalker/PointCLIP. +" +A Survey of Deep Learning for Low-Shot Object Detection,Qihan Huang,http://arxiv.org/pdf/2112.02814v4.pdf,2021-12-06,"['cs.cv', 'cs.ai']",2112.02814v4.pdf," Object detection has achieved a huge breakthrough with deep neural networks +and massive annotated data. However, current detection methods cannot be +directly transferred to the scenario where the annotated data is scarce due to +the severe overfitting problem. Although few-shot learning and zero-shot +learning have been extensively explored in the field of image classification, +it is indispensable to design new methods for object detection in the +data-scarce scenario since object detection has an additional challenging +localization task. Low-Shot Object Detection (LSOD) is an emerging research +topic of detecting objects from a few or even no annotated samples, consisting +of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and +Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review +of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and +analyze them systematically, comprising some extensional topics of LSOD +(semi-supervised LSOD, weakly-supervised LSOD, and incremental LSOD). Then, we +indicate the pros and cons of current LSOD methods with a comparison of their +performance. Finally, we discuss the challenges and promising directions of +LSOD to provide guidance for future works. +" +"Vision-Language Intelligence: Tasks, Representation Learning, and Large Models",Feng Li,http://arxiv.org/pdf/2203.01922v1.pdf,2022-03-03,"['cs.cv', 'cs.ai', 'cs.cl']",2203.01922v1.pdf," This paper presents a comprehensive survey of vision-language (VL) +intelligence from the perspective of time. This survey is inspired by the +remarkable progress in both computer vision and natural language processing, +and recent trends shifting from single modality processing to multiple modality +comprehension. We summarize the development in this field into three time +periods, namely task-specific methods, vision-language pre-training (VLP) +methods, and larger models empowered by large-scale weakly-labeled data. We +first take some common VL tasks as examples to introduce the development of +task-specific methods. Then we focus on VLP methods and comprehensively review +key components of the model structures and training methods. After that, we +show how recent work utilizes large-scale raw image-text data to learn +language-aligned visual representations that generalize better on zero or few +shot learning tasks. Finally, we discuss some potential future trends towards +modality cooperation, unified representation, and knowledge incorporation. We +believe that this review will be of help for researchers and practitioners of +AI and ML, especially those interested in computer vision and natural language +processing. +" +Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning,Zhenhailong Wang,http://arxiv.org/pdf/2203.04904v3.pdf,2022-03-09,"['cs.mm', 'cs.cl', 'cs.cv']",2203.04904v3.pdf," Despite achieving state-of-the-art zero-shot performance, existing +vision-language models still fall short of few-shot transfer ability on +domain-specific problems. Classical fine-tuning often fails to prevent highly +expressive models from exploiting spurious correlations. Although +model-agnostic meta-learning (MAML) presents as a natural alternative for +few-shot transfer learning, the expensive computation due to implicit +second-order optimization limits its use on large-scale vision-language models +such as CLIP. While much literature has been devoted to exploring alternative +optimization strategies, we identify another essential aspect towards effective +few-shot transfer learning, task sampling, which is previously only be viewed +as part of data pre-processing in MAML. To show the impact of task sampling, we +propose a simple algorithm, Model-Agnostic Multitask Fine-tuning (MAMF), which +differentiates classical fine-tuning only on uniformly sampling multiple tasks. +Despite its simplicity, we show that MAMF consistently outperforms classical +fine-tuning on five few-shot vision-language classification tasks. We further +show that the effectiveness of the bi-level optimization in MAML is highly +sensitive to the zero-shot performance of a task in the context of few-shot +vision-language classification. The goal of this paper is to provide new +insights on what makes few-shot learning work, and encourage more research into +investigating better task sampling strategies. +" +mGPT: Few-Shot Learners Go Multilingual,Oleh Shliazhko,http://arxiv.org/pdf/2204.07580v2.pdf,2022-04-15,"['cs.cl', 'cs.ai', '68-06, 68-04, 68t50, 68t01', 'i.2; i.2.7']",2204.07580v2.pdf," Recent studies report that autoregressive language models can successfully +solve many NLP tasks via zero- and few-shot learning paradigms, which opens up +new possibilities for using the pre-trained language models. This paper +introduces two autoregressive GPT-like models with 1.3 billion and 13 billion +parameters trained on 60 languages from 25 language families using Wikipedia +and Colossal Clean Crawled Corpus. We reproduce the GPT-3 architecture using +GPT-2 sources and the sparse attention mechanism; Deepspeed and Megatron +frameworks allow us to parallelize the training and inference steps +effectively. The resulting models show performance on par with the recently +released XGLM models by Facebook, covering more languages and enhancing NLP +possibilities for low resource languages of CIS countries and Russian small +nations. We detail the motivation for the choices of the architecture design, +thoroughly describe the data preparation pipeline, and train five small +versions of the model to choose the most optimal multilingual tokenization +strategy. We measure the model perplexity in all covered languages and evaluate +it on the wide spectre of multilingual tasks, including classification, +generative, sequence labeling and knowledge probing. The models were evaluated +with the zero-shot and few-shot methods. Furthermore, we compared the +classification tasks with the state-of-the-art multilingual model XGLM. source +code and the mGPT XL model are publicly released. +" +In-BoXBART: Get Instructions into Biomedical Multi-Task Learning,Mihir Parmar,http://arxiv.org/pdf/2204.07600v1.pdf,2022-04-15,['cs.cl'],2204.07600v1.pdf," Single-task models have proven pivotal in solving specific tasks; however, +they have limitations in real-world applications where multi-tasking is +necessary and domain shifts are exhibited. Recently, instructional prompts have +shown significant improvement towards multi-task generalization; however, the +effect of instructional prompts and Multi-Task Learning (MTL) has not been +systematically studied in the biomedical domain. Motivated by this, this paper +explores the impact of instructional prompts for biomedical MTL. We introduce +the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X) +various categories. Using this meta-dataset, we propose a unified model termed +In-BoXBART, that can jointly learn all tasks of the BoX without any +task-specific modules. To the best of our knowledge, this is the first attempt +to propose a unified model in the biomedical domain and use instructions to +achieve generalization across several biomedical tasks. Experimental results +indicate that the proposed model: 1) outperforms the single-task baseline by +~3% and multi-task (without instruction) baseline by ~18% on an average, and 2) +shows ~23% improvement compared to the single-task baseline in few-shot +learning (i.e., 32 instances per task) on an average. Our analysis indicates +that there is significant room for improvement across tasks in the BoX, +implying the scope for future research direction. +" +OPT: Open Pre-trained Transformer Language Models,Susan Zhang,http://arxiv.org/pdf/2205.01068v4.pdf,2022-05-02,"['cs.cl', 'cs.lg']",2205.01068v4.pdf," Large language models, which are often trained for hundreds of thousands of +compute days, have shown remarkable capabilities for zero- and few-shot +learning. Given their computational cost, these models are difficult to +replicate without significant capital. For the few that are available through +APIs, no access is granted to the full model weights, making them difficult to +study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only +pre-trained transformers ranging from 125M to 175B parameters, which we aim to +fully and responsibly share with interested researchers. We show that OPT-175B +is comparable to GPT-3, while requiring only 1/7th the carbon footprint to +develop. We are also releasing our logbook detailing the infrastructure +challenges we faced, along with code for experimenting with all of the released +models. +" +Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning,Xiang Chen,http://arxiv.org/pdf/2205.02355v2.pdf,2022-05-04,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2205.02355v2.pdf," Pre-trained language models have contributed significantly to relation +extraction by demonstrating remarkable few-shot learning abilities. However, +prompt tuning methods for relation extraction may still fail to generalize to +those rare or hard patterns. Note that the previous parametric learning +paradigm can be viewed as memorization regarding training data as a book and +inference as the close-book test. Those long-tailed or hard patterns can hardly +be memorized in parameters given few-shot instances. To this end, we regard RE +as an open-book examination and propose a new semiparametric paradigm of +retrieval-enhanced prompt tuning for relation extraction. We construct an +open-book datastore for retrieval regarding prompt-based instance +representations and corresponding relation labels as memorized key-value pairs. +During inference, the model can infer relations by linearly interpolating the +base output of PLM with the non-parametric nearest neighbor distribution over +the datastore. In this way, our model not only infers relation through +knowledge stored in the weights during training but also assists +decision-making by unwinding and querying examples in the open-book datastore. +Extensive experiments on benchmark datasets show that our method can achieve +state-of-the-art in both standard supervised and few-shot settings. Code are +available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE. +" +Towards Unified Prompt Tuning for Few-shot Text Classification,Jianing Wang,http://arxiv.org/pdf/2205.05313v1.pdf,2022-05-11,"['cs.cl', 'cs.ai']",2205.05313v1.pdf," Prompt-based fine-tuning has boosted the performance of Pre-trained Language +Models (PLMs) on few-shot text classification by employing task-specific +prompts. Yet, PLMs are unfamiliar with prompt-style expressions during +pre-training, which limits the few-shot learning performance on downstream +tasks. It would be desirable if the models can acquire some prompting knowledge +before adaptation to specific NLP tasks. We present the Unified Prompt Tuning +(UPT) framework, leading to better few-shot text classification for BERT-style +models by explicitly capturing prompting semantics from non-target NLP +datasets. In UPT, a novel paradigm Prompt-Options-Verbalizer is proposed for +joint prompt learning across different NLP tasks, forcing PLMs to capture +task-invariant prompting knowledge. We further design a self-supervised task +named Knowledge-enhanced Selective Masked Language Modeling to improve the +PLM's generalization abilities for accurate adaptation to previously unseen +tasks. After multi-task learning across multiple tasks, the PLM can be better +prompt-tuned towards any dissimilar target tasks in low-resourced settings. +Experiments over a variety of NLP tasks show that UPT consistently outperforms +state-of-the-arts for prompt-based fine-tuning. +" +Towards Answering Open-ended Ethical Quandary Questions,Yejin Bang,http://arxiv.org/pdf/2205.05989v3.pdf,2022-05-12,"['cs.cl', 'cs.ai', 'cs.lg']",2205.05989v3.pdf," Considerable advancements have been made in various NLP tasks based on the +impressive power of large language models (LLMs) and many NLP applications are +deployed in our daily lives. In this work, we challenge the capability of LLMs +with the new task of Ethical Quandary Generative Question Answering. Ethical +quandary questions are more challenging to address because multiple conflicting +answers may exist to a single quandary. We explore the current capability of +LLMs in providing an answer with a deliberative exchange of different +perspectives to an ethical quandary, in the approach of Socratic philosophy, +instead of providing a closed answer like an oracle. We propose a model that +searches for different ethical principles applicable to the ethical quandary +and generates an answer conditioned on the chosen principles through +prompt-based few-shot learning. We also discuss the remaining challenges and +ethical issues involved in this task and suggest the direction toward +developing responsible NLP systems by incorporating human values explicitly. +" +PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners,Canyu Chen,http://arxiv.org/pdf/2205.09229v3.pdf,2022-05-18,"['cs.cl', 'cs.ai']",2205.09229v3.pdf," Recent advances in large pre-trained language models (PLMs) lead to +impressive gains in natural language understanding (NLU) tasks with +task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on +sufficient labeled training instances, which are usually hard to obtain. +Prompt-based tuning on PLMs has shown to be powerful for various downstream +few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU +tasks mainly focus on deriving proper label words with a verbalizer or +generating prompt templates to elicit semantics from PLMs. In addition, +conventional data augmentation strategies such as synonym substitution, though +widely adopted in low-resource scenarios, only bring marginal improvements for +prompt-based few-shot learning. Thus, an important research question arises: +how to design effective data augmentation methods for prompt-based few-shot +tuning? To this end, considering the label semantics are essential in +prompt-based tuning, we propose a novel label-guided data augmentation +framework PromptDA, which exploits the enriched label semantic information for +data augmentation. Extensive experiment results on few-shot text classification +tasks demonstrate the superior performance of the proposed framework by +effectively leveraging label semantics and data augmentation for natural +language understanding. Our code is available at +https://github.com/canyuchen/PromptDA. +" +What Makes Data-to-Text Generation Hard for Pretrained Language Models?,Moniba Keymanesh,http://arxiv.org/pdf/2205.11505v1.pdf,2022-05-23,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2205.11505v1.pdf," Expressing natural language descriptions of structured facts or relations -- +data-to-text generation (D2T) -- increases the accessibility of structured +knowledge repositories. Previous work shows that pre-trained language +models(PLMs) perform remarkably well on this task after fine-tuning on a +significant amount of task-specific training data. On the other hand, while +auto-regressive PLMs can generalize from a few task examples, their efficacy at +D2T is largely unexplored. Furthermore, we have an incomplete understanding of +the limits of PLMs on D2T. + In this work, we conduct an empirical study of both fine-tuned and +auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their +performance as a function of the amount of task-specific data and how these +data are incorporated into the models: zero and few-shot learning, and +fine-tuning of model weights. In addition, we probe the limits of PLMs by +measuring performance on subsets of the evaluation data: novel predicates and +abstractive test examples. To improve the performance on these subsets, we +investigate two techniques: providing predicate descriptions in the context and +re-ranking generated candidates by information reflected in the source. +Finally, we conduct a human evaluation of model errors and show that D2T +generation tasks would benefit from datasets with more careful manual curation. +" +ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts,Akari Asai,http://arxiv.org/pdf/2205.11961v2.pdf,2022-05-24,['cs.cl'],2205.11961v2.pdf," This work introduces a new multi-task, parameter-efficient language model +(LM) tuning method that learns to transfer knowledge across different tasks via +a mixture of soft prompts-small prefix embedding vectors pre-trained for +different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt +Tuning), obtains source prompts as encodings of large-scale source tasks into a +small number of parameters and trains an attention module to interpolate the +source prompts and a newly initialized target prompt for every instance in the +target task. During training, only the target task prompt and the attention +weights, which are shared between tasks in multi-task training, are updated, +while the original LM and source prompts are intact. ATTEMPT is highly +parameter-efficient (e.g., updates 2,300 times fewer parameters than full +fine-tuning) while achieving high task performance using knowledge from +high-resource tasks. Moreover, it is modular using pre-trained soft prompts, +and can flexibly add or remove source prompts for effective knowledge transfer. +Our experimental results across 21 diverse NLP datasets show that ATTEMPT +significantly outperforms prompt tuning and outperforms or matches fully +fine-tuned or other parameter-efficient tuning approaches that use over ten +times more parameters. Finally, ATTEMPT outperforms previous work in few-shot +learning settings. +" +Making Large Language Models Better Reasoners with Step-Aware Verifier,Yifei Li,http://arxiv.org/pdf/2206.02336v3.pdf,2022-06-06,"['cs.cl', 'cs.ai']",2206.02336v3.pdf," Few-shot learning is a challenging task that requires language models to +generalize from limited examples. Large language models like GPT-3 and PaLM +have made impressive progress in this area, but they still face difficulties in +reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve +their reasoning skills, previous work has proposed to guide the language model +with prompts that elicit a series of reasoning steps before giving the final +answer, achieving a significant improvement on GSM8K from 17.9% to 58.1% in +problem-solving rate. In this paper, we present DIVERSE (Diverse Verifier on +Reasoning Step), a novel approach that further enhances the reasoning +capability of language models. DIVERSE has three main components: first, it +generates diverse prompts to explore different reasoning paths for the same +question; second, it uses a verifier to filter out incorrect answers based on a +weighted voting scheme; and third, it verifies each reasoning step individually +instead of the whole chain. We evaluate DIVERSE on the latest language model +code-davinci-002 and show that it achieves new state-of-the-art results on six +of eight reasoning benchmarks (e.g., GSM8K 74.4% to 83.2%). +" +Language Models are General-Purpose Interfaces,Yaru Hao,http://arxiv.org/pdf/2206.06336v1.pdf,2022-06-13,['cs.cl'],2206.06336v1.pdf," Foundation models have received much attention due to their effectiveness +across a broad range of downstream applications. Though there is a big +convergence in terms of architecture, most pretrained models are typically +still developed for specific tasks or modalities. In this work, we propose to +use language models as a general-purpose interface to various foundation +models. A collection of pretrained encoders perceive diverse modalities (such +as vision, and language), and they dock with a language model that plays the +role of a universal task layer. We propose a semi-causal language modeling +objective to jointly pretrain the interface and the modular encoders. We +subsume the advantages and capabilities from both causal and non-causal +modeling, thereby combining the best of two worlds. Specifically, the proposed +method not only inherits the capabilities of in-context learning and open-ended +generation from causal language modeling, but also is conducive to finetuning +because of the bidirectional encoders. More importantly, our approach +seamlessly unlocks the combinations of the above capabilities, e.g., enabling +in-context learning or instruction following with finetuned encoders. +Experimental results across various language-only and vision-language +benchmarks show that our model outperforms or is competitive with specialized +models on finetuning, zero-shot generalization, and few-shot learning. +" +FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification,Aliaksandra Shysheya,http://arxiv.org/pdf/2206.08671v2.pdf,2022-06-17,"['stat.ml', 'cs.cv', 'cs.lg']",2206.08671v2.pdf," Modern deep learning systems are increasingly deployed in situations such as +personalization and federated learning where it is necessary to support i) +learning on small amounts of data, and ii) communication efficient distributed +training protocols. In this work, we develop FiLM Transfer (FiT) which fulfills +these requirements in the image classification setting by combining ideas from +transfer learning (fixed pretrained backbones and fine-tuned FiLM adapter +layers) and meta-learning (automatically configured Naive Bayes classifiers and +episodic training) to yield parameter efficient models with superior +classification accuracy at low-shot. The resulting parameter efficiency is key +for enabling few-shot learning, inexpensive model updates for personalization, +and communication efficient federated learning. We experiment with FiT on a +wide range of downstream datasets and show that it achieves better +classification accuracy than the leading Big Transfer (BiT) algorithm at +low-shot and achieves state-of-the art accuracy on the challenging VTAB-1k +benchmark, with fewer than 1% of the updateable parameters. Finally, we +demonstrate the parameter efficiency and superior accuracy of FiT in +distributed low-shot applications including model personalization and federated +learning where model update size is an important performance metric. +" +A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots,Shaokang Cai,http://arxiv.org/pdf/2207.10569v1.pdf,2022-07-13,['cs.cl'],2207.10569v1.pdf," The rapid development of artificial intelligence (AI) technology has enabled +large-scale AI applications to land in the market and practice. However, while +AI technology has brought many conveniences to people in the productization +process, it has also exposed many security issues. Especially, attacks against +online learning vulnerabilities of chatbots occur frequently. Therefore, this +paper proposes a semantics censorship chatbot system based on reinforcement +learning, which is mainly composed of two parts: the Offensive semantics +censorship model and the semantics purification model. Offensive semantics +review can combine the context of user input sentences to detect the rapid +evolution of Offensive semantics and respond to Offensive semantics responses. +The semantics purification model For the case of chatting robot models, it has +been contaminated by large numbers of offensive semantics, by strengthening the +offensive reply learned by the learning algorithm, rather than rolling back to +the early versions. In addition, by integrating a once-through learning +approach, the speed of semantics purification is accelerated while reducing the +impact on the quality of replies. The experimental results show that our +proposed approach reduces the probability of the chat model generating +offensive replies and that the integration of the few-shot learning algorithm +improves the training speed rapidly while effectively slowing down the decline +in BLEU values. +" +AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model,Saleh Soltan,http://arxiv.org/pdf/2208.01448v2.pdf,2022-08-02,"['cs.cl', 'cs.lg']",2208.01448v2.pdf," In this work, we demonstrate that multilingual large-scale +sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising +and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners +than decoder-only models on various tasks. In particular, we train a 20 billion +parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B) +and show that it achieves state-of-the-art (SOTA) performance on 1-shot +summarization tasks, outperforming a much larger 540B PaLM decoder model. +AlexaTM 20B also achieves SOTA in 1-shot machine translation, especially for +low-resource languages, across almost all language pairs supported by the model +(Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, +Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset. We also show in +zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2 +datasets and provides SOTA performance on multilingual tasks such as XNLI, +XCOPA, Paws-X, and XWinograd. Overall, our results present a compelling case +for seq2seq models as a powerful alternative to decoder-only models for +Large-scale Language Model (LLM) training. +" +Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction,Jun Zhang,http://arxiv.org/pdf/2208.09652v2.pdf,2022-08-20,"['cs.lg', 'cs.ai', 'physics.bio-ph']",2208.09652v2.pdf," Data-driven predictive methods which can efficiently and accurately transform +protein sequences into biologically active structures are highly valuable for +scientific research and medical development. Determining accurate folding +landscape using co-evolutionary information is fundamental to the success of +modern protein structure prediction methods. As the state of the art, +AlphaFold2 has dramatically raised the accuracy without performing explicit +co-evolutionary analysis. Nevertheless, its performance still shows strong +dependence on available sequence homologs. Based on the interrogation on the +cause of such dependence, we presented EvoGen, a meta generative model, to +remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting +the model with calibrated or virtually generated homologue sequences, EvoGen +helps AlphaFold2 fold accurately in low-data regime and even achieve +encouraging performance with single-sequence predictions. Being able to make +accurate predictions with few-shot MSA not only generalizes AlphaFold2 better +for orphan sequences, but also democratizes its use for high-throughput +applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic +structure generation method which could explore alternative conformations of +protein sequences, and the task-aware differentiable algorithm for sequence +generation will benefit other related tasks including protein design. +" +Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective,Jiangmeng Li,http://arxiv.org/pdf/2208.12681v2.pdf,2022-08-26,['cs.cv'],2208.12681v2.pdf," Few-shot learning models learn representations with limited human +annotations, and such a learning paradigm demonstrates practicability in +various tasks, e.g., image classification, object detection, etc. However, +few-shot object detection methods suffer from an intrinsic defect that the +limited training data makes the model cannot sufficiently explore semantic +information. To tackle this, we introduce knowledge distillation to the +few-shot object detection learning paradigm. We further run a motivating +experiment, which demonstrates that in the process of knowledge distillation, +the empirical error of the teacher model degenerates the prediction performance +of the few-shot object detection model as the student. To understand the +reasons behind this phenomenon, we revisit the learning paradigm of knowledge +distillation on the few-shot object detection task from the causal theoretic +standpoint, and accordingly, develop a Structural Causal Model. Following the +theoretical guidance, we propose a backdoor adjustment-based knowledge +distillation method for the few-shot object detection task, namely Disentangle +and Remerge (D&R), to perform conditional causal intervention toward the +corresponding Structural Causal Model. Empirically, the experiments on +benchmarks demonstrate that D&R can yield significant performance boosts in +few-shot object detection. Code is available at +https://github.com/ZYN-1101/DandR.git. +" +NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results,Dustin Carrión-Ojeda,http://arxiv.org/pdf/2208.14686v1.pdf,2022-08-31,"['cs.lg', 'cs.ai', 'cs.cv', 'cs.ne']",2208.14686v1.pdf," We present the design and baseline results for a new challenge in the +ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on +""cross-domain"" meta-learning. Meta-learning aims to leverage experience gained +from previous tasks to solve new tasks efficiently (i.e., with better +performance, little training data, and/or modest computational resources). +While previous challenges in the series focused on within-domain few-shot +learning problems, with the aim of learning efficiently N-way k-shot tasks +(i.e., N class classification problems with k training examples), this +competition challenges the participants to solve ""any-way"" and ""any-shot"" +problems drawn from various domains (healthcare, ecology, biology, +manufacturing, and others), chosen for their humanitarian and societal impact. +To that end, we created Meta-Album, a meta-dataset of 40 image classification +datasets from 10 domains, from which we carve out tasks with any number of +""ways"" (within the range 2-20) and any number of ""shots"" (within the range +1-20). The competition is with code submission, fully blind-tested on the +CodaLab challenge platform. The code of the winners will be open-sourced, +enabling the deployment of automated machine learning solutions for few-shot +image classification across several domains. +" +Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models,Zichun Yu,http://arxiv.org/pdf/2209.09401v1.pdf,2022-09-20,"['cs.cl', 'cs.lg']",2209.09401v1.pdf," Prompting, which casts downstream applications as language modeling tasks, +has shown to be sample efficient compared to standard fine-tuning with +pre-trained models. However, one pitfall of prompting is the need of +manually-designed patterns, whose outcome can be unintuitive and requires large +validation sets to tune. To tackle the challenge, we propose AutoSeq, a fully +automatic prompting method: (1) We adopt natural language prompts on +sequence-to-sequence models, enabling free-form generation and larger label +search space; (2) We propose label sequences -- phrases with indefinite lengths +to verbalize the labels -- which eliminate the need of manual templates and are +more expressive than single label words; (3) We use beam search to +automatically generate a large amount of label sequence candidates and propose +contrastive re-ranking to get the best combinations. AutoSeq significantly +outperforms other no-manual-design methods, such as soft prompt tuning, adapter +tuning, and automatic search on single label words; the generated label +sequences are even better than curated manual ones on a variety of tasks. Our +method reveals the potential of sequence-to-sequence models in few-shot +learning and sheds light on a path to generic and automatic prompting. The +source code of this paper can be obtained from +https://github.com/thunlp/Seq2Seq-Prompt. +" +Collaboration of Pre-trained Models Makes Better Few-shot Learner,Renrui Zhang,http://arxiv.org/pdf/2209.12255v2.pdf,2022-09-25,['cs.cv'],2209.12255v2.pdf," Few-shot classification requires deep neural networks to learn generalized +representations only from limited training images, which is challenging but +significant in low-data regimes. Recently, CLIP-based methods have shown +promising few-shot performance benefited from the contrastive language-image +pre-training. Based on this point, we question if the large-scale pre-training +can alleviate the few-shot data deficiency and also assist the representation +learning by the pre-learned knowledge. In this paper, we propose CoMo, a +Collaboration of pre-trained Models that incorporates diverse prior knowledge +from various pre-training paradigms for better few-shot learning. Our CoMo +includes: CLIP's language-contrastive knowledge, DINO's vision-contrastive +knowledge, and DALL-E's language-generative knowledge. Specifically, CoMo works +in two aspects: few-shot data expansion and diverse knowledge ensemble. For +one, we generate synthetic images via zero-shot DALL-E to enrich the few-shot +training data without any manpower. For the other, we introduce a learnable +Multi-Knowledge Adapter (MK-Adapter) to adaptively blend the predictions from +CLIP and DINO. By such collaboration, CoMo can fully unleash the potential of +different pre-training methods and unify them to perform state-of-the-art for +few-shot classification. We conduct extensive experiments on 11 datasets to +demonstrate the superiority and generalization ability of our approach. +" +CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training,Tianyu Huang,http://arxiv.org/pdf/2210.01055v3.pdf,2022-10-03,['cs.cv'],2210.01055v3.pdf," Pre-training across 3D vision and language remains under development because +of limited training data. Recent works attempt to transfer vision-language +pre-training models to 3D vision. PointCLIP converts point cloud data to +multi-view depth maps, adopting CLIP for shape classification. However, its +performance is restricted by the domain gap between rendered depth maps and +images, as well as the diversity of depth distributions. To address this issue, +we propose CLIP2Point, an image-depth pre-training method by contrastive +learning to transfer CLIP to the 3D domain, and adapt it to point cloud +classification. We introduce a new depth rendering setting that forms a better +visual effect, and then render 52,460 pairs of images and depth maps from +ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines +cross-modality learning to enforce the depth features for capturing expressive +visual and textual features and intra-modality learning to enhance the +invariance of depth aggregation. Additionally, we propose a novel Dual-Path +Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for +few-shot learning. The dual-path structure allows the joint use of CLIP and +CLIP2Point, and the simplified adapter can well fit few-shot tasks without +post-search. Experimental results show that CLIP2Point is effective in +transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP +and other self-supervised 3D networks, achieving state-of-the-art results on +zero-shot and few-shot classification. +" +Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis,Siddharth Varia,http://arxiv.org/pdf/2210.06629v2.pdf,2022-10-12,['cs.cl'],2210.06629v2.pdf," Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis +task which involves four elements from user-generated texts: aspect term, +aspect category, opinion term, and sentiment polarity. Most computational +approaches focus on some of the ABSA sub-tasks such as tuple (aspect term, +sentiment polarity) or triplet (aspect term, opinion term, sentiment polarity) +extraction using either pipeline or joint modeling approaches. Recently, +generative approaches have been proposed to extract all four elements as (one +or more) quadruplets from text as a single task. In this work, we take a step +further and propose a unified framework for solving ABSA, and the associated +sub-tasks to improve the performance in few-shot scenarios. To this end, we +fine-tune a T5 model with instructional prompts in a multi-task learning +fashion covering all the sub-tasks, as well as the entire quadruple prediction +task. In experiments with multiple benchmark datasets, we show that the +proposed multi-task prompting approach brings performance boost (by absolute +8.29 F1) in the few-shot learning setting. +" +"RARR: Researching and Revising What Language Models Say, Using Language Models",Luyu Gao,http://arxiv.org/pdf/2210.08726v3.pdf,2022-10-17,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2210.08726v3.pdf," Language models (LMs) now excel at many tasks such as few-shot learning, +question answering, reasoning, and dialog. However, they sometimes generate +unsupported or misleading content. A user cannot easily determine whether their +outputs are trustworthy or not, because most LMs do not have any built-in +mechanism for attribution to external evidence. To enable attribution while +still preserving all the powerful advantages of recent generation models, we +propose RARR (Retrofit Attribution using Research and Revision), a system that +1) automatically finds attribution for the output of any text generation model +and 2) post-edits the output to fix unsupported content while preserving the +original output as much as possible. When applied to the output of several +state-of-the-art LMs on a diverse set of generation tasks, we find that RARR +significantly improves attribution while otherwise preserving the original +input to a much greater degree than previously explored edit models. +Furthermore, the implementation of RARR requires only a handful of training +examples, a large language model, and standard web search. +" +TAPE: Assessing Few-shot Russian Language Understanding,Ekaterina Taktasheva,http://arxiv.org/pdf/2210.12813v1.pdf,2022-10-23,['cs.cl'],2210.12813v1.pdf," Recent advances in zero-shot and few-shot learning have shown promise for a +scope of research and practical purposes. However, this fast-growing area lacks +standardized evaluation suites for non-English languages, hindering progress +outside the Anglo-centric paradigm. To address this line of research, we +propose TAPE (Text Attack and Perturbation Evaluation), a novel benchmark that +includes six more complex NLU tasks for Russian, covering multi-hop reasoning, +ethical concepts, logic and commonsense knowledge. The TAPE's design focuses on +systematic zero-shot and few-shot NLU evaluation: (i) linguistic-oriented +adversarial attacks and perturbations for analyzing robustness, and (ii) +subpopulations for nuanced interpretation. The detailed analysis of testing the +autoregressive baselines indicates that simple spelling-based perturbations +affect the performance the most, while paraphrasing the input has a more +negligible effect. At the same time, the results demonstrate a significant gap +between the neural and human baselines for most tasks. We publicly release TAPE +(tape-benchmark.com) to foster research on robust LMs that can generalize to +new tasks when little to no supervision is available. +" +Learning New Tasks from a Few Examples with Soft-Label Prototypes,Avyav Kumar Singh,http://arxiv.org/pdf/2210.17437v2.pdf,2022-10-31,"['cs.lg', 'cs.cl']",2210.17437v2.pdf," It has been experimentally demonstrated that humans are able to learn in a +manner that allows them to make predictions on categories for which they have +not seen any examples (Malaviya et al., 2022). Sucholutsky and Schonlau (2020) +have recently presented a machine learning approach that aims to do the same. +They utilise synthetically generated data and demonstrate that it is possible +to achieve sub-linear scaling and develop models that can learn to recognise N +classes from M training samples where M is less than N - aka less-than-one shot +learning. Their method was, however, defined for univariate or simple +multivariate data (Sucholutsky et al., 2021). We extend it to work on large, +high-dimensional and real-world datasets and empirically validate it in this +new and challenging setting. We apply this method to learn previously unseen +NLP tasks from very few examples (4, 8 or 16). We first generate compact, +sophisticated less-than-one shot representations called soft-label prototypes +which are fitted on training data, capturing the distribution of different +classes across the input domain space. We then use a modified k-Nearest +Neighbours classifier to demonstrate that soft-label prototypes can classify +data competitively, even outperforming much more computationally complex +few-shot learning methods. +" +QAmeleon: Multilingual QA with Only 5 Examples,Priyanka Agrawal,http://arxiv.org/pdf/2211.08264v2.pdf,2022-11-15,['cs.cl'],2211.08264v2.pdf," The availability of large, high-quality datasets has been one of the main +drivers of recent progress in question answering (QA). Such annotated datasets +however are difficult and costly to collect, and rarely exist in languages +other than English, rendering QA technology inaccessible to underrepresented +languages. An alternative to building large monolingual training datasets is to +leverage pre-trained language models (PLMs) under a few-shot learning setting. +Our approach, QAmeleon, uses a PLM to automatically generate multilingual data +upon which QA models are trained, thus avoiding costly annotation. Prompt +tuning the PLM for data synthesis with only five examples per language delivers +accuracy superior to translation-based baselines, bridges nearly 60% of the gap +between an English-only baseline and a fully supervised upper bound trained on +almost 50,000 hand labeled examples, and always leads to substantial +improvements compared to fine-tuning a QA model directly on labeled examples in +low resource settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show +that few-shot prompt tuning for data synthesis scales across languages and is a +viable alternative to large-scale annotation. +" +Explicit Knowledge Transfer for Weakly-Supervised Code Generation,Zhangir Azerbayev,http://arxiv.org/pdf/2211.16740v3.pdf,2022-11-30,['cs.cl'],2211.16740v3.pdf," Large language models (LLMs) can acquire strong code-generation capabilities +through few-shot learning. In contrast, supervised fine-tuning is still needed +for smaller models to achieve good performance. Such fine-tuning demands a +large number of task-specific NL-code pairs, which are expensive to obtain. In +this paper, we attempt to transfer the code generation ability of an LLM to a +smaller model with the aid of weakly-supervised data. More specifically, we +propose explicit knowledge transfer (EKT), which uses the few-shot capabilities +of a teacher LLM to create NL-code pairs that we then filter for correctness +and fine-tune the student on. We evaluate EKT on the task of generating code +solutions to math word problems from the GSM8k dataset. We find that EKT not +only yields better performance than training with expert iteration, but also +outperforms knowledge distillation, another form of knowledge transfer. A +GPT-Neo 1.3B model trained using EKT with a GPT-J teacher achieves a 12.4% +pass@100 on GSM8k, while the same student and teacher trained with knowledge +distillation yield only a 3.7% pass@100. We also show that it is possible for a +student model to outperform the teacher using EKT. +" +Can In-context Learners Learn a Reasoning Concept from Demonstrations?,Michal Štefánik,http://arxiv.org/pdf/2212.01692v4.pdf,2022-12-03,"['cs.cl', 'cs.ai', 'cs.lg']",2212.01692v4.pdf," Language models exhibit an emergent ability to learn a new task from a small +number of input-output demonstrations. However, recent work shows that +in-context learners largely rely on their pre-trained knowledge, such as the +sentiment of the labels, instead of learning new associations from the input. +We argue that the commonly-used few-shot evaluation using a random selection of +in-context demonstrations can not disentangle models' reliance on such biases, +as most of the randomly-selected demonstrations do not present relations +informative for prediction beyond exposing the task's input-output +distribution. + Therefore, to evaluate models' in-context learning ability independent of +models' memory, we introduce a Concept-sharing few-shot learning method +choosing the demonstrations that share an underlying concept with the predicted +sample. We extract a set of such concepts from available human explanations and +measure how much models can benefit from presenting these concepts in few-shot +demonstrations. + We find that most of the recent in-context learners can not consistently +benefit from the demonstrated concepts, irrespective of the model size. +However, we note that T0 models are more sensitive to exhibited concepts, +benefiting from concept-sharing demonstrations in 7 out of 8 evaluation +scenarios. +" +Frozen CLIP Model is An Efficient Point Cloud Backbone,Xiaoshui Huang,http://arxiv.org/pdf/2212.04098v2.pdf,2022-12-08,['cs.cv'],2212.04098v2.pdf," The pretraining-finetuning paradigm has demonstrated great success in NLP and +2D image fields because of the high-quality representation ability and +transferability of their pretrained models. However, pretraining such a strong +model is difficult in the 3D point cloud field since the training data is +limited and point cloud collection is expensive. This paper introduces +Efficient Point Cloud Learning (EPCL), an effective and efficient point cloud +learner for directly training high-quality point cloud models with a frozen +CLIP model. Our EPCL connects the 2D and 3D modalities by semantically aligning +the 2D features and point cloud features without paired 2D-3D data. +Specifically, the input point cloud is divided into a sequence of tokens and +directly fed into the frozen CLIP model to learn point cloud representation. +Furthermore, we design a task token to narrow the gap between 2D images and 3D +point clouds. Comprehensive experiments on 3D detection, semantic segmentation, +classification and few-shot learning demonstrate that the 2D CLIP model can be +an efficient point cloud backbone and our method achieves state-of-the-art +accuracy on both real-world and synthetic downstream tasks. Code will be +available. +" +Federated Few-Shot Learning for Mobile NLP,Dongqi Cai,http://arxiv.org/pdf/2212.05974v2.pdf,2022-12-12,"['cs.lg', 'cs.cl']",2212.05974v2.pdf," Natural language processing (NLP) sees rich mobile applications. To support +various language understanding tasks, a foundation NLP model is often +fine-tuned in a federated, privacy-preserving setting (FL). This process +currently relies on at least hundreds of thousands of labeled training samples +from mobile clients; yet mobile users often lack willingness or knowledge to +label their data. Such an inadequacy of data labels is known as a few-shot +scenario; it becomes the key blocker for mobile NLP applications. + For the first time, this work investigates federated NLP in the few-shot +scenario (FedFSL). By retrofitting algorithmic advances of pseudo labeling and +prompt learning, we first establish a training pipeline that delivers +competitive accuracy when only 0.05% (fewer than 100) of the training data is +labeled and the remaining is unlabeled. To instantiate the workflow, we further +present a system FeS, addressing the high execution cost with novel designs. +(1) Curriculum pacing, which injects pseudo labels to the training workflow at +a rate commensurate to the learning progress; (2) Representational diversity, a +mechanism for selecting the most learnable data, only for which pseudo labels +will be generated; (3) Co-planning of a model's training depth and layer +capacity. Together, these designs reduce the training delay, client energy, and +network traffic by up to 46.0$\times$, 41.2$\times$ and 3000.0$\times$, +respectively. Through algorithm/system co-design, FFNLP demonstrates that FL +can apply to challenging settings where most training samples are unlabeled. +" +FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks,Weilong Dong,http://arxiv.org/pdf/2212.08354v1.pdf,2022-12-16,['cs.cl'],2212.08354v1.pdf," Massively multi-task learning with large language models has recently made +substantial progress on few-shot generalization. However, this is usually +performed in a centralized learning fashion, ignoring the privacy sensitivity +issue of (annotated) data used in multiple tasks. To mitigate this issue, we +propose FewFedWeight, a few-shot federated learning framework across multiple +tasks, to achieve the best of both worlds: privacy preservation and cross-task +generalization. FewFedWeight trains client models in isolated devices without +sharing data. It broadcasts the global model in the server to each client and +produces pseudo data for clients so that knowledge from the global model can be +explored to enhance few-shot learning of each client model. An energy-based +algorithm is further proposed to weight pseudo samples in order to reduce the +negative impact of noise from the generated pseudo data. Adaptive model weights +of client models are also tuned according to their performance. We use these +model weights to dynamically aggregate client models to update the global +model. Experiments on 118 NLP tasks show that FewFedWeight can significantly +improve the performance of client models on 61% tasks with an average +performance improvement rate of 30.5% over the baseline and substantially +outperform FedAvg and other decentralized learning methods. +" +Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning,Chris Lengerich,http://arxiv.org/pdf/2212.11353v1.pdf,2022-12-21,"['cs.cl', 'cs.lg']",2212.11353v1.pdf," Traditional approaches to RL have focused on learning decision policies +directly from episodic decisions, while slowly and implicitly learning the +semantics of compositional representations needed for generalization. While +some approaches have been adopted to refine representations via auxiliary +self-supervised losses while simultaneously learning decision policies, +learning compositional representations from hand-designed and +context-independent self-supervised losses (multi-view) still adapts relatively +slowly to the real world, which contains many non-IID subspaces requiring rapid +distribution shift in both time and spatial attention patterns at varying +levels of abstraction. In contrast, supervised language model cascades have +shown the flexibility to adapt to many diverse manifolds, and hints of +self-learning needed for autonomous task transfer. However, to date, transfer +methods for language models like few-shot learning and fine-tuning still +require human supervision and transfer learning using self-learning methods has +been underexplored. We propose a self-supervised loss policy called contrastive +distillation which manifests latent variables with high mutual information with +both source and target tasks from weights to tokens. We show how this +outperforms common methods of transfer learning and suggests a useful design +axis of trading off compute for generalizability for online transfer. +Contrastive distillation is improved through sampling from memory and suggests +a simple algorithm for more efficiently sampling negative examples for +contrastive losses than random sampling. +" +Exploring Efficient Few-shot Adaptation for Vision Transformers,Chengming Xu,http://arxiv.org/pdf/2301.02419v1.pdf,2023-01-06,['cs.cv'],2301.02419v1.pdf," The task of Few-shot Learning (FSL) aims to do the inference on novel +categories containing only few labeled examples, with the help of knowledge +learned from base categories containing abundant labeled training samples. +While there are numerous works into FSL task, Vision Transformers (ViTs) have +rarely been taken as the backbone to FSL with few trials focusing on naive +finetuning of whole backbone or classification layer.} Essentially, despite +ViTs have been shown to enjoy comparable or even better performance on other +vision tasks, it is still very nontrivial to efficiently finetune the ViTs in +real-world FSL scenarios. To this end, we propose a novel efficient Transformer +Tuning (eTT) method that facilitates finetuning ViTs in the FSL tasks. The key +novelties come from the newly presented Attentive Prefix Tuning (APT) and +Domain Residual Adapter (DRA) for the task and backbone tuning, individually. +Specifically, in APT, the prefix is projected to new key and value pairs that +are attached to each self-attention layer to provide the model with +task-specific information. Moreover, we design the DRA in the form of learnable +offset vectors to handle the potential domain gaps between base and novel data. +To ensure the APT would not deviate from the initial task-specific information +much, we further propose a novel prototypical regularization, which maximizes +the similarity between the projected distribution of prefix and initial +prototypes, regularizing the update procedure. Our method receives outstanding +performance on the challenging Meta-Dataset. We conduct extensive experiments +to show the efficacy of our model. +" +Unleashing the Power of Shared Label Structures for Human Activity Recognition,Xiyuan Zhang,http://arxiv.org/pdf/2301.03462v2.pdf,2023-01-01,"['cs.lg', 'cs.ai', 'eess.sp']",2301.03462v2.pdf," Current human activity recognition (HAR) techniques regard activity labels as +integer class IDs without explicitly modeling the semantics of class labels. We +observe that different activity names often have shared structures. For +example, ""open door"" and ""open fridge"" both have ""open"" as the action; ""kicking +soccer ball"" and ""playing tennis ball"" both have ""ball"" as the object. Such +shared structures in label names can be translated to the similarity in sensory +data and modeling common structures would help uncover knowledge across +different activities, especially for activities with limited samples. In this +paper, we propose SHARE, a HAR framework that takes into account shared +structures of label names for different activities. To exploit the shared +structures, SHARE comprises an encoder for extracting features from input +sensory time series and a decoder for generating label names as a token +sequence. We also propose three label augmentation techniques to help the model +more effectively capture semantic structures across activities, including a +basic token-level augmentation, and two enhanced embedding-level and +sequence-level augmentations utilizing the capabilities of pre-trained models. +SHARE outperforms state-of-the-art HAR models in extensive experiments on seven +HAR benchmark datasets. We also evaluate in few-shot learning and label +imbalance settings and observe even more significant performance gap. +" +"See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning",Zhenfang Chen,http://arxiv.org/pdf/2301.05226v1.pdf,2023-01-12,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2301.05226v1.pdf," Large pre-trained vision and language models have demonstrated remarkable +capacities for various tasks. However, solving the knowledge-based visual +reasoning tasks remains challenging, which requires a model to comprehensively +understand image content, connect the external world knowledge, and perform +step-by-step reasoning to answer the questions correctly. To this end, we +propose a novel framework named Interactive Prompting Visual Reasoner (IPVR) +for few-shot knowledge-based visual reasoning. IPVR contains three stages, see, +think and confirm. The see stage scans the image and grounds the visual concept +candidates with a visual perception model. The think stage adopts a pre-trained +large language model (LLM) to attend to the key concepts from candidates +adaptively. It then transforms them into text context for prompting with a +visual captioning model and adopts the LLM to generate the answer. The confirm +stage further uses the LLM to generate the supporting rationale to the answer, +verify the generated rationale with a cross-modality classifier and ensure that +the rationale can infer the predicted output consistently. We conduct +experiments on a range of knowledge-based visual reasoning datasets. We found +our IPVR enjoys several benefits, 1). it achieves better performance than the +previous few-shot learning baselines; 2). it enjoys the total transparency and +trustworthiness of the whole reasoning process by providing rationales for each +reasoning step; 3). it is computation-efficient compared with other fine-tuning +baselines. +" +Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning,Xinyi Wang,http://arxiv.org/pdf/2301.11916v3.pdf,2023-01-27,"['cs.cl', 'cs.ai', 'cs.lg']",2301.11916v3.pdf," In recent years, pre-trained large language models (LLMs) have demonstrated +remarkable efficiency in achieving an inference-time few-shot learning +capability known as in-context learning. However, existing literature has +highlighted the sensitivity of this capability to the selection of few-shot +demonstrations. Current understandings of the underlying mechanisms by which +this capability arises from regular language model pretraining objectives +remain disconnected from the real-world LLMs. This study aims to examine the +in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs +as latent variable models. On this premise, we propose an algorithm to select +optimal demonstrations from a set of annotated data with a small LM, and then +directly generalize the selected demonstrations to larger LMs. We demonstrate +significant improvement over baselines, averaged over eight GPT models on eight +real-world text classification datasets. We also demonstrate the real-world +usefulness of our algorithm on GSM8K, a math word problem dataset. Our +empirical findings support our hypothesis that LLMs implicitly infer a latent +variable containing task information. +" +Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment,Hao Liu,http://arxiv.org/pdf/2302.00902v2.pdf,2023-02-02,"['cs.lg', 'cs.cl', 'cs.cv']",2302.00902v2.pdf," Recent progress in scaling up large language models has shown impressive +capabilities in performing few-shot learning across a wide range of text-based +tasks. However, a key limitation is that these language models fundamentally +lack visual perception - a crucial attribute needed to extend these models to +be able to interact with the real world and solve vision tasks, such as in +visual-question answering and robotics. Prior works have largely connected +image to text through pretraining and/or fine-tuning on curated image-text +datasets, which can be a costly and expensive process. In order to resolve this +limitation, we propose a simple yet effective approach called +Language-Quantized AutoEncoder (LQAE), a modification of VQ-VAE that learns to +align text-image data in an unsupervised manner by leveraging pretrained +language models (e.g., BERT, RoBERTa). Our main idea is to encode image as +sequences of text tokens by directly quantizing image embeddings using a +pretrained language codebook. We then apply random masking followed by a BERT +model, and have the decoder reconstruct the original image from BERT predicted +text token embeddings. By doing so, LQAE learns to represent similar images +with similar clusters of text tokens, thereby aligning these two modalities +without the use of aligned text-image pairs. This enables few-shot image +classification with large language models (e.g., GPT-3) as well as linear +classification of images based on BERT text features. To the best of our +knowledge, our work is the first work that uses unaligned images for multimodal +tasks by leveraging the power of pretrained language models. +" +The unreasonable effectiveness of few-shot learning for machine translation,Xavier Garcia,http://arxiv.org/pdf/2302.01398v1.pdf,2023-02-02,['cs.cl'],2302.01398v1.pdf," We demonstrate the potential of few-shot translation systems, trained with +unpaired language data, for both high and low-resource language pairs. We show +that with only 5 examples of high-quality translation data shown at inference, +a transformer decoder-only model trained solely with self-supervised learning, +is able to match specialized supervised state-of-the-art models as well as more +general commercial translation systems. In particular, we outperform the best +performing system on the WMT'21 English - Chinese news translation task by only +using five examples of English - Chinese parallel data at inference. Moreover, +our approach in building these models does not necessitate joint multilingual +training or back-translation, is conceptually simple and shows the potential to +extend to the multilingual setting. Furthermore, the resulting models are two +orders of magnitude smaller than state-of-the-art language models. We then +analyze the factors which impact the performance of few-shot translation +systems, and highlight that the quality of the few-shot demonstrations heavily +determines the quality of the translations generated by our models. Finally, we +show that the few-shot paradigm also provides a way to control certain +attributes of the translation -- we show that we are able to control for +regional varieties and formality using only a five examples at inference, +paving the way towards controllable machine translation systems. +" +CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models,Changan Niu,http://arxiv.org/pdf/2302.04030v2.pdf,2023-02-08,"['cs.se', 'cs.ai']",2302.04030v2.pdf," Despite the recent advances showing that a model pre-trained on large-scale +source code data is able to gain appreciable generalization capability, it +still requires a sizeable amount of data on the target task for fine-tuning. +And the effectiveness of the model generalization is largely affected by the +size and quality of the fine-tuning data, which is detrimental for target tasks +with limited or unavailable resources. Therefore, cross-task generalization, +with the goal of improving the generalization of the model to unseen tasks that +have not been seen before, is of strong research and application value. + In this paper, we propose a large-scale benchmark that includes 216 existing +code-related tasks. Then, we annotate each task with the corresponding meta +information such as task description and instruction, which contains detailed +information about the task and a solution guide. This also helps us to easily +create a wide variety of ``training/evaluation'' task splits to evaluate the +various cross-task generalization capabilities of the model. Then we perform +some preliminary experiments to demonstrate that the cross-task generalization +of models can be largely improved by in-context learning methods such as +few-shot learning and learning from task instructions, which shows the +promising prospects of conducting cross-task learning research on our +benchmark. We hope that the collection of the datasets and our benchmark will +facilitate future work that is not limited to cross-task generalization. +" +Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning,Zhuolin Yang,http://arxiv.org/pdf/2302.04858v2.pdf,2023-02-09,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.ir', 'cs.lg']",2302.04858v2.pdf," Augmenting pretrained language models (LMs) with a vision encoder (e.g., +Flamingo) has obtained the state-of-the-art results in image-to-text +generation. However, these models store all the knowledge within their +parameters, thus often requiring enormous model parameters to model the +abundant visual concepts and very rich textual descriptions. Additionally, they +are inefficient in incorporating new data, requiring a computational-expensive +fine-tuning process. In this work, we introduce a Retrieval-augmented Visual +Language Model, Re-ViLM, built upon the Flamingo, that supports retrieving the +relevant knowledge from the external database for zero and in-context few-shot +image-to-text generations. By storing certain knowledge explicitly in the +external database, our approach reduces the number of model parameters and can +easily accommodate new data during evaluation by simply updating the database. +We also construct an interleaved image and text data that facilitates +in-context few-shot learning capabilities. We demonstrate that Re-ViLM +significantly boosts performance for image-to-text generation tasks, especially +for zero-shot and few-shot generation in out-of-domain settings with 4 times +less parameters compared with baseline methods. +" +Mask-guided BERT for Few Shot Text Classification,Wenxiong Liao,http://arxiv.org/pdf/2302.10447v3.pdf,2023-02-21,"['cs.cl', 'cs.ai']",2302.10447v3.pdf," Transformer-based language models have achieved significant success in +various domains. However, the data-intensive nature of the transformer +architecture requires much labeled data, which is challenging in low-resource +scenarios (i.e., few-shot learning (FSL)). The main challenge of FSL is the +difficulty of training robust models on small amounts of samples, which +frequently leads to overfitting. Here we present Mask-BERT, a simple and +modular framework to help BERT-based architectures tackle FSL. The proposed +approach fundamentally differs from existing FSL strategies such as prompt +tuning and meta-learning. The core idea is to selectively apply masks on text +inputs and filter out irrelevant information, which guides the model to focus +on discriminative tokens that influence prediction results. In addition, to +make the text representations from different categories more separable and the +text representations from the same category more compact, we introduce a +contrastive learning loss function. Experimental results on public-domain +benchmark datasets demonstrate the effectiveness of Mask-BERT. +" +Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation,Minchang Kim,http://arxiv.org/pdf/2302.14640v2.pdf,2023-02-28,"['cs.ir', 'cs.lg']",2302.14640v2.pdf," Sequential recommenders have made great strides in capturing a user's +preferences. Nevertheless, the cold-start recommendation remains a fundamental +challenge as they typically involve limited user-item interactions for +personalization. Recently, gradient-based meta-learning approaches have emerged +in the sequential recommendation field due to their fast adaptation and +easy-to-integrate abilities. The meta-learning algorithms formulate the +cold-start recommendation as a few-shot learning problem, where each user is +represented as a task to be adapted. While meta-learning algorithms generally +assume that task-wise samples are evenly distributed over classes or values, +user-item interactions in real-world applications do not conform to such a +distribution (e.g., watching favorite videos multiple times, leaving only +positive ratings without any negative ones). Consequently, imbalanced user +feedback, which accounts for the majority of task training data, may dominate +the user adaptation process and prevent meta-learning algorithms from learning +meaningful meta-knowledge for personalized recommendations. To alleviate this +limitation, we propose a novel sequential recommendation framework based on +gradient-based meta-learning that captures the imbalanced rating distribution +of each user and computes adaptive loss for user-specific learning. Our work is +the first to tackle the impact of imbalanced ratings in cold-start sequential +recommendation scenarios. Through extensive experiments conducted on real-world +datasets, we demonstrate the effectiveness of our framework. +" +"Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners",Renrui Zhang,http://arxiv.org/pdf/2303.02151v1.pdf,2023-03-03,"['cs.cv', 'cs.cl']",2303.02151v1.pdf," Visual recognition in low-data regimes requires deep neural networks to learn +generalized representations from limited training samples. Recently, CLIP-based +methods have shown promising few-shot performance benefited from the +contrastive language-image pre-training. We then question, if the more diverse +pre-training knowledge can be cascaded to further assist few-shot +representation learning. In this paper, we propose CaFo, a Cascade of +Foundation models that incorporates diverse prior knowledge of various +pre-training paradigms for better few-shot learning. Our CaFo incorporates +CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, +DALL-E's vision-generative knowledge, and GPT-3's language-generative +knowledge. Specifically, CaFo works by 'Prompt, Generate, then Cache'. Firstly, +we leverage GPT-3 to produce textual inputs for prompting CLIP with rich +downstream linguistic semantics. Then, we generate synthetic images via DALL-E +to expand the few-shot training data without any manpower. At last, we +introduce a learnable cache model to adaptively blend the predictions from CLIP +and DINO. By such collaboration, CaFo can fully unleash the potential of +different pre-training methods and unify them to perform state-of-the-art for +few-shot classification. Code is available at +https://github.com/ZrrSkywalker/CaFo. +" +Knowledge-augmented Few-shot Visual Relation Detection,Tianyu Yu,http://arxiv.org/pdf/2303.05342v1.pdf,2023-03-09,"['cs.cv', 'cs.ai']",2303.05342v1.pdf," Visual Relation Detection (VRD) aims to detect relationships between objects +for image understanding. Most existing VRD methods rely on thousands of +training samples of each relationship to achieve satisfactory performance. Some +recent papers tackle this problem by few-shot learning with elaborately +designed pipelines and pre-trained word vectors. However, the performance of +existing few-shot VRD models is severely hampered by the poor generalization +capability, as they struggle to handle the vast semantic diversity of visual +relationships. Nonetheless, humans have the ability to learn new relationships +with just few examples based on their knowledge. Inspired by this, we devise a +knowledge-augmented, few-shot VRD framework leveraging both textual knowledge +and visual relation knowledge to improve the generalization ability of few-shot +VRD. The textual knowledge and visual relation knowledge are acquired from a +pre-trained language model and an automatically constructed visual relation +knowledge graph, respectively. We extensively validate the effectiveness of our +framework. Experiments conducted on three benchmarks from the commonly used +Visual Genome dataset show that our performance surpasses existing +state-of-the-art models with a large improvement. +" +Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models,Juncheng Li,http://arxiv.org/pdf/2303.06571v2.pdf,2023-03-12,['cs.cv'],2303.06571v2.pdf," Prompt tuning, a recently emerging paradigm, enables the powerful +vision-language pre-training models to adapt to downstream tasks in a parameter +-- and data -- efficient way, by learning the ``soft prompts'' to condition +frozen pre-training models. Though effective, it is particularly problematic in +the few-shot scenario, where prompt tuning performance is sensitive to the +initialization and requires a time-consuming process to find a good +initialization, thus restricting the fast adaptation ability of the +pre-training models. In addition, prompt tuning could undermine the +generalizability of the pre-training models, because the learnable prompt +tokens are easy to overfit to the limited training samples. To address these +issues, we introduce a novel Gradient-RegulAted Meta-prompt learning (GRAM) +framework that jointly meta-learns an efficient soft prompt initialization for +better adaptation and a lightweight gradient regulating function for strong +cross-domain generalizability in a meta-learning paradigm using only the +unlabeled image-text pre-training data. Rather than designing a specific prompt +tuning method, our GRAM can be easily incorporated into various prompt tuning +methods in a model-agnostic way, and comprehensive experiments show that GRAM +brings about consistent improvement for them in several settings (i.e., +few-shot learning, cross-domain generalization, cross-dataset generalization, +etc.) over 11 datasets. Further, experiments show that GRAM enables the +orthogonal methods of textual and visual prompt tuning to work in a +mutually-enhanced way, offering better generalizability beyond the uni-modal +prompt tuning methods. +" +Decomposed Prototype Learning for Few-Shot Scene Graph Generation,Xingchen Li,http://arxiv.org/pdf/2303.10863v1.pdf,2023-03-20,['cs.cv'],2303.10863v1.pdf," Today's scene graph generation (SGG) models typically require abundant manual +annotations to learn new predicate types. Thus, it is difficult to apply them +to real-world applications with a long-tailed distribution of predicates. In +this paper, we focus on a new promising task of SGG: few-shot SGG (FSSGG). +FSSGG encourages models to be able to quickly transfer previous knowledge and +recognize novel predicates well with only a few examples. Although many +advanced approaches have achieved great success on few-shot learning (FSL) +tasks, straightforwardly extending them into FSSGG is not applicable due to two +intrinsic characteristics of predicate concepts: 1) Each predicate category +commonly has multiple semantic meanings under different contexts. 2) The visual +appearance of relation triplets with the same predicate differs greatly under +different subject-object pairs. Both issues make it hard to model conventional +latent representations for predicate categories with state-of-the-art FSL +methods. To this end, we propose a novel Decomposed Prototype Learning (DPL). +Specifically, we first construct a decomposable prototype space to capture +intrinsic visual patterns of subjects and objects for predicates, and enhance +their feature representations with these decomposed prototypes. Then, we devise +an intelligent metric learner to assign adaptive weights to each support sample +by considering the relevance of their subject-object pairs. We further re-split +the VG dataset and compare DPL with various FSL methods to benchmark this task. +Extensive results show that DPL achieves excellent performance in both base and +novel categories. +" +Supervised Masked Knowledge Distillation for Few-Shot Transformers,Han Lin,http://arxiv.org/pdf/2303.15466v2.pdf,2023-03-25,"['cs.cv', 'cs.ai']",2303.15466v2.pdf," Vision Transformers (ViTs) emerge to achieve impressive performance on many +data-abundant computer vision tasks by capturing long-range dependencies among +local features. However, under few-shot learning (FSL) settings on small +datasets with only a few labeled data, ViT tends to overfit and suffers from +severe performance degradation due to its absence of CNN-alike inductive bias. +Previous works in FSL avoid such problem either through the help of +self-supervised auxiliary losses, or through the dextile uses of label +information under supervised settings. But the gap between self-supervised and +supervised few-shot Transformers is still unfilled. Inspired by recent advances +in self-supervised knowledge distillation and masked image modeling (MIM), we +propose a novel Supervised Masked Knowledge Distillation model (SMKD) for +few-shot Transformers which incorporates label information into +self-distillation frameworks. Compared with previous self-supervised methods, +we allow intra-class knowledge distillation on both class and patch tokens, and +introduce the challenging task of masked patch tokens reconstruction across +intra-class images. Experimental results on four few-shot classification +benchmark datasets show that our method with simple design outperforms previous +methods by a large margin and achieves a new start-of-the-art. Detailed +ablation studies confirm the effectiveness of each component of our model. Code +for this paper is available here: https://github.com/HL-hanlin/SMKD. +" +"Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text",Wanrong Zhu,http://arxiv.org/pdf/2304.06939v3.pdf,2023-04-14,"['cs.cv', 'cs.cl']",2304.06939v3.pdf," In-context vision and language models like Flamingo support arbitrarily +interleaved sequences of images and text as input. This format not only enables +few-shot learning via interleaving independent supervised (image, text) +examples, but also, more complex prompts involving interaction between images, +e.g., ""What do image A and image B have in common?"" To support this interface, +pretraining occurs over web corpora that similarly contain interleaved +images+text. To date, however, large-scale data of this form have not been +publicly available. + We release Multimodal C4, an augmentation of the popular text-only C4 corpus +with images interleaved. We use a linear assignment algorithm to place images +into longer bodies of text using CLIP features, a process that we show +outperforms alternatives. Multimodal C4 spans everyday topics like cooking, +travel, technology, etc. A manual inspection of a random sample of documents +shows that a vast majority (88%) of images are topically relevant, and that +linear assignment frequently selects individual sentences specifically +well-aligned with each image (80%). After filtering NSFW images, ads, etc., the +resulting corpus consists of 101.2M documents with 571M images interleaved in +43B English tokens. +" +A Survey on Few-Shot Class-Incremental Learning,Songsong Tian,http://arxiv.org/pdf/2304.08130v2.pdf,2023-04-17,['cs.cv'],2304.08130v2.pdf," Large deep learning models are impressive, but they struggle when real-time +data is not available. Few-shot class-incremental learning (FSCIL) poses a +significant challenge for deep neural networks to learn new tasks from just a +few labeled samples without forgetting the previously learned ones. This setup +easily leads to catastrophic forgetting and overfitting problems, severely +affecting model performance. Studying FSCIL helps overcome deep learning model +limitations on data volume and acquisition time, while improving practicality +and adaptability of machine learning models. This paper provides a +comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize +few-shot learning and incremental learning, focusing on introducing FSCIL from +two perspectives, while reviewing over 30 theoretical research studies and more +than 20 applied research studies. From the theoretical perspective, we provide +a novel categorization approach that divides the field into five subcategories, +including traditional machine learning methods, meta-learning based methods, +feature and feature space-based methods, replay-based methods, and dynamic +network structure-based methods. We also evaluate the performance of recent +theoretical research on benchmark datasets of FSCIL. From the application +perspective, FSCIL has achieved impressive achievements in various fields of +computer vision such as image classification, object detection, and image +segmentation, as well as in natural language processing and graph. We summarize +the important applications. Finally, we point out potential future research +directions, including applications, problem setups, and theory development. +Overall, this paper offers a comprehensive analysis of the latest advances in +FSCIL from a methodological, performance, and application perspective. +" +Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems,Zheng An,http://arxiv.org/pdf/2304.12010v1.pdf,2023-04-24,['quant-ph'],2304.12010v1.pdf," Schr\""odinger's equation serves as a fundamental component in characterizing +quantum systems, wherein both quantum state tomography and Hamiltonian learning +are instrumental in comprehending and interpreting quantum systems. While +numerous techniques exist for carrying out state tomography and learning +Hamiltonians individually, no method has been developed to combine these two +aspects. In this study, we introduce a new approach that employs the attention +mechanism in transformer models to effectively merge quantum state tomography +and Hamiltonian learning. By carefully choosing and preparing the training +data, our method integrates both tasks without altering the model's +architecture, allowing the model to effectively learn the intricate +relationships between quantum states and Hamiltonian. We also demonstrate the +effectiveness of our approach across various quantum systems, ranging from +simple 2-qubit cases to more involved 2D antiferromagnetic Heisenberg +structures. The data collection process is streamlined, as it only necessitates +a one-way generation process beginning with state tomography. Furthermore, the +scalability and few-shot learning capabilities of our method could potentially +minimize the resources required for characterizing and optimizing quantum +systems. Our research provides valuable insights into the relationship between +Hamiltonian structure and quantum system behavior, fostering opportunities for +additional studies on quantum systems and the advancement of quantum +computation and associated technologies. +" +Analogy-Forming Transformers for Few-Shot 3D Parsing,Nikolaos Gkanatsios,http://arxiv.org/pdf/2304.14382v2.pdf,2023-04-27,"['cs.cv', 'cs.ai', 'cs.lg']",2304.14382v2.pdf," We present Analogical Networks, a model that encodes domain knowledge +explicitly, in a collection of structured labelled 3D scenes, in addition to +implicitly, as model parameters, and segments 3D object scenes with analogical +reasoning: instead of mapping a scene to part segments directly, our model +first retrieves related scenes from memory and their corresponding part +structures, and then predicts analogous part structures for the input scene, +via an end-to-end learnable modulation mechanism. By conditioning on more than +one retrieved memories, compositions of structures are predicted, that mix and +match parts across the retrieved memories. One-shot, few-shot or many-shot +learning are treated uniformly in Analogical Networks, by conditioning on the +appropriate set of memories, whether taken from a single, few or many memory +exemplars, and inferring analogous parses. We show Analogical Networks are +competitive with state-of-the-art 3D segmentation transformers in many-shot +settings, and outperform them, as well as existing paradigms of meta-learning +and few-shot learning, in few-shot settings. Analogical Networks successfully +segment instances of novel object categories simply by expanding their memory, +without any weight updates. Our code and models are publicly available in the +project webpage: http://analogicalnets.github.io/. +" +HQP: A Human-Annotated Dataset for Detecting Online Propaganda,Abdurahman Maarouf,http://arxiv.org/pdf/2304.14931v2.pdf,2023-04-28,['cs.cl'],2304.14931v2.pdf," Online propaganda poses a severe threat to the integrity of societies. +However, existing datasets for detecting online propaganda have a key +limitation: they were annotated using weak labels that can be noisy and even +incorrect. To address this limitation, our work makes the following +contributions: (1) We present HQP: a novel dataset (N=30,000) for detecting +online propaganda with high-quality labels. To the best of our knowledge, HQP +is the first dataset for detecting online propaganda that was created through +human annotation. (2) We show empirically that state-of-the-art language models +fail in detecting online propaganda when trained with weak labels (AUC: 64.03). +In contrast, state-of-the-art language models can accurately detect online +propaganda when trained with our high-quality labels (AUC: 92.25), which is an +improvement of ~44%. (3) To address the cost of labeling, we extend our work to +few-shot learning. Specifically, we show that prompt-based learning using a +small sample of high-quality labels can still achieve a reasonable performance +(AUC: 80.27). Finally, we discuss implications for the NLP community to balance +the cost and quality of labeling. Crucially, our work highlights the importance +of high-quality labels for sensitive NLP tasks such as propaganda detection. +" +Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment,Zhen Zhang,http://arxiv.org/pdf/2305.03510v2.pdf,2023-05-02,"['cs.cl', 'cs.ai']",2305.03510v2.pdf," Pre-trained vision and language models such as CLIP have witnessed remarkable +success in connecting images and texts with a primary focus on English texts. +Despite recent efforts to extend CLIP to support other languages, disparities +in performance among different languages have been observed due to uneven +resource availability. Additionally, current cross-lingual transfer methods of +those pre-trained models would consume excessive resources for a large number +of languages. Therefore, we propose a new parameter-efficient cross-lingual +transfer learning framework that utilizes a translation-based alignment method +to mitigate multilingual disparities and explores parameter-efficient +fine-tuning methods for parameter-efficient cross-lingual transfer. Extensive +experiments on XTD and Multi30K datasets, covering 11 languages under +zero-shot, few-shot, and full-dataset learning scenarios, show that our +framework significantly reduces the multilingual disparities among languages +and improves cross-lingual transfer results, especially in low-resource +scenarios, while only keeping and fine-tuning an extremely small number of +parameters compared to the full model (e.g., Our framework only requires 0.16\% +additional parameters of a full-model for each language in the few-shot +learning scenario). The codes are available at +\url{https://github.com/eric-ai-lab/PECTVLM}. The codes are available at +\url{https://github.com/eric-ai-lab/PECTVLM}. +" +CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors,Peng Li,http://arxiv.org/pdf/2305.05711v2.pdf,2023-05-09,"['cs.cl', 'cs.ai']",2305.05711v2.pdf," Large language models (LLMs) pre-trained on massive corpora have demonstrated +impressive few-shot learning ability on many NLP tasks. A common practice is to +recast the task into a text-to-text format such that generative LLMs of natural +language (NL-LLMs) like GPT-3 can be prompted to solve it. However, it is +nontrivial to perform information extraction (IE) tasks with NL-LLMs since the +output of the IE task is usually structured and therefore is hard to be +converted into plain text. In this paper, we propose to recast the structured +output in the form of code instead of natural language and utilize generative +LLMs of code (Code-LLMs) such as Codex to perform IE tasks, in particular, +named entity recognition and relation extraction. In contrast to NL-LLMs, we +show that Code-LLMs can be well-aligned with these IE tasks by designing +code-style prompts and formulating these IE tasks as code generation tasks. +Experiment results on seven benchmarks show that our method consistently +outperforms fine-tuning moderate-size pre-trained models specially designed for +IE tasks (e.g., UIE) and prompting NL-LLMs under few-shot settings. We further +conduct a series of in-depth analyses to demonstrate the merits of leveraging +Code-LLMs for IE tasks. +" +Qualifying Chinese Medical Licensing Examination with Knowledge Enhanced Generative Pre-training Model,Jiageng Wu,http://arxiv.org/pdf/2305.10163v2.pdf,2023-05-17,"['cs.cl', 'cs.ai', 'cs.cy']",2305.10163v2.pdf," Generative Pre-Training (GPT) models like ChatGPT have demonstrated +exceptional performance in various Natural Language Processing (NLP) tasks. +Although ChatGPT has been integrated into the overall workflow to boost +efficiency in many domains, the lack of flexibility in the finetuning process +hinders its applications in areas that demand extensive domain expertise and +semantic knowledge, such as healthcare. In this paper, we evaluate ChatGPT on +the China National Medical Licensing Examination (CNMLE) and propose a novel +approach to improve ChatGPT from two perspectives: integrating medical domain +knowledge and enabling few-shot learning. By using a simple but effective +retrieval method, medical background knowledge is extracted as semantic +instructions to guide the inference of ChatGPT. Similarly, relevant medical +questions are identified and fed as demonstrations to ChatGPT. Experimental +results show that directly applying ChatGPT fails to qualify the CNMLE at a +score of 51 (i.e., only 51\% of questions are answered correctly). While our +knowledge-enhanced model achieves a high score of 70 on CNMLE-2022 which not +only passes the qualification but also surpasses the average score of humans +(61). This research demonstrates the potential of knowledge-enhanced ChatGPT to +serve as versatile medical assistants, capable of analyzing real-world medical +problems in a more accessible, user-friendly, and adaptable manner. +" +PointGPT: Auto-regressively Generative Pre-training from Point Clouds,Guangyan Chen,http://arxiv.org/pdf/2305.11487v2.pdf,2023-05-19,['cs.cv'],2305.11487v2.pdf," Large language models (LLMs) based on the generative pre-training transformer +(GPT) have demonstrated remarkable effectiveness across a diverse range of +downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, +a novel approach that extends the concept of GPT to point clouds, addressing +the challenges associated with disorder properties, low information density, +and task gaps. Specifically, a point cloud auto-regressive generation task is +proposed to pre-train transformer models. Our method partitions the input point +cloud into multiple point patches and arranges them in an ordered sequence +based on their spatial proximity. Then, an extractor-generator based +transformer decoder, with a dual masking strategy, learns latent +representations conditioned on the preceding point patches, aiming to predict +the next one in an auto-regressive manner. Our scalable approach allows for +learning high-capacity models that generalize well, achieving state-of-the-art +performance on various downstream tasks. In particular, our approach achieves +classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the +ScanObjectNN dataset, outperforming all other transformer models. Furthermore, +our method also attains new state-of-the-art accuracies on all four few-shot +learning benchmarks. +" +A Survey of Diffusion Models in Natural Language Processing,Hao Zou,http://arxiv.org/pdf/2305.14671v2.pdf,2023-05-24,['cs.cl'],2305.14671v2.pdf," This survey paper provides a comprehensive review of the use of diffusion +models in natural language processing (NLP). Diffusion models are a class of +mathematical models that aim to capture the diffusion of information or signals +across a network or manifold. In NLP, diffusion models have been used in a +variety of applications, such as natural language generation, sentiment +analysis, topic modeling, and machine translation. This paper discusses the +different formulations of diffusion models used in NLP, their strengths and +limitations, and their applications. We also perform a thorough comparison +between diffusion models and alternative generative models, specifically +highlighting the autoregressive (AR) models, while also examining how diverse +architectures incorporate the Transformer in conjunction with diffusion models. +Compared to AR models, diffusion models have significant advantages for +parallel generation, text interpolation, token-level controls such as syntactic +structures and semantic contents, and robustness. Exploring further +permutations of integrating Transformers into diffusion models would be a +valuable pursuit. Also, the development of multimodal diffusion models and +large-scale diffusion language models with notable capabilities for few-shot +learning would be important directions for the future advance of diffusion +models in NLP. +" +Benchmarking Arabic AI with Large Language Models,Ahmed Abdelali,http://arxiv.org/pdf/2305.14982v1.pdf,2023-05-24,"['cs.cl', 'cs.ai', '68t50', 'f.2.2; i.2.7']",2305.14982v1.pdf," With large Foundation Models (FMs), language technologies (AI in general) are +entering a new paradigm: eliminating the need for developing large-scale +task-specific datasets and supporting a variety of tasks through set-ups +ranging from zero-shot to few-shot learning. However, understanding FMs +capabilities requires a systematic benchmarking effort by comparing FMs +performance with the state-of-the-art (SOTA) task-specific models. With that +goal, past work focused on the English language and included a few efforts with +multiple languages. Our study contributes to ongoing research by evaluating FMs +performance for standard Arabic NLP and Speech processing, including a range of +tasks from sequence tagging to content classification across diverse domains. +We start with zero-shot learning using GPT-3.5-turbo, Whisper, and USM, +addressing 33 unique tasks using 59 publicly available datasets resulting in 96 +test setups. For a few tasks, FMs performs on par or exceeds the performance of +the SOTA models but for the majority it under-performs. Given the importance of +prompt for the FMs performance, we discuss our prompt strategies in detail and +elaborate on our findings. Our future work on Arabic AI will explore few-shot +prompting, expand the range of tasks, and investigate additional open-source +models. +" +Sentiment Analysis in the Era of Large Language Models: A Reality Check,Wenxuan Zhang,http://arxiv.org/pdf/2305.15005v1.pdf,2023-05-24,['cs.cl'],2305.15005v1.pdf," Sentiment analysis (SA) has been a long-standing research area in natural +language processing. It can offer rich insights into human sentiments and +opinions and has thus seen considerable interest from both academia and +industry. With the advent of large language models (LLMs) such as ChatGPT, +there is a great potential for their employment on SA problems. However, the +extent to which existing LLMs can be leveraged for different sentiment analysis +tasks remains unclear. This paper aims to provide a comprehensive investigation +into the capabilities of LLMs in performing various sentiment analysis tasks, +from conventional sentiment classification to aspect-based sentiment analysis +and multifaceted analysis of subjective texts. We evaluate performance across +13 tasks on 26 datasets and compare the results against small language models +(SLMs) trained on domain-specific datasets. Our study reveals that while LLMs +demonstrate satisfactory performance in simpler tasks, they lag behind in more +complex tasks requiring deeper understanding or structured sentiment +information. However, LLMs significantly outperform SLMs in few-shot learning +settings, suggesting their potential when annotation resources are limited. We +also highlight the limitations of current evaluation practices in assessing +LLMs' SA abilities and propose a novel benchmark, \textsc{SentiEval}, for a +more comprehensive and realistic evaluation. Data and code during our +investigations are available at +\url{https://github.com/DAMO-NLP-SG/LLM-Sentiment}. +" +Impact of Large Language Models on Generating Software Specifications,Danning Xie,http://arxiv.org/pdf/2306.03324v2.pdf,2023-06-06,['cs.se'],2306.03324v2.pdf," Software specifications are essential for ensuring the reliability of +software systems. Existing specification extraction approaches, however, suffer +from limited generalizability and require manual efforts. The recent emergence +of Large Language Models (LLMs), which have been successfully applied to +numerous software engineering tasks, offers a promising avenue for automating +this process. In this paper, we conduct the first empirical study to evaluate +the capabilities of LLMs for generating software specifications from software +comments or documentation. We evaluate LLMs' performance with Few Shot Learning +(FSL), enabling LLMs to generalize from a small number of examples, as well as +different prompt construction strategies, and compare the performance of LLMs +with traditional approaches. Additionally, we conduct a comparative diagnosis +of the failure cases from both LLMs and traditional methods, identifying their +unique strengths and weaknesses. Lastly, we conduct extensive experiments on 15 +state of the art LLMs, evaluating their performance and cost effectiveness for +generating software specifications. + Our results show that with FSL, LLMs outperform traditional methods (by +5.6%), and more sophisticated prompt construction strategies can further +enlarge this performance gap (up to 5.1 to 10.0%). Yet, LLMs suffer from their +unique challenges, such as ineffective prompts and the lack of domain +knowledge, which together account for 53 to 60% of LLM unique failures. The +strong performance of open source models (e.g., StarCoder) makes closed source +models (e.g., GPT 3 Davinci) less desirable due to size and cost. Our study +offers valuable insights for future research to improve specification +generation. +" +One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning,Arnav Chavan,http://arxiv.org/pdf/2306.07967v2.pdf,2023-06-13,"['cs.lg', 'cs.ai', 'cs.cv']",2306.07967v2.pdf," We present Generalized LoRA (GLoRA), an advanced approach for universal +parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), +GLoRA employs a generalized prompt module to optimize pre-trained model weights +and adjust intermediate activations, providing more flexibility and capability +across diverse tasks and datasets. Moreover, GLoRA facilitates efficient +parameter adaptation by employing a scalable, modular, layer-wise structure +search that learns individual adapter of each layer. Originating from a unified +mathematical formulation, GLoRA exhibits strong transfer learning, few-shot +learning and domain generalization abilities, as it adapts to new tasks through +not only weights but also additional dimensions like activations. Comprehensive +experiments demonstrate that GLoRA outperforms all previous methods in natural, +specialized, and structured vision benchmarks, achieving superior accuracy with +fewer parameters and computations. The proposed method on LLaMA-1 and LLaMA-2 +also show considerable enhancements compared to the original LoRA in the +language domain. Furthermore, our structural re-parameterization design ensures +that GLoRA incurs no extra inference cost, rendering it a practical solution +for resource-limited applications. Code and models are available at: +https://github.com/Arnav0400/ViT-Slim/tree/master/GLoRA. +" +Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts,Xuan-Phi Nguyen,http://arxiv.org/pdf/2306.11372v1.pdf,2023-06-20,"['cs.cl', 'cs.ai']",2306.11372v1.pdf," Large language models (LLMs) are known to effectively perform tasks by simply +observing few exemplars. However, in low-resource languages, obtaining such +hand-picked exemplars can still be challenging, where unsupervised techniques +may be necessary. Moreover, competent generative capabilities of LLMs are +observed only in high-resource languages, while their performances among +under-represented languages fall behind due to pre-training data imbalance. To +elicit LLMs' ability onto low-resource languages without any supervised data, +we propose to assemble synthetic exemplars from a diverse set of high-resource +languages to prompt the LLMs to translate from any language into English. These +prompts are then used to create intra-lingual exemplars to perform tasks in the +target languages. Our unsupervised prompting method performs on par with +supervised few-shot learning in LLMs of different sizes for translations +between English and 13 Indic and 21 African low-resource languages. We also +show that fine-tuning a 7B model on data generated from our method helps it +perform competitively with a 175B model. In non-English translation tasks, our +method even outperforms supervised prompting by up to 3 chrF++ in many +low-resource languages. When evaluated on zero-shot multilingual summarization, +our method surpasses other English-pivoting baselines by up to 4 ROUGE-L and is +also favored by GPT-4. +" +ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion,Yingjun Du,http://arxiv.org/pdf/2306.14770v2.pdf,2023-06-26,"['cs.lg', 'cs.ai']",2306.14770v2.pdf," Prototype-based meta-learning has emerged as a powerful technique for +addressing few-shot learning challenges. However, estimating a deterministic +prototype using a simple average function from a limited number of examples +remains a fragile process. To overcome this limitation, we introduce ProtoDiff, +a novel framework that leverages a task-guided diffusion model during the +meta-training phase to gradually generate prototypes, thereby providing +efficient class representations. Specifically, a set of prototypes is optimized +to achieve per-task prototype overfitting, enabling accurately obtaining the +overfitted prototypes for individual tasks. Furthermore, we introduce a +task-guided diffusion process within the prototype space, enabling the +meta-learning of a generative process that transitions from a vanilla prototype +to an overfitted prototype. ProtoDiff gradually generates task-specific +prototypes from random noise during the meta-test stage, conditioned on the +limited samples available for the new task. Furthermore, to expedite training +and enhance ProtoDiff's performance, we propose the utilization of residual +prototype learning, which leverages the sparsity of the residual prototype. We +conduct thorough ablation studies to demonstrate its ability to accurately +capture the underlying prototype distribution and enhance generalization. The +new state-of-the-art performance on within-domain, cross-domain, and few-task +few-shot classification further substantiates the benefit of ProtoDiff. +" +Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection,Zhewei Chen,http://arxiv.org/pdf/2306.16186v1.pdf,2023-06-28,"['cs.cv', 'cs.ai', 'i.2.10; i.4.9; i.5.4']",2306.16186v1.pdf," Fabric defect segmentation is integral to textile quality control. Despite +this, the scarcity of high-quality annotated data and the diversity of fabric +defects present significant challenges to the application of deep learning in +this field. These factors limit the generalization and segmentation performance +of existing models, impeding their ability to handle the complexity of diverse +fabric types and defects. To overcome these obstacles, this study introduces an +innovative method to infuse specialized knowledge of fabric defects into the +Segment Anything Model (SAM), a large-scale visual model. By introducing and +training a unique set of fabric defect-related parameters, this approach +seamlessly integrates domain-specific knowledge into SAM without the need for +extensive modifications to the pre-existing model parameters. The revamped SAM +model leverages generalized image understanding learned from large-scale +natural image datasets while incorporating fabric defect-specific knowledge, +ensuring its proficiency in fabric defect segmentation tasks. The experimental +results reveal a significant improvement in the model's segmentation +performance, attributable to this novel amalgamation of generic and +fabric-specific knowledge. When benchmarking against popular existing +segmentation models across three datasets, our proposed model demonstrates a +substantial leap in performance. Its impressive results in cross-dataset +comparisons and few-shot learning experiments further demonstrate its potential +for practical applications in textile quality control. +" +Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation,Balamurali Murugesan,http://arxiv.org/pdf/2307.00097v2.pdf,2023-06-30,['cs.cv'],2307.00097v2.pdf," Recently, CLIP-based approaches have exhibited remarkable performance on +generalization and few-shot learning tasks, fueled by the power of contrastive +language-vision pre-training. In particular, prompt tuning has emerged as an +effective strategy to adapt the pre-trained language-vision models to +downstream tasks by employing task-related textual tokens. Motivated by this +progress, in this work we question whether other fundamental problems, such as +weakly supervised semantic segmentation (WSSS), can benefit from prompt tuning. +Our findings reveal two interesting observations that shed light on the impact +of prompt tuning on WSSS. First, modifying only the class token of the text +prompt results in a greater impact on the Class Activation Map (CAM), compared +to arguably more complex strategies that optimize the context. And second, the +class token associated with the image ground truth does not necessarily +correspond to the category that yields the best CAM. Motivated by these +observations, we introduce a novel approach based on a PrOmpt cLass lEarning +(POLE) strategy. Through extensive experiments we demonstrate that our simple, +yet efficient approach achieves SOTA performance in a well-known WSSS +benchmark. These results highlight not only the benefits of language-vision +models in WSSS but also the potential of prompt learning for this problem. The +code is available at https://github.com/rB080/WSS_POLE. +" +Meta-training with Demonstration Retrieval for Efficient Few-shot Learning,Aaron Mueller,http://arxiv.org/pdf/2307.00119v1.pdf,2023-06-30,['cs.cl'],2307.00119v1.pdf," Large language models show impressive results on few-shot NLP tasks. However, +these models are memory and computation-intensive. Meta-training allows one to +leverage smaller models for few-shot generalization in a domain-general and +task-agnostic manner; however, these methods alone results in models that may +not have sufficient parameterization or knowledge to adapt quickly to a large +variety of tasks. To overcome this issue, we propose meta-training with +demonstration retrieval, where we use a dense passage retriever to retrieve +semantically similar labeled demonstrations to each example for more varied +supervision. By separating external knowledge from model parameters, we can use +meta-training to train parameter-efficient models that generalize well on a +larger variety of tasks. We construct a meta-training set from UnifiedQA and +CrossFit, and propose a demonstration bank based on UnifiedQA tasks. To our +knowledge, our work is the first to combine retrieval with meta-training, to +use DPR models to retrieve demonstrations, and to leverage demonstrations from +many tasks simultaneously, rather than randomly sampling demonstrations from +the training set of the target task. Our approach outperforms a variety of +targeted parameter-efficient and retrieval-augmented few-shot methods on QA, +NLI, and text classification tasks (including SQuAD, QNLI, and TREC). Our +approach can be meta-trained and fine-tuned quickly on a single GPU. +" +TablEye: Seeing small Tables through the Lens of Images,Seung-eon Lee,http://arxiv.org/pdf/2307.02491v1.pdf,2023-07-04,"['cs.lg', 'cs.ai']",2307.02491v1.pdf," The exploration of few-shot tabular learning becomes imperative. Tabular data +is a versatile representation that captures diverse information, yet it is not +exempt from limitations, property of data and model size. Labeling extensive +tabular data can be challenging, and it may not be feasible to capture every +important feature. Few-shot tabular learning, however, remains relatively +unexplored, primarily due to scarcity of shared information among independent +datasets and the inherent ambiguity in defining boundaries within tabular data. +To the best of our knowledge, no meaningful and unrestricted few-shot tabular +learning techniques have been developed without imposing constraints on the +dataset. In this paper, we propose an innovative framework called TablEye, +which aims to overcome the limit of forming prior knowledge for tabular data by +adopting domain transformation. It facilitates domain transformation by +generating tabular images, which effectively conserve the intrinsic semantics +of the original tabular data. This approach harnesses rigorously tested +few-shot learning algorithms and embedding functions to acquire and apply prior +knowledge. Leveraging shared data domains allows us to utilize this prior +knowledge, originally learned from the image domain. Specifically, TablEye +demonstrated a superior performance by outstripping the TabLLM in a 4-shot task +with a maximum 0.11 AUC and a STUNT in a 1- shot setting, where it led on +average by 3.17% accuracy. +" +Text Descriptions are Compressive and Invariant Representations for Visual Learning,Zhili Feng,http://arxiv.org/pdf/2307.04317v2.pdf,2023-07-10,"['cs.cv', 'cs.lg']",2307.04317v2.pdf," Modern image classification is based upon directly predicting classes via +large discriminative networks, which do not directly contain information about +the intuitive visual features that may constitute a classification decision. +Recently, work in vision-language models (VLM) such as CLIP has provided ways +to specify natural language descriptions of image classes, but typically +focuses on providing single descriptions for each class. In this work, we +demonstrate that an alternative approach, in line with humans' understanding of +multiple visual features per class, can also provide compelling performance in +the robust few-shot learning setting. In particular, we introduce a novel +method, \textit{SLR-AVD (Sparse Logistic Regression using Augmented Visual +Descriptors)}. This method first automatically generates multiple visual +descriptions of each class via a large language model (LLM), then uses a VLM to +translate these descriptions to a set of visual feature embeddings of each +image, and finally uses sparse logistic regression to select a relevant subset +of these features to classify each image. Core to our approach is the fact +that, information-theoretically, these descriptive features are more invariant +to domain shift than traditional image embeddings, even though the VLM training +process is not explicitly designed for invariant representation learning. These +invariant descriptive features also compose a better input compression scheme. +When combined with finetuning, we show that SLR-AVD is able to outperform +existing state-of-the-art finetuning approaches on both in-distribution and +out-of-distribution performance. +" +DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI,Jianguo Zhang,http://arxiv.org/pdf/2307.10172v2.pdf,2023-07-19,"['cs.cl', 'cs.ai']",2307.10172v2.pdf," Despite advancements in conversational AI, language models encounter +challenges to handle diverse conversational tasks, and existing dialogue +dataset collections often lack diversity and comprehensiveness. To tackle these +issues, we introduce DialogStudio: the largest and most diverse collection of +dialogue datasets, unified under a consistent format while preserving their +original information. Our collection encompasses data from open-domain +dialogues, task-oriented dialogues, natural language understanding, +conversational recommendation, dialogue summarization, and knowledge-grounded +dialogues, making it an incredibly rich and diverse resource for dialogue +research and model training. To further enhance the utility of DialogStudio, we +identify the licenses for each dataset and design domain-aware prompts for +selected dialogues to facilitate instruction-aware fine-tuning. Furthermore, we +develop conversational AI models using the dataset collection, and our +experiments in both zero-shot and few-shot learning scenarios demonstrate the +superiority of DialogStudio. To improve transparency and support dataset and +task-based research, as well as language model pre-training, all datasets, +licenses, codes, and models associated with DialogStudio are made publicly +accessible at https://github.com/salesforce/DialogStudio +" +Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks,Chengguang Gan,http://arxiv.org/pdf/2307.10291v2.pdf,2023-07-18,['cs.cl'],2307.10291v2.pdf," Information extraction(IE) is a crucial subfield within natural language +processing. However, for the traditionally segmented approach to sentence +classification and Named Entity Recognition, the intricate interactions between +these individual subtasks remain largely uninvestigated. In this study, we +propose an integrative analysis, converging sentence classification with Named +Entity Recognition, with the objective to unveil and comprehend the mutual +reinforcement effect within these two information extraction subtasks. To +achieve this, we introduce a Sentence Classification and Named Entity +Recognition Multi-task (SCNM) approach that combines Sentence Classification +(SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label +Generation (SLG) framework for SCNM and construct a Wikipedia dataset +containing both SC and NER. Using a format converter, we unify input formats +and employ a generative model to generate SC-labels, NER-labels, and associated +text segments. We propose a Constraint Mechanism (CM) to improve generated +format accuracy. Our results show SC accuracy increased by 1.13 points and NER +by 1.06 points in SCNM compared to standalone tasks, with CM raising format +accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects +between SC and NER, and integration enhances both tasks' performance. We +additionally implemented the SLG framework on single SC task. It yielded +superior accuracies compared to the baseline on two distinct Japanese SC +datasets. Notably, in the experiment of few-shot learning, SLG framework shows +much better performance than fine-tune method. These empirical findings +contribute additional evidence to affirm the efficacy of the SLG framework. +" +CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study,Zihan Guan,http://arxiv.org/pdf/2307.11346v1.pdf,2023-07-21,"['cs.cl', 'cs.ai']",2307.11346v1.pdf," Participant recruitment based on unstructured medical texts such as clinical +notes and radiology reports has been a challenging yet important task for the +cohort establishment in clinical research. Recently, Large Language Models +(LLMs) such as ChatGPT have achieved tremendous success in various downstream +tasks thanks to their promising performance in language understanding, +inference, and generation. It is then natural to test their feasibility in +solving the cohort recruitment task, which involves the classification of a +given paragraph of medical text into disease label(s). However, when applied to +knowledge-intensive problem settings such as medical text classification, where +the LLMs are expected to understand the decision made by human experts and +accurately identify the implied disease labels, the LLMs show a mediocre +performance. A possible explanation is that, by only using the medical text, +the LLMs neglect to use the rich context of additional information that +languages afford. To this end, we propose to use a knowledge graph as auxiliary +information to guide the LLMs in making predictions. Moreover, to further boost +the LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample +selection strategy enhanced by reinforcement learning, which selects a set of +CoT samples given each individual medical report. Experimental results and +various ablation studies show that our few-shot learning method achieves +satisfactory performance compared with fine-tuning strategies and gains superb +advantages when the available data is limited. The code and sample dataset of +the proposed CohortGPT model is available at: +https://anonymous.4open.science/r/CohortGPT-4872/ +" +Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models,Christos Christodoulou,http://arxiv.org/pdf/2307.12155v1.pdf,2023-07-22,['cs.cl'],2307.12155v1.pdf," Misinformation on YouTube is a significant concern, necessitating robust +detection strategies. In this paper, we introduce a novel methodology for video +classification, focusing on the veracity of the content. We convert the +conventional video classification task into a text classification task by +leveraging the textual content derived from the video transcripts. We employ +advanced machine learning techniques like transfer learning to solve the +classification challenge. Our approach incorporates two forms of transfer +learning: (a) fine-tuning base transformer models such as BERT, RoBERTa, and +ELECTRA, and (b) few-shot learning using sentence-transformers MPNet and +RoBERTa-large. We apply the trained models to three datasets: (a) YouTube +Vaccine-misinformation related videos, (b) YouTube Pseudoscience videos, and +(c) Fake-News dataset (a collection of articles). Including the Fake-News +dataset extended the evaluation of our approach beyond YouTube videos. Using +these datasets, we evaluated the models distinguishing valid information from +misinformation. The fine-tuned models yielded Matthews Correlation +Coefficient>0.81, accuracy>0.90, and F1 score>0.90 in two of three datasets. +Interestingly, the few-shot models outperformed the fine-tuned ones by 20% in +both Accuracy and F1 score for the YouTube Pseudoscience dataset, highlighting +the potential utility of this approach -- especially in the context of limited +training data. +" +ChatGPT for Arabic Grammatical Error Correction,Sang Yun Kwon,http://arxiv.org/pdf/2308.04492v1.pdf,2023-08-08,['cs.ai'],2308.04492v1.pdf," Recently, large language models (LLMs) fine-tuned to follow human instruction +have exhibited significant capabilities in various English NLP tasks. However, +their performance in grammatical error correction (GEC) tasks, particularly in +non-English languages, remains significantly unexplored. In this paper, we +delve into abilities of instruction fine-tuned LLMs in Arabic GEC, a task made +complex due to Arabic's rich morphology. Our findings suggest that various +prompting methods, coupled with (in-context) few-shot learning, demonstrate +considerable effectiveness, with GPT-4 achieving up to $65.49$ +F\textsubscript{1} score under expert prompting (approximately $5$ points +higher than our established baseline). This highlights the potential of LLMs in +low-resource settings, offering a viable approach for generating useful +synthetic data for model training. Despite these positive results, we find that +instruction fine-tuned models, regardless of their size, significantly +underperform compared to fully fine-tuned models of significantly smaller +sizes. This disparity highlights a substantial room for improvements for LLMs. +Inspired by methods from low-resource machine translation, we also develop a +method exploiting synthetic data that significantly outperforms previous models +on two standard Arabic benchmarks. Our work sets new SoTA for Arabic GEC, with +$72.19\%$ and $73.26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively. +" +LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking,Fahim Dalvi,http://arxiv.org/pdf/2308.04945v1.pdf,2023-08-09,"['cs.cl', 'cs.ai', '68t50', 'f.2.2; i.2.7']",2308.04945v1.pdf," The recent development and success of Large Language Models (LLMs) +necessitate an evaluation of their performance across diverse NLP tasks in +different languages. Although several frameworks have been developed and made +publicly available, their customization capabilities for specific tasks and +datasets are often complex for different users. In this study, we introduce the +LLMeBench framework. Initially developed to evaluate Arabic NLP tasks using +OpenAI's GPT and BLOOM models; it can be seamlessly customized for any NLP task +and model, regardless of language. The framework also features zero- and +few-shot learning settings. A new custom dataset can be added in less than 10 +minutes, and users can use their own model API keys to evaluate the task at +hand. The developed framework has been already tested on 31 unique NLP tasks +using 53 publicly available datasets within 90 experimental setups, involving +approximately 296K data points. We plan to open-source the framework for the +community (https://github.com/qcri/LLMeBench/). A video demonstrating the +framework is available online (https://youtu.be/FkQn4UjYA0s). +" +Link-Context Learning for Multimodal LLMs,Yan Tai,http://arxiv.org/pdf/2308.07891v1.pdf,2023-08-15,"['cs.cv', 'cs.cl']",2308.07891v1.pdf," The ability to learn from context with novel concepts, and deliver +appropriate responses are essential in human conversations. Despite current +Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being +trained on mega-scale datasets, recognizing unseen images or understanding +novel concepts in a training-free manner remains a challenge. In-Context +Learning (ICL) explores training-free few-shot learning, where models are +encouraged to ``learn to learn"" from limited tasks and generalize to unseen +tasks. In this work, we propose link-context learning (LCL), which emphasizes +""reasoning from cause and effect"" to augment the learning capabilities of +MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal +relationship between the support set and the query set. By providing +demonstrations with causal links, LCL guides the model to discern not only the +analogy but also the underlying causal associations between data points, which +empowers MLLMs to recognize unseen images and understand novel concepts more +effectively. To facilitate the evaluation of this novel approach, we introduce +the ISEKAI dataset, comprising exclusively of unseen generated image-label +pairs designed for link-context learning. Extensive experiments show that our +LCL-MLLM exhibits strong link-context learning capabilities to novel concepts +over vanilla MLLMs. Code and data will be released at +https://github.com/isekai-portal/Link-Context-Learning. +" +CodeCoT and Beyond: Learning to Program and Test like a Developer,Dong Huang,http://arxiv.org/pdf/2308.08784v1.pdf,2023-08-17,"['cs.se', 'cs.ai']",2308.08784v1.pdf," In natural language processing, transformer-based large language models +(LLMs) like GPT-x models developed by OpenAI have revolutionized the landscape. +Despite their impressive capabilities, these models often encounter challenges +when handling tasks that differ from their training data, resulting in +compromised performance. To address this, few-shot learning has emerged as a +valuable technique, allowing LLMs to adapt with minimal task-specific data. One +innovative strategy, known as Chain-of-Thought Prompting (CoT), has been +introduced to guide LLMs in revealing cognitive processes during multi-step +reasoning. In this paper, we propose Code Chain-of-Thought~(CodeCoT), which +consists of two components: the Vanilla CodeCoT and the Self-exam CodeCoT. The +latter incorporates self-examination, empowering the model to iteratively +generate code, formulate test cases, and refine its outputs. Specifically, the +process entails the generation of test examples by the model corresponding to +the code it is tasked to implement. If it fails on the test examples, then it +regenerates the code based on the erroneous code and associated error types. +Through comprehensive experiments, we observed that both techniques +significantly enhance code generation accuracy across various LLM variants. Our +evaluation results reveal that CodeCoT improves the code generation +effectiveness, including an unprecedented pass@1 accuracy of 79.27\% using the +Self-exam CodeCoT approach on the gpt-3.5-turbo-0613 model in the HumanEval +dataset. +" +Large Language Models Vote: Prompting for Rare Disease Identification,David Oniani,http://arxiv.org/pdf/2308.12890v2.pdf,2023-08-24,"['cs.cl', 'cs.ai']",2308.12890v2.pdf," The emergence of generative Large Language Models (LLMs) emphasizes the need +for accurate and efficient prompting approaches. LLMs are often applied in +Few-Shot Learning (FSL) contexts, where tasks are executed with minimal +training data. FSL has become popular in many Artificial Intelligence (AI) +subdomains, including AI for health. Rare diseases affect a small fraction of +the population. Rare disease identification from clinical notes inherently +requires FSL techniques due to limited data availability. Manual data +collection and annotation is both expensive and time-consuming. In this paper, +we propose Models-Vote Prompting (MVP), a flexible prompting approach for +improving the performance of LLM queries in FSL settings. MVP works by +prompting numerous LLMs to perform the same tasks and then conducting a +majority vote on the resulting outputs. This method achieves improved results +to any one model in the ensemble on one-shot rare disease identification and +classification tasks. We also release a novel rare disease dataset for FSL, +available to those who signed the MIMIC-IV Data Use Agreement (DUA). +Furthermore, in using MVP, each model is prompted multiple times, substantially +increasing the time needed for manual annotation, and to address this, we +assess the feasibility of using JSON for automating generative LLM evaluation. +" +Diagnosing Infeasible Optimization Problems Using Large Language Models,Hao Chen,http://arxiv.org/pdf/2308.12923v1.pdf,2023-08-23,"['cs.hc', 'cs.cl', 'cs.lg', 'math.oc']",2308.12923v1.pdf," Decision-making problems can be represented as mathematical optimization +models, finding wide applications in fields such as economics, engineering and +manufacturing, transportation, and health care. Optimization models are +mathematical abstractions of the problem of making the best decision while +satisfying a set of requirements or constraints. One of the primary barriers to +deploying these models in practice is the challenge of helping practitioners +understand and interpret such models, particularly when they are infeasible, +meaning no decision satisfies all the constraints. Existing methods for +diagnosing infeasible optimization models often rely on expert systems, +necessitating significant background knowledge in optimization. In this paper, +we introduce OptiChat, a first-of-its-kind natural language-based system +equipped with a chatbot GUI for engaging in interactive conversations about +infeasible optimization models. OptiChat can provide natural language +descriptions of the optimization model itself, identify potential sources of +infeasibility, and offer suggestions to make the model feasible. The +implementation of OptiChat is built on GPT-4, which interfaces with an +optimization solver to identify the minimal subset of constraints that render +the entire optimization problem infeasible, also known as the Irreducible +Infeasible Subset (IIS). We utilize few-shot learning, expert chain-of-thought, +key-retrieve, and sentiment prompts to enhance OptiChat's reliability. Our +experiments demonstrate that OptiChat assists both expert and non-expert users +in improving their understanding of the optimization models, enabling them to +quickly identify the sources of infeasibility. +" +Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks,Xiangyang Zhu,http://arxiv.org/pdf/2308.12961v1.pdf,2023-08-24,['cs.cv'],2308.12961v1.pdf," To reduce the reliance on large-scale datasets, recent works in 3D +segmentation resort to few-shot learning. Current 3D few-shot semantic +segmentation methods first pre-train the models on `seen' classes, and then +evaluate their generalization performance on `unseen' classes. However, the +prior pre-training stage not only introduces excessive time overhead, but also +incurs a significant domain gap on `unseen' classes. To tackle these issues, we +propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and +a further training-based variant, TFS3D-T. Without any learnable parameters, +TFS3D extracts dense representations by trigonometric positional encodings, and +achieves comparable performance to previous training-based methods. Due to the +elimination of pre-training, TFS3D can alleviate the domain gap issue and save +a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to +train a lightweight query-support transferring attention (QUEST), which +enhances the interaction between the few-shot query and support data. +Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by ++6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the +training time by -90%, indicating superior effectiveness and efficiency. +" +"LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding",Yushi Bai,http://arxiv.org/pdf/2308.14508v1.pdf,2023-08-28,['cs.cl'],2308.14508v1.pdf," Although large language models (LLMs) demonstrate impressive performance for +many language tasks, most of them can only handle texts a few thousand tokens +long, limiting their applications on longer sequence inputs, such as books, +reports, and codebases. Recent works have proposed methods to improve LLMs' +long context capabilities by extending context windows and more sophisticated +memory mechanisms. However, comprehensive benchmarks tailored for evaluating +long context understanding are lacking. In this paper, we introduce LongBench, +the first bilingual, multi-task benchmark for long context understanding, +enabling a more rigorous evaluation of long context understanding. LongBench +comprises 21 datasets across 6 task categories in both English and Chinese, +with an average length of 6,711 words (English) and 13,386 characters +(Chinese). These tasks cover key long-text application areas including +single-doc QA, multi-doc QA, summarization, few-shot learning, synthetic tasks, +and code completion. All datasets in LongBench are standardized into a unified +format, allowing for effortless automatic evaluation of LLMs. Upon +comprehensive evaluation of 8 LLMs on LongBench, we find that: (1) Commercial +model (GPT-3.5-Turbo-16k) outperforms other open-sourced models, but still +struggles on longer contexts. (2) Scaled position embedding and fine-tuning on +longer sequences lead to substantial improvement on long context understanding. +(3) Context compression technique such as retrieval brings improvement for +model with weak ability on long contexts, but the performance still lags behind +models that have strong long context understanding capability. The code and +datasets are available at https://github.com/THUDM/LongBench. +" +TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification,Jianing Wang,http://arxiv.org/pdf/2308.15010v1.pdf,2023-08-29,['cs.cl'],2308.15010v1.pdf," Text classification is one of the most imperative tasks in natural language +processing (NLP). Recent advances with pre-trained language models (PLMs) have +shown remarkable success on this task. However, the satisfying results obtained +by PLMs heavily depend on the large amounts of task-specific labeled data, +which may not be feasible in many application scenarios due to data access and +privacy constraints. The recently-proposed prompt-based fine-tuning paradigm +improves the performance of PLMs for few-shot text classification with +task-specific templates. Yet, it is unclear how the prompting knowledge can be +transferred across tasks, for the purpose of mutual reinforcement. We propose +TransPrompt v2, a novel transferable prompting framework for few-shot learning +across similar or distant text classification tasks. For learning across +similar tasks, we employ a multi-task meta-knowledge acquisition (MMA) +procedure to train a meta-learner that captures the cross-task transferable +knowledge. For learning across distant tasks, we further inject the task type +descriptions into the prompt, and capture the intra-type and inter-type prompt +embeddings among multiple distant tasks. Additionally, two de-biasing +techniques are further designed to make the trained meta-learner more +task-agnostic and unbiased towards any tasks. After that, the meta-learner can +be adapted to each specific task with better parameters initialization. +Extensive experiments show that TransPrompt v2 outperforms single-task and +cross-task strong baselines over multiple NLP tasks and datasets. We further +show that the meta-learner can effectively improve the performance of PLMs on +previously unseen tasks. In addition, TransPrompt v2 also outperforms strong +fine-tuning baselines when learning with full training sets. +" +AskIt: Unified Programming Interface for Programming with Large Language Models,Katsumi Okuda,http://arxiv.org/pdf/2308.15645v1.pdf,2023-08-29,"['cs.pl', 'cs.ai', 'cs.se']",2308.15645v1.pdf," In the evolving landscape of software development, Large Language Models +(LLMs) exhibit a unique phenomenon known as emergent abilities, demonstrating +adeptness across numerous tasks, from text summarization to code generation. +While these abilities open up novel avenues in software design and crafting, +their incorporation presents substantial challenges. Developers grapple with +decisions surrounding the direct embedding of LLMs within applications versus +employing them for code generation. Moreover, effective prompt design becomes a +critical concern, given the necessity of data extraction from natural language +outputs. To address these intricacies, this paper introduces AskIt, a +domain-specific language (DSL) specifically designed for LLMs. AskIt simplifies +LLM integration, offering type-guided output control, template-based function +definitions, and a unified interface that diminishes the distinction between +LLM-based code generation and application integration. Furthermore, through +Programming by Example (PBE), AskIt harnesses the power of few-shot learning at +the programming language level. Our evaluations underscore AskIt's potency. +Across 50 tasks, AskIt generated concise prompts for the given tasks, achieving +a 16.14% reduction in prompt length relative to benchmarks. Additionally, by +enabling the transition from direct LLM application usage to function +generation, AskIt achieved significant speedups, as observed in our GSM8K +benchmark experiments. Through these advancements, AskIt streamlines the +integration of LLMs in software development, offering a more efficient, +versatile approach for leveraging emergent abilities. The implementations of +AskIt in TypeScript and Python are available at +https://github.com/katsumiok/ts-askit and https://github.com/katsumiok/pyaskit, +respectively. +" +Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning,Yiming Zhang,http://arxiv.org/pdf/2308.16466v3.pdf,2023-08-31,['cs.cv'],2308.16466v3.pdf," While the Segment Anything Model (SAM) excels in semantic segmentation for +general-purpose images, its performance significantly deteriorates when applied +to medical images, primarily attributable to insufficient representation of +medical images in its training dataset. Nonetheless, gathering comprehensive +datasets and training models that are universally applicable is particularly +challenging due to the long-tail problem common in medical images. To address +this gap, here we present a Self-Sampling Meta SAM (SSM-SAM) framework for +few-shot medical image segmentation. Our innovation lies in the design of three +key modules: 1) An online fast gradient descent optimizer, further optimized by +a meta-learner, which ensures swift and robust adaptation to new tasks. 2) A +Self-Sampling module designed to provide well-aligned visual prompts for +improved attention allocation; and 3) A robust attention-based decoder +specifically designed for medical few-shot learning to capture relationship +between different slices. Extensive experiments on a popular abdominal CT +dataset and an MRI dataset demonstrate that the proposed method achieves +significant improvements over state-of-the-art methods in few-shot +segmentation, with an average improvements of 10.21% and 1.80% in terms of DSC, +respectively. In conclusion, we present a novel approach for rapid online +adaptation in interactive image segmentation, adapting to a new organ in just +0.83 minutes. Code is publicly available on GitHub upon acceptance. +" +Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs,Xuanwen Huang,http://arxiv.org/pdf/2309.02848v1.pdf,2023-09-06,['cs.si'],2309.02848v1.pdf," Text-attributed Graphs (TAGs) are commonly found in the real world, such as +social networks and citation networks, and consist of nodes represented by +textual descriptions. Currently, mainstream machine learning methods on TAGs +involve a two-stage modeling approach: (1) unsupervised node feature extraction +with pre-trained language models (PLMs); and (2) supervised learning using +Graph Neural Networks (GNNs). However, we observe that these representations, +which have undergone large-scale pre-training, do not significantly improve +performance with a limited amount of training samples. The main issue is that +existing methods have not effectively integrated information from the graph and +downstream tasks simultaneously. In this paper, we propose a novel framework +called G-Prompt, which combines a graph adapter and task-specific prompts to +extract node features. First, G-Prompt introduces a learnable GNN layer +(\emph{i.e.,} adaptor) at the end of PLMs, which is fine-tuned to better +capture the masked tokens considering graph neighborhood information. After the +adapter is trained, G-Prompt incorporates task-specific prompts to obtain +\emph{interpretable} node representations for the downstream task. Our +experiment results demonstrate that our proposed method outperforms current +state-of-the-art (SOTA) methods on few-shot node classification. More +importantly, in zero-shot settings, the G-Prompt embeddings can not only +provide better task interpretability than vanilla PLMs but also achieve +comparable performance with fully-supervised baselines. +" +Cross-Image Context Matters for Bongard Problems,Nikhil Raghuraman,http://arxiv.org/pdf/2309.03468v1.pdf,2023-09-07,"['cs.cv', 'cs.ai', 'cs.lg']",2309.03468v1.pdf," Current machine learning methods struggle to solve Bongard problems, which +are a type of IQ test that requires deriving an abstract ""concept"" from a set +of positive and negative ""support"" images, and then classifying whether or not +a new query image depicts the key concept. On Bongard-HOI, a benchmark for +natural-image Bongard problems, existing methods have only reached 66% accuracy +(where chance is 50%). Low accuracy is often attributed to neural nets' lack of +ability to find human-like symbolic rules. In this work, we point out that many +existing methods are forfeiting accuracy due to a much simpler problem: they do +not incorporate information contained in the support set as a whole, and rely +instead on information extracted from individual supports. This is a critical +issue, because unlike in few-shot learning tasks concerning object +classification, the ""key concept"" in a typical Bongard problem can only be +distinguished using multiple positives and multiple negatives. We explore a +variety of simple methods to take this cross-image context into account, and +demonstrate substantial gains over prior methods, leading to new +state-of-the-art performance on Bongard-LOGO (75.3%) and Bongard-HOI (72.45%) +and strong performance on the original Bongard problem set (60.84%). +" +DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning,Zhengxiang Shi,http://arxiv.org/pdf/2309.05173v2.pdf,2023-09-11,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2309.05173v2.pdf," Prompt tuning (PT), where a small amount of trainable soft (continuous) +prompt vectors is affixed to the input of language models (LM), has shown +promising results across various tasks and models for parameter-efficient +fine-tuning (PEFT). PT stands out from other PEFT approaches because it +maintains competitive performance with fewer trainable parameters and does not +drastically scale up its parameters as the model size expands. However, PT +introduces additional soft prompt tokens, leading to longer input sequences, +which significantly impacts training and inference time and memory usage due to +the Transformer's quadratic complexity. Particularly concerning for Large +Language Models (LLMs) that face heavy daily querying. To address this issue, +we propose Decomposed Prompt Tuning (DePT), which decomposes the soft prompt +into a shorter soft prompt and a pair of low-rank matrices that are then +optimised with two different learning rates. This allows DePT to achieve better +performance while saving over 20% memory and time costs compared to vanilla PT +and its variants, without changing trainable parameter sizes. Through extensive +experiments on 23 natural language processing (NLP) and vision-language (VL) +tasks, we demonstrate that DePT outperforms state-of-the-art PEFT approaches, +including the full fine-tuning baseline in some scenarios. Additionally, we +empirically show that DEPT grows more efficient as the model size increases. +Our further study reveals that DePT integrates seamlessly with +parameter-efficient transfer learning in the few-shot learning setting and +highlights its adaptability to various model architectures and sizes. +" +Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model,Neel Bhate,http://arxiv.org/pdf/2309.05475v2.pdf,2023-09-11,['cs.cl'],2309.05475v2.pdf," Demographics, Social determinants of health, and family history documented in +the unstructured text within the electronic health records are increasingly +being studied to understand how this information can be utilized with the +structured data to improve healthcare outcomes. After the GPT models were +released, many studies have applied GPT models to extract this information from +the narrative clinical notes. Different from the existing work, our research +focuses on investigating the zero-shot learning on extracting this information +together by providing minimum information to the GPT model. We utilize +de-identified real-world clinical notes annotated for demographics, various +social determinants, and family history information. Given that the GPT model +might provide text different from the text in the original data, we explore two +sets of evaluation metrics, including the traditional NER evaluation metrics +and semantic similarity evaluation metrics, to completely understand the +performance. Our results show that the GPT-3.5 method achieved an average of +0.975 F1 on demographics extraction, 0.615 F1 on social determinants +extraction, and 0.722 F1 on family history extraction. We believe these results +can be further improved through model fine-tuning or few-shots learning. +Through the case studies, we also identified the limitations of the GPT models, +which need to be addressed in future research. +" +GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection,Yufei Li,http://arxiv.org/pdf/2309.05953v1.pdf,2023-09-12,"['cs.lg', 'cs.ir']",2309.05953v1.pdf," Logs play a crucial role in system monitoring and debugging by recording +valuable system information, including events and states. Although various +methods have been proposed to detect anomalies in log sequences, they often +overlook the significance of considering relations among system components, +such as services and users, which can be identified from log contents. +Understanding these relations is vital for detecting anomalies and their +underlying causes. To address this issue, we introduce GLAD, a Graph-based Log +Anomaly Detection framework designed to detect relational anomalies in system +logs. GLAD incorporates log semantics, relational patterns, and sequential +patterns into a unified framework for anomaly detection. Specifically, GLAD +first introduces a field extraction module that utilizes prompt-based few-shot +learning to identify essential fields from log contents. Then GLAD constructs +dynamic log graphs for sliding windows by interconnecting extracted fields and +log events parsed from the log parser. These graphs represent events and fields +as nodes and their relations as edges. Subsequently, GLAD utilizes a +temporal-attentive graph edge anomaly detection model for identifying anomalous +relations in these dynamic log graphs. This model employs a Graph Neural +Network (GNN)-based encoder enhanced with transformers to capture content, +structural and temporal features. We evaluate our proposed method on three +datasets, and the results demonstrate the effectiveness of GLAD in detecting +anomalies indicated by varying relational patterns. +" +Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level,Jingzhe Ding,http://arxiv.org/pdf/2309.08182v2.pdf,2023-09-15,"['cs.cl', 'cs.ai', 'i.2.7']",2309.08182v2.pdf," Our work demonstrates that large language model (LLM) pre-trained on texts +can not only solve pure math word problems, but also physics word problems, +whose solution requires calculation and inference based on prior physical +knowledge. We collect and annotate the first physics word problem +dataset-PhysQA, which contains over 1000 junior high school physics word +problems (covering Kinematics, Mass&Density, Mechanics, Heat, Electricity). +Then we use OpenAI' s GPT3.5 to generate the answer of these problems and found +that GPT3.5 could automatically solve 49.3% of the problems through zero-shot +learning and 73.2% through few-shot learning. This result demonstrates that by +using similar problems and their answers as prompt, LLM could solve elementary +physics word problems approaching human level performance. In addition to +solving problems, GPT3.5 can also summarize the knowledge or topics covered by +the problems, provide relevant explanations, and generate new physics word +problems based on the input. Our work is the first research to focus on the +automatic solving, explanation, and generation of physics word problems across +various types and scenarios, and we achieve an acceptable and state-of-the-art +accuracy. This underscores the potential of LLMs for further applications in +secondary education. +" +SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels,Henry Hengyuan Zhao,http://arxiv.org/pdf/2309.08513v2.pdf,2023-09-15,"['cs.cv', 'cs.ai']",2309.08513v2.pdf," Pre-trained vision transformers have strong representation benefits to +various downstream tasks. Recently, many parameter-efficient fine-tuning (PEFT) +methods have been proposed, and their experiments demonstrate that tuning only +1% of extra parameters could surpass full fine-tuning in low-data resource +scenarios. However, these methods overlook the task-specific information when +fine-tuning diverse downstream tasks. In this paper, we propose a simple yet +effective method called ""Salient Channel Tuning"" (SCT) to leverage the +task-specific information by forwarding the model with the task images to +select partial channels in a feature map that enables us to tune only 1/8 +channels leading to significantly lower parameter costs. Experiments outperform +full fine-tuning on 18 out of 19 tasks in the VTAB-1K benchmark by adding only +0.11M parameters of the ViT-B, which is 780$\times$ fewer than its full +fine-tuning counterpart. Furthermore, experiments on domain generalization and +few-shot learning surpass other PEFT methods with lower parameter costs, +demonstrating our proposed tuning technique's strong capability and +effectiveness in the low-data regime. +" +nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance,Yunxiang Li,http://arxiv.org/pdf/2309.16967v2.pdf,2023-09-29,"['cs.cv', 'eess.iv']",2309.16967v2.pdf," The recent developments of foundation models in computer vision, especially +the Segment Anything Model (SAM), allow scalable and domain-agnostic image +segmentation to serve as a general-purpose segmentation tool. In parallel, the +field of medical image segmentation has benefited significantly from +specialized neural networks like the nnUNet, which is trained on +domain-specific datasets and can automatically configure the network to tailor +to specific segmentation challenges. To combine the advantages of foundation +models and domain-specific models, we present nnSAM, which synergistically +integrates the SAM model with the nnUNet model to achieve more accurate and +robust medical image segmentation. The nnSAM model leverages the powerful and +robust feature extraction capabilities of SAM, while harnessing the automatic +configuration capabilities of nnUNet to promote dataset-tailored learning. Our +comprehensive evaluation of nnSAM model on different sizes of training samples +shows that it allows few-shot learning, which is highly relevant for medical +image segmentation where high-quality, annotated data can be scarce and costly +to obtain. By melding the strengths of both its predecessors, nnSAM positions +itself as a potential new benchmark in medical image segmentation, offering a +tool that combines broad applicability with specialized efficiency. The code is +available at https://github.com/Kent0n-Li/Medical-Image-Segmentation. +" +An evaluation of GPT models for phenotype concept recognition,Tudor Groza,http://arxiv.org/pdf/2309.17169v1.pdf,2023-09-29,"['cs.cl', 'cs.ai']",2309.17169v1.pdf," Objective: Clinical deep phenotyping plays a critical role in both the +diagnosis of patients with rare disorders as well as in building care +coordination plans. The process relies on modelling and curating patient +profiles using ontology concepts, usually from the Human Phenotype Ontology. +Machine learning methods have been widely adopted to support this phenotype +concept recognition task. With the significant shift in the use of large +language models (LLMs) for most NLP tasks, herewithin, we examine the +performance of the latest Generative Pre-trained Transformer (GPT) models +underpinning ChatGPT in clinical deep phenotyping. Materials and Methods: The +experimental setup of the study included seven prompts of various levels of +specificity, two GPT models (gpt-3.5 and gpt-4.0) and an established gold +standard for phenotype recognition. Results: Our results show that, currently, +these models have not yet achieved state of the art performance. The best run, +using few-shots learning, achieved 0.41 F1 score, compared to a 0.62 F1 score +achieved by the current best in class tool. Conclusion: The non-deterministic +nature of the outcomes and the lack of concordance between different runs using +the same prompt and input makes the use of these LLMs in clinical settings +problematic. +" +RA-DIT: Retrieval-Augmented Dual Instruction Tuning,Xi Victoria Lin,http://arxiv.org/pdf/2310.01352v3.pdf,2023-10-02,"['cs.cl', 'cs.ai']",2310.01352v3.pdf," Retrieval-augmented language models (RALMs) improve performance by accessing +long-tail and up-to-date knowledge from external data stores, but are +challenging to build. Existing approaches require either expensive +retrieval-specific modifications to LM pre-training or use post-hoc integration +of the data store that leads to suboptimal performance. We introduce +Retrieval-Augmented Dual Instruction Tuning (RA-DIT), a lightweight fine-tuning +methodology that provides a third option by retrofitting any LLM with retrieval +capabilities. Our approach operates in two distinct fine-tuning steps: (1) one +updates a pre-trained LM to better use retrieved information, while (2) the +other updates the retriever to return more relevant results, as preferred by +the LM. By fine-tuning over tasks that require both knowledge utilization and +contextual awareness, we demonstrate that each stage yields significant +performance improvements, and using both leads to additional gains. Our best +model, RA-DIT 65B, achieves state-of-the-art performance across a range of +knowledge-intensive zero- and few-shot learning benchmarks, significantly +outperforming existing in-context RALM approaches by up to +8.9% in 0-shot +setting and +1.4% in 5-shot setting on average. +" +UniPredict: Large Language Models are Universal Tabular Predictors,Ruiyu Wang,http://arxiv.org/pdf/2310.03266v1.pdf,2023-10-05,['cs.lg'],2310.03266v1.pdf," Tabular data prediction is a fundamental machine learning task for many +applications. Existing methods predominantly employ discriminative modeling and +operate under the assumption of a fixed target column, necessitating +re-training for every new predictive task. Inspired by the generative power of +large language models (LLMs), this paper exploits the idea of building +universal tabular data predictors based on generative modeling, namely +UniPredict. Here, we show that scaling up an LLM to extensive tabular datasets +with the capability of comprehending diverse tabular inputs and predicting for +target variables following the input instructions. Specifically, we train a +single LLM on an aggregation of 169 tabular datasets with diverse targets and +compare its performance against baselines that are trained on each dataset +separately. We observe this versatile UniPredict model demonstrates an +advantage over other models, ranging from 5.4% to 13.4%, when compared with the +best tree-boosting baseline and the best neural network baseline, respectively. +We further test UniPredict in few-shot learning settings on another 62 tabular +datasets. Our method achieves strong performance in quickly adapting to new +tasks, where our method outperforms XGBoost over 100% on the low-resource setup +and shows a significant margin over all baselines. We envision that UniPredict +sheds light on developing a universal tabular data prediction system that +learns from data at scale and serves a wide range of prediction tasks. +" +LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression,Huiqiang Jiang,http://arxiv.org/pdf/2310.06839v1.pdf,2023-10-10,"['cs.cl', 'cs.lg']",2310.06839v1.pdf," In long context scenarios, large language models (LLMs) face three main +challenges: higher computational/financial cost, longer latency, and inferior +performance. Some studies reveal that the performance of LLMs depends on both +the density and the position of the key information (question relevant) in the +input prompt. Inspired by these findings, we propose LongLLMLingua for prompt +compression towards improving LLMs' perception of the key information to +simultaneously address the three challenges. We conduct evaluation on a wide +range of long context scenarios including single-/multi-document QA, few-shot +learning, summarization, synthetic tasks, and code completion. The experimental +results show that LongLLMLingua compressed prompt can derive higher performance +with much less cost. The latency of the end-to-end system is also reduced. For +example, on NaturalQuestions benchmark, LongLLMLingua gains a performance boost +of up to 17.1% over the original prompt with ~4x fewer tokens as input to +GPT-3.5-Turbo. It can derive cost savings of \$28.5 and \$27.4 per 1,000 +samples from the LongBench and ZeroScrolls benchmark, respectively. +Additionally, when compressing prompts of ~10k tokens at a compression rate of +2x-10x, LongLLMLingua can speed up the end-to-end latency by 1.4x-3.8x. Our +code is available at https://aka.ms/LLMLingua. +" +Empower Text-Attributed Graphs Learning with Large Language Models (LLMs),Jianxiang Yu,http://arxiv.org/pdf/2310.09872v1.pdf,2023-10-15,['cs.lg'],2310.09872v1.pdf," Text-attributed graphs have recently garnered significant attention due to +their wide range of applications in web domains. Existing methodologies employ +word embedding models for acquiring text representations as node features, +which are subsequently fed into Graph Neural Networks (GNNs) for training. +Recently, the advent of Large Language Models (LLMs) has introduced their +powerful capabilities in information retrieval and text generation, which can +greatly enhance the text attributes of graph data. Furthermore, the acquisition +and labeling of extensive datasets are both costly and time-consuming +endeavors. Consequently, few-shot learning has emerged as a crucial problem in +the context of graph learning tasks. In order to tackle this challenge, we +propose a lightweight paradigm called ENG, which adopts a plug-and-play +approach to empower text-attributed graphs through node generation using LLMs. +Specifically, we utilize LLMs to extract semantic information from the labels +and generate samples that belong to these categories as exemplars. +Subsequently, we employ an edge predictor to capture the structural information +inherent in the raw dataset and integrate the newly generated samples into the +original graph. This approach harnesses LLMs for enhancing class-level +information and seamlessly introduces labeled nodes and edges without modifying +the raw dataset, thereby facilitating the node classification task in few-shot +scenarios. Extensive experiments demonstrate the outstanding performance of our +proposed paradigm, particularly in low-shot scenarios. For instance, in the +1-shot setting of the ogbn-arxiv dataset, ENG achieves a 76% improvement over +the baseline model. +" +In-Context Learning with Iterative Demonstration Selection,Chengwei Qin,http://arxiv.org/pdf/2310.09881v2.pdf,2023-10-15,"['cs.cl', 'cs.ai']",2310.09881v2.pdf," Spurred by advancements in scale, large language models (LLMs) have +demonstrated strong few-shot learning ability via in-context learning (ICL). +However, the performance of ICL has been shown to be highly sensitive to the +selection of few-shot demonstrations. Selecting the most suitable examples as +context remains an ongoing challenge and an open problem. Existing literature +has highlighted the importance of selecting examples that are diverse or +semantically similar to the test sample while ignoring the fact that the +optimal selection dimension, i.e., diversity or similarity, is task-specific. +Leveraging the merits of both dimensions, we propose Iterative Demonstration +Selection (IDS). Using zero-shot chain-of-thought reasoning (Zero-shot-CoT), +IDS iteratively selects examples that are diverse but still strongly correlated +with the test sample as ICL demonstrations. Specifically, IDS applies +Zero-shot-CoT to the test sample before demonstration selection. The output +reasoning path is then used to choose demonstrations that are prepended to the +test sample for inference. The generated answer is accompanied by its +corresponding reasoning path for extracting a new set of demonstrations in the +next iteration. After several iterations, IDS adopts majority voting to obtain +the final result. Through extensive experiments on tasks including commonsense +reasoning, question answering, topic classification, and sentiment analysis, we +demonstrate that IDS can consistently outperform existing ICL demonstration +selection methods. +" +The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages,Chiyu Zhang,http://arxiv.org/pdf/2310.14557v1.pdf,2023-10-23,['cs.cl'],2310.14557v1.pdf," Instruction tuned large language models (LLMs), such as ChatGPT, demonstrate +remarkable performance in a wide range of tasks. Despite numerous recent +studies that examine the performance of instruction-tuned LLMs on various NLP +benchmarks, there remains a lack of comprehensive investigation into their +ability to understand cross-lingual sociopragmatic meaning (SM), i.e., meaning +embedded within social and interactive contexts. This deficiency arises partly +from SM not being adequately represented in any of the existing benchmarks. To +address this gap, we present SPARROW, an extensive multilingual benchmark +specifically designed for SM understanding. SPARROW comprises 169 datasets +covering 13 task types across six primary categories (e.g., anti-social +language detection, emotion recognition). SPARROW datasets encompass 64 +different languages originating from 12 language families representing 16 +writing scripts. We evaluate the performance of various multilingual pretrained +language models (e.g., mT5) and instruction-tuned LLMs (e.g., BLOOMZ, ChatGPT) +on SPARROW through fine-tuning, zero-shot, and/or few-shot learning. Our +comprehensive analysis reveals that existing open-source instruction tuned LLMs +still struggle to understand SM across various languages, performing close to a +random baseline in some cases. We also find that although ChatGPT outperforms +many LLMs, it still falls behind task-specific finetuned models with a gap of +12.19 SPARROW score. Our benchmark is available at: +https://github.com/UBC-NLP/SPARROW +" +PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent,Guangliang Liu,http://arxiv.org/pdf/2310.17588v1.pdf,2023-10-26,"['cs.lg', 'cs.cl']",2310.17588v1.pdf," Fine-tuning pretrained language models (PLMs) for downstream tasks is a +large-scale optimization problem, in which the choice of the training algorithm +critically determines how well the trained model can generalize to unseen test +data, especially in the context of few-shot learning. To achieve good +generalization performance and avoid overfitting, techniques such as data +augmentation and pruning are often applied. However, adding these +regularizations necessitates heavy tuning of the hyperparameters of +optimization algorithms, such as the popular Adam optimizer. In this paper, we +propose a two-stage fine-tuning method, PAC-tuning, to address this +optimization challenge. First, based on PAC-Bayes training, PAC-tuning directly +minimizes the PAC-Bayes generalization bound to learn proper parameter +distribution. Second, PAC-tuning modifies the gradient by injecting noise with +the variance learned in the first stage into the model parameters during +training, resulting in a variant of perturbed gradient descent (PGD). In the +past, the few-shot scenario posed difficulties for PAC-Bayes training because +the PAC-Bayes bound, when applied to large models with limited training data, +might not be stringent. Our experimental results across 5 GLUE benchmark tasks +demonstrate that PAC-tuning successfully handles the challenges of fine-tuning +tasks and outperforms strong baseline methods by a visible margin, further +confirming the potential to apply PAC training for any other settings where the +Adam optimizer is currently used for training. +" +Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning,Ruizhe Shi,http://arxiv.org/pdf/2310.20587v3.pdf,2023-10-31,['cs.lg'],2310.20587v3.pdf," Offline reinforcement learning (RL) aims to find a near-optimal policy using +pre-collected datasets. In real-world scenarios, data collection could be +costly and risky; therefore, offline RL becomes particularly challenging when +the in-domain data is limited. Given recent advances in Large Language Models +(LLMs) and their few-shot learning prowess, this paper introduces +$\textbf{La}$nguage Models for $\textbf{Mo}$tion Control ($\textbf{LaMo}$), a +general framework based on Decision Transformers to effectively use pre-trained +Language Models (LMs) for offline RL. Our framework highlights four crucial +components: (1) Initializing Decision Transformers with sequentially +pre-trained LMs, (2) employing the LoRA fine-tuning method, in contrast to +full-weight fine-tuning, to combine the pre-trained knowledge from LMs and +in-domain knowledge effectively, (3) using the non-linear MLP transformation +instead of linear projections, to generate embeddings, and (4) integrating an +auxiliary language prediction loss during fine-tuning to stabilize the LMs and +retain their original abilities on languages. Empirical results indicate +$\textbf{LaMo}$ achieves state-of-the-art performance in sparse-reward tasks +and closes the gap between value-based offline RL methods and decision +transformers in dense-reward tasks. In particular, our method demonstrates +superior performance in scenarios with limited data samples. Our project +website is $\href{https://lamo2023.github.io}{\text{this https URL}}$. +" +On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval,Jiayi Chen,http://arxiv.org/pdf/2311.00693v1.pdf,2023-11-01,['cs.ai'],2311.00693v1.pdf," Visually-rich document entity retrieval (VDER), which extracts key +information (e.g. date, address) from document images like invoices and +receipts, has become an important topic in industrial NLP applications. The +emergence of new document types at a constant pace, each with its unique entity +types, presents a unique challenge: many documents contain unseen entity types +that occur only a couple of times. Addressing this challenge requires models to +have the ability of learning entities in a few-shot manner. However, prior +works for Few-shot VDER mainly address the problem at the document level with a +predefined global entity space, which doesn't account for the entity-level +few-shot scenario: target entity types are locally personalized by each task +and entity occurrences vary significantly among documents. To address this +unexplored scenario, this paper studies a novel entity-level few-shot VDER +task. The challenges lie in the uniqueness of the label space for each task and +the increased complexity of out-of-distribution (OOD) contents. To tackle this +novel task, we present a task-aware meta-learning based framework, with a +central focus on achieving effective task personalization that distinguishes +between in-task and out-of-task distribution. Specifically, we adopt a +hierarchical decoder (HC) and employ contrastive learning (ContrastProtoNet) to +achieve this goal. Furthermore, we introduce a new dataset, FewVEX, to boost +future research in the field of entity-level few-shot VDER. Experimental +results demonstrate our approaches significantly improve the robustness of +popular meta-learning baselines. +" +A Survey of Large Language Models for Autonomous Driving,Zhenjie Yang,http://arxiv.org/pdf/2311.01043v1.pdf,2023-11-02,['cs.ai'],2311.01043v1.pdf," Autonomous driving technology, a catalyst for revolutionizing transportation +and urban mobility, has the tend to transition from rule-based systems to +data-driven strategies. Traditional module-based systems are constrained by +cumulative errors among cascaded modules and inflexible pre-set rules. In +contrast, end-to-end autonomous driving systems have the potential to avoid +error accumulation due to their fully data-driven training process, although +they often lack transparency due to their ``black box"" nature, complicating the +validation and traceability of decisions. Recently, large language models +(LLMs) have demonstrated abilities including understanding context, logical +reasoning, and generating answers. A natural thought is to utilize these +abilities to empower autonomous driving. By combining LLM with foundation +vision models, it could open the door to open-world understanding, reasoning, +and few-shot learning, which current autonomous driving systems are lacking. In +this paper, we systematically review a research line about \textit{Large +Language Models for Autonomous Driving (LLM4AD)}. This study evaluates the +current state of technological advancements, distinctly outlining the principal +challenges and prospective directions for the field. For the convenience of +researchers in academia and industry, we provide real-time updates on the +latest advances in the field as well as relevant open-source resources via the +designated link: https://github.com/Thinklab-SJTU/Awesome-LLM4AD. +" +Robust Fine-Tuning of Vision-Language Models for Domain Generalization,Kevin Vogt-Lowell,http://arxiv.org/pdf/2311.02236v1.pdf,2023-11-03,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2311.02236v1.pdf," Transfer learning enables the sharing of common knowledge among models for a +variety of downstream tasks, but traditional methods suffer in limited training +data settings and produce narrow models incapable of effectively generalizing +under distribution shifts. Foundation models have recently demonstrated +impressive zero-shot inference capabilities and robustness under distribution +shifts. However, zero-shot evaluation for these models has been predominantly +confined to benchmarks with simple distribution shifts, limiting our +understanding of their effectiveness under the more realistic shifts found in +practice. Moreover, common fine-tuning methods for these models have yet to be +evaluated against vision models in few-shot scenarios where training data is +limited. To address these gaps, we present a new recipe for few-shot +fine-tuning of the popular vision-language foundation model CLIP and evaluate +its performance on challenging benchmark datasets with realistic distribution +shifts from the WILDS collection. Our experimentation demonstrates that, while +zero-shot CLIP fails to match performance of trained vision models on more +complex benchmarks, few-shot CLIP fine-tuning outperforms its vision-only +counterparts in terms of in-distribution and out-of-distribution accuracy at +all levels of training data availability. This provides a strong incentive for +adoption of foundation models within few-shot learning applications operating +with real-world data. Code is available at +https://github.com/mit-ll/robust-vision-language-finetuning +" +"A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics",Qing Li,http://arxiv.org/pdf/2103.01403v3.pdf,2021-03-02,"['cs.lg', 'cs.ai', 'cs.cv']",2103.01403v3.pdf," Inspired by humans' exceptional ability to master arithmetic and generalize +to new problems, we present a new dataset, Handwritten arithmetic with INTegers +(HINT), to examine machines' capability of learning generalizable concepts at +three levels: perception, syntax, and semantics. In HINT, machines are tasked +with learning how concepts are perceived from raw signals such as images (i.e., +perception), how multiple concepts are structurally combined to form a valid +expression (i.e., syntax), and how concepts are realized to afford various +reasoning tasks (i.e., semantics), all in a weakly supervised manner. Focusing +on systematic generalization, we carefully design a five-fold test set to +evaluate both the interpolation and the extrapolation of learned concepts +w.r.t. the three levels. Further, we design a few-shot learning split to +determine whether or not models can rapidly learn new concepts and generalize +them to more complex scenarios. To comprehend existing models' limitations, we +undertake extensive experiments with various sequence-to-sequence models, +including RNNs, Transformers, and GPT-3 (with the chain of thought prompting). +The results indicate that current models struggle to extrapolate to long-range +syntactic dependency and semantics. Models exhibit a considerable gap toward +human-level generalization when evaluated with new concepts in a few-shot +setting. Moreover, we discover that it is infeasible to solve HINT by merely +scaling up the dataset and the model size; this strategy contributes little to +the extrapolation of syntax and semantics. Finally, in zero-shot GPT-3 +experiments, the chain of thought prompting exhibits impressive results and +significantly boosts the test accuracy. We believe the HINT dataset and the +experimental findings are of great interest to the learning community on +systematic generalization. +" +Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video,Sodiq Adewole,http://arxiv.org/pdf/2101.04240v2.pdf,2021-01-11,['cs.cv'],2101.04240v2.pdf," Effective and rapid detection of lesions in the Gastrointestinal tract is +critical to gastroenterologist's response to some life-threatening diseases. +Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopy +procedure by allowing gastroenterologists visualize the entire GI tract +non-invasively. Once the tiny capsule is swallowed, it sequentially capture +images of the GI tract at about 2 to 6 frames per second (fps). A single video +can last up to 8 hours producing between 30,000 to 100,000 images. Automating +the detection of frames containing specific lesion in WCE video would relieve +gastroenterologists the arduous task of reviewing the entire video before +making diagnosis. While the WCE produces large volume of images, only about 5\% +of the frames contain lesions that aid the diagnosis process. Convolutional +Neural Network (CNN) based models have been very successful in various image +classification tasks. However, they suffer excessive parameters, are sample +inefficient and rely on very large amount of training data. Deploying a CNN +classifier for lesion detection task will require time-to-time fine-tuning to +generalize to any unforeseen category. In this paper, we propose a metric-based +learning framework followed by a few-shot lesion recognition in WCE data. +Metric-based learning is a meta-learning framework designed to establish +similarity or dissimilarity between concepts while few-shot learning (FSL) aims +to identify new concepts from only a small number of examples. We train a +feature extractor to learn a representation for different small bowel lesions +using metric-based learning. At the testing stage, the category of an unseen +sample is predicted from only a few support examples, thereby allowing the +model to generalize to a new category that has never been seen before. We +demonstrated the efficacy of this method on real patient capsule endoscopy +data. +" +Program Synthesis with Large Language Models,Jacob Austin,http://arxiv.org/pdf/2108.07732v1.pdf,2021-08-16,"['cs.pl', 'cs.lg']",2108.07732v1.pdf," This paper explores the limits of the current generation of large language +models for program synthesis in general purpose programming languages. We +evaluate a collection of such models (with between 244M and 137B parameters) on +two new benchmarks, MBPP and MathQA-Python, in both the few-shot and +fine-tuning regimes. Our benchmarks are designed to measure the ability of +these models to synthesize short Python programs from natural language +descriptions. The Mostly Basic Programming Problems (MBPP) dataset contains 974 +programming tasks, designed to be solvable by entry-level programmers. The +MathQA-Python dataset, a Python version of the MathQA benchmark, contains 23914 +problems that evaluate the ability of the models to synthesize code from more +complex text. On both datasets, we find that synthesis performance scales +log-linearly with model size. Our largest models, even without finetuning on a +code dataset, can synthesize solutions to 59.6 percent of the problems from +MBPP using few-shot learning with a well-designed prompt. Fine-tuning on a +held-out portion of the dataset improves performance by about 10 percentage +points across most model sizes. On the MathQA-Python dataset, the largest +fine-tuned model achieves 83.8 percent accuracy. Going further, we study the +model's ability to engage in dialog about code, incorporating human feedback to +improve its solutions. We find that natural language feedback from a human +halves the error rate compared to the model's initial prediction. Additionally, +we conduct an error analysis to shed light on where these models fall short and +what types of programs are most difficult to generate. Finally, we explore the +semantic grounding of these models by fine-tuning them to predict the results +of program execution. We find that even our best models are generally unable to +predict the output of a program given a specific input. +" +Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code,Andrea Tagarelli,http://arxiv.org/pdf/2112.03033v1.pdf,2021-12-02,"['cs.cl', 'cs.ai', 'cs.ir', 'physics.soc-ph']",2112.03033v1.pdf," Modeling law search and retrieval as prediction problems has recently emerged +as a predominant approach in law intelligence. Focusing on the law article +retrieval task, we present a deep learning framework named LamBERTa, which is +designed for civil-law codes, and specifically trained on the Italian civil +code. To our knowledge, this is the first study proposing an advanced approach +to law article prediction for the Italian legal system based on a BERT +(Bidirectional Encoder Representations from Transformers) learning framework, +which has recently attracted increased attention among deep learning +approaches, showing outstanding effectiveness in several natural language +processing and learning tasks. We define LamBERTa models by fine-tuning an +Italian pre-trained BERT on the Italian civil code or its portions, for law +article retrieval as a classification task. One key aspect of our LamBERTa +framework is that we conceived it to address an extreme classification +scenario, which is characterized by a high number of classes, the few-shot +learning problem, and the lack of test query benchmarks for Italian legal +prediction tasks. To solve such issues, we define different methods for the +unsupervised labeling of the law articles, which can in principle be applied to +any law article code system. We provide insights into the explainability and +interpretability of our LamBERTa models, and we present an extensive +experimental analysis over query sets of different type, for single-label as +well as multi-label evaluation tasks. Empirical evidence has shown the +effectiveness of LamBERTa, and also its superiority against widely used +deep-learning text classifiers and a few-shot learner conceived for an +attribute-aware prediction task. +" +"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model",Shaden Smith,http://arxiv.org/pdf/2201.11990v3.pdf,2022-01-28,['cs.cl'],2201.11990v3.pdf," Pretrained general-purpose language models can achieve state-of-the-art +accuracies in various natural language processing domains by adapting to +downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of +their success, the size of these models has increased rapidly, requiring +high-performance hardware, software, and algorithmic techniques to enable +training such large models. As the result of a joint effort between Microsoft +and NVIDIA, we present details on the training of the largest monolithic +transformer based language model, Megatron-Turing NLG 530B (MT-NLG), with 530 +billion parameters. In this paper, we first focus on the infrastructure as well +as the 3D parallelism methodology used to train this model using DeepSpeed and +Megatron. Next, we detail the training process, the design of our training +corpus, and our data curation techniques, which we believe is a key ingredient +to the success of the model. Finally, we discuss various evaluation results, as +well as other interesting observations and new properties exhibited by MT-NLG. +We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning +accuracies on several NLP benchmarks and establishes new state-of-the-art +results. We believe that our contributions will help further the development of +large-scale training infrastructures, large-scale language models, and natural +language generations. +" +Data Distributional Properties Drive Emergent In-Context Learning in Transformers,Stephanie C. Y. Chan,http://arxiv.org/pdf/2205.05055v6.pdf,2022-04-22,"['cs.lg', 'cs.ai', 'cs.cl']",2205.05055v6.pdf," Large transformer-based models are able to perform in-context few-shot +learning, without being explicitly trained for it. This observation raises the +question: what aspects of the training regime lead to this emergent behavior? +Here, we show that this behavior is driven by the distributions of the training +data itself. In-context learning emerges when the training data exhibits +particular distributional properties such as burstiness (items appear in +clusters rather than being uniformly distributed over time) and having large +numbers of rarely occurring classes. In-context learning also emerges more +strongly when item meanings or interpretations are dynamic rather than fixed. +These properties are exemplified by natural language, but are also inherent to +naturalistic data in a wide range of other domains. They also depart +significantly from the uniform, i.i.d. training distributions typically used +for standard supervised learning. In our initial experiments, we found that +in-context learning traded off against more conventional weight-based learning, +and models were unable to achieve both simultaneously. However, our later +experiments uncovered that the two modes of learning could co-exist in a single +model when it was trained on data following a skewed Zipfian distribution -- +another common property of naturalistic data, including language. In further +experiments, we found that naturalistic data distributions were only able to +elicit in-context learning in transformers, and not in recurrent models. In +sum, our findings indicate how the transformer architecture works together with +particular properties of the training data to drive the intriguing emergent +in-context learning behaviour of large language models, and how future work +might encourage both in-context and in-weights learning in domains beyond +language. +" +Large Language Models are Zero-Shot Reasoners,Takeshi Kojima,http://arxiv.org/pdf/2205.11916v4.pdf,2022-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2205.11916v4.pdf," Pretrained large language models (LLMs) are widely used in many sub-fields of +natural language processing (NLP) and generally known as excellent few-shot +learners with task-specific exemplars. Notably, chain of thought (CoT) +prompting, a recent technique for eliciting complex multi-step reasoning +through step-by-step answer examples, achieved the state-of-the-art +performances in arithmetics and symbolic reasoning, difficult system-2 tasks +that do not follow the standard scaling laws for LLMs. While these successes +are often attributed to LLMs' ability for few-shot learning, we show that LLMs +are decent zero-shot reasoners by simply adding ""Let's think step by step"" +before each answer. Experimental results demonstrate that our Zero-shot-CoT, +using the same single prompt template, significantly outperforms zero-shot LLM +performances on diverse benchmark reasoning tasks including arithmetics +(MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin +Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled +Objects), without any hand-crafted few-shot examples, e.g. increasing the +accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with +large InstructGPT model (text-davinci-002), as well as similar magnitudes of +improvements with another off-the-shelf large model, 540B parameter PaLM. The +versatility of this single prompt across very diverse reasoning tasks hints at +untapped and understudied fundamental zero-shot capabilities of LLMs, +suggesting high-level, multi-task broad cognitive capabilities may be extracted +by simple prompting. We hope our work not only serves as the minimal strongest +zero-shot baseline for the challenging reasoning benchmarks, but also +highlights the importance of carefully exploring and analyzing the enormous +zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or +few-shot exemplars. +" +Hungry Hungry Hippos: Towards Language Modeling with State Space Models,Daniel Y. Fu,http://arxiv.org/pdf/2212.14052v3.pdf,2022-12-28,"['cs.lg', 'cs.cl']",2212.14052v3.pdf," State space models (SSMs) have demonstrated state-of-the-art sequence +modeling performance in some modalities, but underperform attention in language +modeling. Moreover, despite scaling nearly linearly in sequence length instead +of quadratically, SSMs are still slower than Transformers due to poor hardware +utilization. In this paper, we make progress on understanding the expressivity +gap between SSMs and attention in language modeling, and on reducing the +hardware barrier between SSMs and attention. First, we use synthetic language +modeling tasks to understand the gap between SSMs and attention. We find that +existing SSMs struggle with two capabilities: recalling earlier tokens in the +sequence and comparing tokens across the sequence. To understand the impact on +language modeling, we propose a new SSM layer, H3, that is explicitly designed +for these abilities. H3 matches attention on the synthetic languages and comes +within 0.4 PPL of Transformers on OpenWebText. Furthermore, a hybrid +125M-parameter H3-attention model that retains two attention layers +surprisingly outperforms Transformers on OpenWebText by 1.0 PPL. Next, to +improve the efficiency of training SSMs on modern hardware, we propose +FlashConv. FlashConv uses a fused block FFT algorithm to improve efficiency on +sequences up to 8K, and introduces a novel state passing algorithm that +exploits the recurrent properties of SSMs to scale to longer sequences. +FlashConv yields 2$\times$ speedup on the long-range arena benchmark and allows +hybrid language models to generate text 2.4$\times$ faster than Transformers. +Using FlashConv, we scale hybrid H3-attention language models up to 2.7B +parameters on the Pile and find promising initial results, achieving lower +perplexity than Transformers and outperforming Transformers in zero- and +few-shot learning on a majority of tasks in the SuperGLUE benchmark. +" +CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP,Runnan Chen,http://arxiv.org/pdf/2301.04926v2.pdf,2023-01-12,['cs.cv'],2301.04926v2.pdf," Contrastive Language-Image Pre-training (CLIP) achieves promising results in +2D zero-shot and few-shot learning. Despite the impressive performance in 2D, +applying CLIP to help the learning in 3D scene understanding has yet to be +explored. In this paper, we make the first attempt to investigate how CLIP +knowledge benefits 3D scene understanding. We propose CLIP2Scene, a simple yet +effective framework that transfers CLIP knowledge from 2D image-text +pre-trained models to a 3D point cloud network. We show that the pre-trained 3D +network yields impressive performance on various downstream tasks, i.e., +annotation-free and fine-tuning with labelled data for semantic segmentation. +Specifically, built upon CLIP, we design a Semantic-driven Cross-modal +Contrastive Learning framework that pre-trains a 3D network via semantic and +spatial-temporal consistency regularization. For the former, we first leverage +CLIP's text semantics to select the positive and negative point samples and +then employ the contrastive loss to train the 3D network. In terms of the +latter, we force the consistency between the temporally coherent point cloud +features and their corresponding image features. We conduct experiments on +SemanticKITTI, nuScenes, and ScanNet. For the first time, our pre-trained +network achieves annotation-free 3D semantic segmentation with 20.8% and 25.08% +mIoU on nuScenes and ScanNet, respectively. When fine-tuned with 1% or 100% +labelled data, our method significantly outperforms other self-supervised +methods, with improvements of 8% and 1% mIoU, respectively. Furthermore, we +demonstrate the generalizability for handling cross-domain datasets. Code is +publicly available https://github.com/runnanchen/CLIP2Scene. +" +An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation,Max Schäfer,http://arxiv.org/pdf/2302.06527v3.pdf,2023-02-13,"['cs.se', 'cs.ai']",2302.06527v3.pdf," Unit tests play a key role in ensuring the correctness of software. However, +manually creating unit tests is a laborious task, motivating the need for +automation. Large Language Models (LLMs) have recently been applied to this +problem, utilizing additional training or few-shot learning on examples of +existing tests. This paper presents a large-scale empirical evaluation on the +effectiveness of LLMs for automated unit test generation without additional +training or manual effort, providing the LLM with the signature and +implementation of the function under test, along with usage examples extracted +from documentation. We also attempt to repair failed generated tests by +re-prompting the model with the failing test and error message. We implement +our approach in TestPilot, a test generation tool for JavaScript that +automatically generates unit tests for all API functions in an npm package. We +evaluate TestPilot using OpenAI's gpt3.5-turbo LLM on 25 npm packages with a +total of 1,684 API functions. The generated tests achieve a median statement +coverage of 70.2% and branch coverage of 52.8%, significantly improving on +Nessie, a recent feedback-directed JavaScript test generation technique, which +achieves only 51.3% statement coverage and 25.6% branch coverage. We also find +that 92.8% of TestPilot's generated tests have no more than 50% similarity with +existing tests (as measured by normalized edit distance), with none of them +being exact copies. Finally, we run TestPilot with two additional LLMs, +OpenAI's older code-cushman-002 LLM and the open LLM StarCoder. Overall, we +observed similar results with the former (68.2% median statement coverage), and +somewhat worse results with the latter (54.0% median statement coverage), +suggesting that the effectiveness of the approach is influenced by the size and +training set of the LLM, but does not fundamentally depend on the specific +model. +" +On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence,Gengchen Mai,http://arxiv.org/pdf/2304.06798v1.pdf,2023-04-13,"['cs.ai', 'cs.cl', 'cs.cv', 'i.2.0; i.2.4; i.2.7; i.2.10; i.5.1']",2304.06798v1.pdf," Large pre-trained models, also known as foundation models (FMs), are trained +in a task-agnostic manner on large-scale data and can be adapted to a wide +range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. +Despite their successes in language and vision tasks, we have yet seen an +attempt to develop foundation models for geospatial artificial intelligence +(GeoAI). In this work, we explore the promises and challenges of developing +multimodal foundation models for GeoAI. We first investigate the potential of +many existing FMs by testing their performances on seven tasks across multiple +geospatial subdomains including Geospatial Semantics, Health Geography, Urban +Geography, and Remote Sensing. Our results indicate that on several geospatial +tasks that only involve text modality such as toponym recognition, location +description recognition, and US state-level/county-level dementia time series +forecasting, these task-agnostic LLMs can outperform task-specific +fully-supervised models in a zero-shot or few-shot learning setting. However, +on other geospatial tasks, especially tasks that involve multiple data +modalities (e.g., POI-based urban function classification, street view +image-based urban noise intensity classification, and remote sensing image +scene classification), existing foundation models still underperform +task-specific models. Based on these observations, we propose that one of the +major challenges of developing a FM for GeoAI is to address the multimodality +nature of geospatial tasks. After discussing the distinct challenges of each +geospatial data modality, we suggest the possibility of a multimodal foundation +model which can reason over various types of geospatial data through geospatial +alignments. We conclude this paper by discussing the unique risks and +challenges to develop such a model for GeoAI. +" +Learning to detect an animal sound from five examples,Inês Nolasco,http://arxiv.org/pdf/2305.13210v1.pdf,2023-05-22,"['cs.sd', 'eess.as', 'q-bio.qm']",2305.13210v1.pdf," Automatic detection and classification of animal sounds has many applications +in biodiversity monitoring and animal behaviour. In the past twenty years, the +volume of digitised wildlife sound available has massively increased, and +automatic classification through deep learning now shows strong results. +However, bioacoustics is not a single task but a vast range of small-scale +tasks (such as individual ID, call type, emotional indication) with wide +variety in data characteristics, and most bioacoustic tasks do not come with +strongly-labelled training data. The standard paradigm of supervised learning, +focussed on a single large-scale dataset and/or a generic pre-trained +algorithm, is insufficient. In this work we recast bioacoustic sound event +detection within the AI framework of few-shot learning. We adapt this framework +to sound event detection, such that a system can be given the annotated +start/end times of as few as 5 events, and can then detect events in +long-duration audio -- even when the sound category was not known at the time +of algorithm training. We introduce a collection of open datasets designed to +strongly test a system's ability to perform few-shot sound event detections, +and we present the results of a public contest to address the task. We show +that prototypical networks are a strong-performing method, when enhanced with +adaptations for general characteristics of animal sounds. We demonstrate that +widely-varying sound event durations are an important factor in performance, as +well as non-stationarity, i.e. gradual changes in conditions throughout the +duration of a recording. For fine-grained bioacoustic recognition tasks without +massive annotated training data, our results demonstrate that few-shot sound +event detection is a powerful new method, strongly outperforming traditional +signal-processing detection methods in the fully automated scenario. +" +The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification,Linhao Qu,http://arxiv.org/pdf/2305.17891v1.pdf,2023-05-29,['cs.cv'],2305.17891v1.pdf," This paper introduces the novel concept of few-shot weakly supervised +learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. +A solution is proposed based on prompt learning and the utilization of a large +language model, GPT-4. Since a WSI is too large and needs to be divided into +patches for processing, WSI classification is commonly approached as a Multiple +Instance Learning (MIL) problem. In this context, each WSI is considered a bag, +and the obtained patches are treated as instances. The objective of FSWC is to +classify both bags and instances with only a limited number of labeled bags. +Unlike conventional few-shot learning problems, FSWC poses additional +challenges due to its weak bag labels within the MIL framework. Drawing +inspiration from the recent achievements of vision-language models (V-L models) +in downstream few-shot classification tasks, we propose a two-level prompt +learning MIL framework tailored for pathology, incorporating language prior +knowledge. Specifically, we leverage CLIP to extract instance features for each +patch, and introduce a prompt-guided pooling strategy to aggregate these +instance features into a bag feature. Subsequently, we employ a small number of +labeled bags to facilitate few-shot prompt learning based on the bag features. +Our approach incorporates the utilization of GPT-4 in a question-and-answer +mode to obtain language prior knowledge at both the instance and bag levels, +which are then integrated into the instance and bag level language prompts. +Additionally, a learnable component of the language prompts is trained using +the available few-shot labeled data. We conduct extensive experiments on three +real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, +demonstrating the notable performance of the proposed method in bag and +instance classification. All codes will be made publicly accessible. +" +Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing,Arghavan Moradi Dakhel,http://arxiv.org/pdf/2308.16557v1.pdf,2023-08-31,['cs.se'],2308.16557v1.pdf," One of the critical phases in software development is software testing. +Testing helps with identifying potential bugs and reducing maintenance costs. +The goal of automated test generation tools is to ease the development of tests +by suggesting efficient bug-revealing tests. Recently, researchers have +leveraged Large Language Models (LLMs) of code to generate unit tests. While +the code coverage of generated tests was usually assessed, the literature has +acknowledged that the coverage is weakly correlated with the efficiency of +tests in bug detection. To improve over this limitation, in this paper, we +introduce MuTAP for improving the effectiveness of test cases generated by LLMs +in terms of revealing bugs by leveraging mutation testing. Our goal is achieved +by augmenting prompts with surviving mutants, as those mutants highlight the +limitations of test cases in detecting bugs. MuTAP is capable of generating +effective test cases in the absence of natural language descriptions of the +Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate +their performance on different benchmarks. Our results show that our proposed +method is able to detect up to 28% more faulty human-written code snippets. +Among these, 17% remained undetected by both the current state-of-the-art fully +automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning +approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57% +on synthetic buggy code, outperforming all other approaches in our evaluation. +Our findings suggest that although LLMs can serve as a useful tool to generate +test cases, they require specific post-processing steps to enhance the +effectiveness of the generated test cases which may suffer from syntactic or +functional errors and may be ineffective in detecting certain types of bugs and +testing corner cases PUTs. +" +LLM4SGG: Large Language Model for Weakly Supervised Scene Graph Generation,Kibum Kim,http://arxiv.org/pdf/2310.10404v4.pdf,2023-10-16,['cs.cv'],2310.10404v4.pdf," Weakly-Supervised Scene Graph Generation (WSSGG) research has recently +emerged as an alternative to the fully-supervised approach that heavily relies +on costly annotations. In this regard, studies on WSSGG have utilized image +captions to obtain unlocalized triplets while primarily focusing on grounding +the unlocalized triplets over image regions. However, they have overlooked the +two issues involved in the triplet formation process from the captions: 1) +Semantic over-simplification issue arises when extracting triplets from +captions, where fine-grained predicates in captions are undesirably converted +into coarse-grained predicates, resulting in a long-tailed predicate +distribution, and 2) Low-density scene graph issue arises when aligning the +triplets in the caption with entity/predicate classes of interest, where many +triplets are discarded and not used in training, leading to insufficient +supervision. To tackle the two issues, we propose a new approach, i.e., Large +Language Model for weakly-supervised SGG (LLM4SGG), where we mitigate the two +issues by leveraging the LLM's in-depth understanding of language and reasoning +ability during the extraction of triplets from captions and alignment of +entity/predicate classes with target data. To further engage the LLM in these +processes, we adopt the idea of Chain-of-Thought and the in-context few-shot +learning strategy. To validate the effectiveness of LLM4SGG, we conduct +extensive experiments on Visual Genome and GQA datasets, showing significant +improvements in both Recall@K and mean Recall@K compared to the +state-of-the-art WSSGG methods. A further appeal is that LLM4SGG is +data-efficient, enabling effective model training with a small amount of +training images. +" +Language Models are Few-Shot Learners,Tom B. Brown,http://arxiv.org/pdf/2005.14165v4.pdf,2020-05-28,['cs.cl'],2005.14165v4.pdf," Recent work has demonstrated substantial gains on many NLP tasks and +benchmarks by pre-training on a large corpus of text followed by fine-tuning on +a specific task. While typically task-agnostic in architecture, this method +still requires task-specific fine-tuning datasets of thousands or tens of +thousands of examples. By contrast, humans can generally perform a new language +task from only a few examples or from simple instructions - something which +current NLP systems still largely struggle to do. Here we show that scaling up +language models greatly improves task-agnostic, few-shot performance, sometimes +even reaching competitiveness with prior state-of-the-art fine-tuning +approaches. Specifically, we train GPT-3, an autoregressive language model with +175 billion parameters, 10x more than any previous non-sparse language model, +and test its performance in the few-shot setting. For all tasks, GPT-3 is +applied without any gradient updates or fine-tuning, with tasks and few-shot +demonstrations specified purely via text interaction with the model. GPT-3 +achieves strong performance on many NLP datasets, including translation, +question-answering, and cloze tasks, as well as several tasks that require +on-the-fly reasoning or domain adaptation, such as unscrambling words, using a +novel word in a sentence, or performing 3-digit arithmetic. At the same time, +we also identify some datasets where GPT-3's few-shot learning still struggles, +as well as some datasets where GPT-3 faces methodological issues related to +training on large web corpora. Finally, we find that GPT-3 can generate samples +of news articles which human evaluators have difficulty distinguishing from +articles written by humans. We discuss broader societal impacts of this finding +and of GPT-3 in general. +" +MasakhaNEWS: News Topic Classification for African languages,David Ifeoluwa Adelani,http://arxiv.org/pdf/2304.09972v2.pdf,2023-04-19,['cs.cl'],2304.09972v2.pdf," African languages are severely under-represented in NLP research due to lack +of datasets covering several NLP tasks. While there are individual language +specific datasets that are being expanded to different tasks, only a handful of +NLP tasks (e.g. named entity recognition and machine translation) have +standardized benchmark datasets covering several geographical and +typologically-diverse African languages. In this paper, we develop MasakhaNEWS +-- a new benchmark dataset for news topic classification covering 16 languages +widely spoken in Africa. We provide an evaluation of baseline models by +training classical machine learning models and fine-tuning several language +models. Furthermore, we explore several alternatives to full fine-tuning of +language models that are better suited for zero-shot and few-shot learning such +as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern +exploiting training (PET), prompting language models (like ChatGPT), and +prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). +Our evaluation in zero-shot setting shows the potential of prompting ChatGPT +for news topic classification in low-resource African languages, achieving an +average performance of 70 F1 points without leveraging additional supervision +like MAD-X. In few-shot setting, we show that with as little as 10 examples per +label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of +full supervised training (92.6 F1 points) leveraging the PET approach. +" +Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods,Mengsay Loem,http://arxiv.org/pdf/2305.18156v1.pdf,2023-05-29,"['cs.cl', 'cs.ai']",2305.18156v1.pdf," Large-scale pre-trained language models such as GPT-3 have shown remarkable +performance across various natural language processing tasks. However, applying +prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks +and their controllability remains underexplored. Controllability in GEC is +crucial for real-world applications, particularly in educational settings, +where the ability to tailor feedback according to learner levels and specific +error types can significantly enhance the learning process. This paper +investigates the performance and controllability of prompt-based methods with +GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact +of task instructions and examples on GPT-3's output, focusing on controlling +aspects such as minimal edits, fluency edits, and learner levels. Our findings +demonstrate that GPT-3 could effectively perform GEC tasks, outperforming +existing supervised and unsupervised approaches. We also showed that GPT-3 +could achieve controllability when appropriate task instructions and examples +are given. +" +Causal Intervention-based Prompt Debiasing for Event Argument Extraction,Jiaju Lin,http://arxiv.org/pdf/2210.01561v1.pdf,2022-10-04,"['cs.cl', 'cs.ai']",2210.01561v1.pdf," Prompt-based methods have become increasingly popular among information +extraction tasks, especially in low-data scenarios. By formatting a finetune +task into a pre-training objective, prompt-based methods resolve the data +scarce problem effectively. However, seldom do previous research investigate +the discrepancy among different prompt formulating strategies. In this work, we +compare two kinds of prompts, name-based prompt and ontology-base prompt, and +reveal how ontology-base prompt methods exceed its counterpart in zero-shot +event argument extraction (EAE) . Furthermore, we analyse the potential risk in +ontology-base prompts via a causal view and propose a debias method by causal +intervention. Experiments on two benchmarks demonstrate that modified by our +debias method, the baseline model becomes both more effective and robust, with +significant improvement in the resistance to adversarial attacks. +" +When Prompt-based Incremental Learning Does Not Meet Strong Pretraining,Yu-Ming Tang,http://arxiv.org/pdf/2308.10445v1.pdf,2023-08-21,['cs.cv'],2308.10445v1.pdf," Incremental learning aims to overcome catastrophic forgetting when learning +deep networks from sequential tasks. With impressive learning efficiency and +performance, prompt-based methods adopt a fixed backbone to sequential tasks by +learning task-specific prompts. However, existing prompt-based methods heavily +rely on strong pretraining (typically trained on ImageNet-21k), and we find +that their models could be trapped if the potential gap between the pretraining +task and unknown future tasks is large. In this work, we develop a learnable +Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and +prompt learning processes into a learnable prompt generator. Hence, the whole +prompting process can be optimized to reduce the negative effects of the gap +between tasks effectively. To make our APG avoid learning ineffective +knowledge, we maintain a knowledge pool to regularize APG with the feature +distribution of each class. Extensive experiments show that our method +significantly outperforms advanced methods in exemplar-free incremental +learning without (strong) pretraining. Besides, under strong retraining, our +method also has comparable performance to existing prompt-based models, showing +that our method can still benefit from pretraining. Codes can be found at +https://github.com/TOM-tym/APG +" +Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts,Zewei Sun,http://arxiv.org/pdf/2209.11409v1.pdf,2022-09-23,['cs.cl'],2209.11409v1.pdf," Domain adaptation is an important challenge for neural machine translation. +However, the traditional fine-tuning solution requires multiple extra training +and yields a high cost. In this paper, we propose a non-tuning paradigm, +resolving domain adaptation with a prompt-based method. Specifically, we +construct a bilingual phrase-level database and retrieve relevant pairs from it +as a prompt for the input sentences. By utilizing Retrieved Phrase-level +Prompts (RePP), we effectively boost the translation quality. Experiments show +that our method improves domain-specific machine translation for 6.2 BLEU +scores and improves translation constraints for 11.5% accuracy without +additional training. +" +NSP-BERT: A Prompt-based Few-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction,Yi Sun,http://arxiv.org/pdf/2109.03564v2.pdf,2021-09-08,"['cs.cl', 'cs.ai']",2109.03564v2.pdf," Using prompts to utilize language models to perform various downstream tasks, +also known as prompt-based learning or prompt-learning, has lately gained +significant success in comparison to the pre-train and fine-tune paradigm. +Nonetheless, virtually all prompt-based methods are token-level, meaning they +all utilize GPT's left-to-right language model or BERT's masked language model +to perform cloze-style tasks. In this paper, we attempt to accomplish several +NLP tasks in the zero-shot scenario using a BERT original pre-training task +abandoned by RoBERTa and other models--Next Sentence Prediction (NSP). Unlike +token-level techniques, our sentence-level prompt-based method NSP-BERT does +not need to fix the length of the prompt or the position to be predicted, +allowing it to handle tasks such as entity linking with ease. Based on the +characteristics of NSP-BERT, we offer several quick building templates for +various downstream tasks. We suggest a two-stage prompt method for word sense +disambiguation tasks in particular. Our strategies for mapping the labels +significantly enhance the model's performance on sentence pair tasks. On the +FewCLUE benchmark, our NSP-BERT outperforms other zero-shot methods on most of +these tasks and comes close to the few-shot methods. +" +Introducing Language Guidance in Prompt-based Continual Learning,Muhammad Gul Zain Ali Khan,http://arxiv.org/pdf/2308.15827v1.pdf,2023-08-30,['cs.cv'],2308.15827v1.pdf," Continual Learning aims to learn a single model on a sequence of tasks +without having access to data from previous tasks. The biggest challenge in the +domain still remains catastrophic forgetting: a loss in performance on seen +classes of earlier tasks. Some existing methods rely on an expensive replay +buffer to store a chunk of data from previous tasks. This, while promising, +becomes expensive when the number of tasks becomes large or data can not be +stored for privacy reasons. As an alternative, prompt-based methods have been +proposed that store the task information in a learnable prompt pool. This +prompt pool instructs a frozen image encoder on how to solve each task. While +the model faces a disjoint set of classes in each task in this setting, we +argue that these classes can be encoded to the same embedding space of a +pre-trained language encoder. In this work, we propose Language Guidance for +Prompt-based Continual Learning (LGCL) as a plug-in for prompt-based methods. +LGCL is model agnostic and introduces language guidance at the task level in +the prompt pool and at the class level on the output feature of the vision +encoder. We show with extensive experimentation that LGCL consistently improves +the performance of prompt-based continual learning methods to set a new +state-of-the art. LGCL achieves these performance improvements without needing +any additional learnable parameters. +" +Enable Language Models to Implicitly Learn Self-Improvement From Data,Ziqi Wang,http://arxiv.org/pdf/2310.00898v2.pdf,2023-10-02,['cs.cl'],2310.00898v2.pdf," Large Language Models (LLMs) have demonstrated remarkable capabilities in +open-ended text generation tasks. However, the inherent open-ended nature of +these tasks implies that there is always room for improvement in the quality of +model responses. To address this challenge, various approaches have been +proposed to enhance the performance of LLMs. There has been a growing focus on +enabling LLMs to self-improve their response quality, thereby reducing the +reliance on extensive human annotation efforts for collecting diverse and +high-quality training data. Recently, prompting-based methods have been widely +explored among self-improvement methods owing to their effectiveness, +efficiency, and convenience. However, those methods usually require explicitly +and thoroughly written rubrics as inputs to LLMs. It is expensive and +challenging to manually derive and provide all necessary rubrics with a +real-world complex goal for improvement (e.g., being more helpful and less +harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework +that implicitly learns the improvement goal from human preference data. PIT +only requires preference data that are used to train reward models without +extra human efforts. Specifically, we reformulate the training objective of +reinforcement learning from human feedback (RLHF) -- instead of maximizing +response quality for a given input, we maximize the quality gap of the response +conditioned on a reference response. In this way, PIT is implicitly trained +with the improvement goal of better aligning with human preferences. +Experiments on two real-world datasets and one synthetic dataset show that our +method significantly outperforms prompting-based methods. +" +MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition,Jinming Zhao,http://arxiv.org/pdf/2111.00865v1.pdf,2021-10-27,"['cs.cv', 'eess.iv']",2111.00865v1.pdf," Multimodal emotion recognition study is hindered by the lack of labelled +corpora in terms of scale and diversity, due to the high annotation cost and +label ambiguity. In this paper, we propose a pre-training model +\textbf{MEmoBERT} for multimodal emotion recognition, which learns multimodal +joint representations through self-supervised learning from large-scale +unlabeled video data that come in sheer volume. Furthermore, unlike the +conventional ""pre-train, finetune"" paradigm, we propose a prompt-based method +that reformulates the downstream emotion classification task as a masked text +prediction one, bringing the downstream task closer to the pre-training. +Extensive experiments on two benchmark datasets, IEMOCAP and MSP-IMPROV, show +that our proposed MEmoBERT significantly enhances emotion recognition +performance. +" +PSG: Prompt-based Sequence Generation for Acronym Extraction,Bin Li,http://arxiv.org/pdf/2111.14301v2.pdf,2021-11-29,"['cs.cl', 'cs.ai']",2111.14301v2.pdf," Acronym extraction aims to find acronyms (i.e., short-forms) and their +meanings (i.e., long-forms) from the documents, which is important for +scientific document understanding (SDU@AAAI-22) tasks. Previous works are +devoted to modeling this task as a paragraph-level sequence labeling problem. +However, it lacks the effective use of the external knowledge, especially when +the datasets are in a low-resource setting. Recently, the prompt-based method +with the vast pre-trained language model can significantly enhance the +performance of the low-resourced downstream tasks. In this paper, we propose a +Prompt-based Sequence Generation (PSG) method for the acronym extraction task. +Specifically, we design a template for prompting the extracted acronym texts +with auto-regression. A position extraction algorithm is designed for +extracting the position of the generated answers. The results on the acronym +extraction of Vietnamese and Persian in a low-resource setting show that the +proposed method outperforms all other competitive state-of-the-art (SOTA) +methods. +" +Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches,Virginia Adams,http://arxiv.org/pdf/2111.15622v1.pdf,2021-11-30,['cs.cl'],2111.15622v1.pdf," The Biocreative VII Track-2 challenge consists of named entity recognition, +entity-linking (or entity-normalization), and topic indexing tasks -- with +entities and topics limited to chemicals for this challenge. Named entity +recognition is a well-established problem and we achieve our best performance +with BERT-based BioMegatron models. We extend our BERT-based approach to the +entity linking task. After the second stage of pretraining BioBERT with a +metric-learning loss strategy called self-alignment pretraining (SAP), we link +entities based on the cosine similarity between their SAP-BioBERT word +embeddings. Despite the success of our named entity recognition experiments, we +find the chemical indexing task generally more challenging. + In addition to conventional NER methods, we attempt both named entity +recognition and entity linking with a novel text-to-text or ""prompt"" based +method that uses generative language models such as T5 and GPT. We achieve +encouraging results with this new approach. +" +AdaPrompt: Adaptive Model Training for Prompt-based NLP,Yulong Chen,http://arxiv.org/pdf/2202.04824v2.pdf,2022-02-10,['cs.cl'],2202.04824v2.pdf," Prompt-based learning, with its capability to tackle zero-shot and few-shot +NLP tasks, has gained much attention in community. The main idea is to bridge +the gap between NLP downstream tasks and language modeling (LM), by mapping +these tasks into natural language prompts, which are then filled by pre-trained +language models (PLMs). However, for prompt learning, there are still two +salient gaps between NLP tasks and pretraining. First, prompt information is +not necessarily sufficiently present during LM pretraining. Second, +task-specific data are not necessarily well represented during pretraining. We +address these two issues by proposing AdaPrompt, adaptively retrieving external +data for continual pretraining of PLMs by making use of both task and prompt +characteristics. In addition, we make use of knowledge in Natural Language +Inference models for deriving adaptive verbalizers. Experimental results on +five NLP benchmarks show that AdaPrompt can improve over standard PLMs in +few-shot settings. In addition, in zero-shot settings, our method outperforms +standard prompt-based methods by up to 26.35\% relative error reduction. +" +Prompting to Distill: Boosting Data-Free Knowledge Distillation via Reinforced Prompt,Xinyin Ma,http://arxiv.org/pdf/2205.07523v1.pdf,2022-05-16,['cs.cl'],2205.07523v1.pdf," Data-free knowledge distillation (DFKD) conducts knowledge distillation via +eliminating the dependence of original training data, and has recently achieved +impressive results in accelerating pre-trained language models. At the heart of +DFKD is to reconstruct a synthetic dataset by inverting the parameters of the +uncompressed model. Prior DFKD approaches, however, have largely relied on +hand-crafted priors of the target data distribution for the reconstruction, +which can be inevitably biased and often incompetent to capture the intrinsic +distributions. To address this problem, we propose a prompt-based method, +termed as PromptDFD, that allows us to take advantage of learned language +priors, which effectively harmonizes the synthetic sentences to be semantically +and grammatically correct. Specifically, PromptDFD leverages a pre-trained +generative model to provide language priors and introduces a reinforced topic +prompter to control data synthesis, making the generated samples thematically +relevant and semantically plausible, and thus friendly to downstream tasks. As +shown in our experiments, the proposed method substantially improves the +synthesis quality and achieves considerable improvements on distillation +performance. In some cases, PromptDFD even gives rise to results on par with +those from the data-driven knowledge distillation with access to the original +training data. +" +"Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias",Yarden Tal,http://arxiv.org/pdf/2206.09860v1.pdf,2022-06-20,['cs.cl'],2206.09860v1.pdf," The size of pretrained models is increasing, and so is their performance on a +variety of NLP tasks. However, as their memorization capacity grows, they might +pick up more social biases. In this work, we examine the connection between +model size and its gender bias (specifically, occupational gender bias). We +measure bias in three masked language model families (RoBERTa, DeBERTa, and T5) +in two setups: directly using prompt based method, and using a downstream task +(Winogender). We find on the one hand that larger models receive higher bias +scores on the former task, but when evaluated on the latter, they make fewer +gender errors. To examine these potentially conflicting results, we carefully +investigate the behavior of the different models on Winogender. We find that +while larger models outperform smaller ones, the probability that their +mistakes are caused by gender bias is higher. Moreover, we find that the +proportion of stereotypical errors compared to anti-stereotypical ones grows +with the model size. Our findings highlight the potential risks that can arise +from increasing model size. +" +PromptAttack: Prompt-based Attack for Language Models via Gradient Search,Yundi Shi,http://arxiv.org/pdf/2209.01882v1.pdf,2022-09-05,"['cs.cl', 'cs.ai', 'cs.cr']",2209.01882v1.pdf," As the pre-trained language models (PLMs) continue to grow, so do the +hardware and data requirements for fine-tuning PLMs. Therefore, the researchers +have come up with a lighter method called \textit{Prompt Learning}. However, +during the investigations, we observe that the prompt learning methods are +vulnerable and can easily be attacked by some illegally constructed prompts, +resulting in classification errors, and serious security problems for PLMs. +Most of the current research ignores the security issue of prompt-based +methods. Therefore, in this paper, we propose a malicious prompt template +construction method (\textbf{PromptAttack}) to probe the security performance +of PLMs. Several unfriendly template construction approaches are investigated +to guide the model to misclassify the task. Extensive experiments on three +datasets and three PLMs prove the effectiveness of our proposed approach +PromptAttack. We also conduct experiments to verify that our method is +applicable in few-shot scenarios. +" +ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering,Zhiyu Chen,http://arxiv.org/pdf/2210.03849v1.pdf,2022-10-07,['cs.cl'],2210.03849v1.pdf," With the recent advance in large pre-trained language models, researchers +have achieved record performances in NLP tasks that mostly focus on language +pattern matching. The community is experiencing the shift of the challenge from +how to model language to the imitation of complex reasoning abilities like +human beings. In this work, we investigate the application domain of finance +that involves real-world, complex numerical reasoning. We propose a new +large-scale dataset, ConvFinQA, aiming to study the chain of numerical +reasoning in conversational question answering. Our dataset poses great +challenge in modeling long-range, complex numerical reasoning paths in +real-world conversations. We conduct comprehensive experiments and analyses +with both the neural symbolic methods and the prompting-based methods, to +provide insights into the reasoning mechanisms of these two divisions. We +believe our new dataset should serve as a valuable resource to push forward the +exploration of real-world, complex reasoning tasks as the next research focus. +Our dataset and code is publicly available at +https://github.com/czyssrs/ConvFinQA. +" +Can Language Models Be Specific? How?,Jie Huang,http://arxiv.org/pdf/2210.05159v2.pdf,2022-10-11,"['cs.cl', 'cs.ai']",2210.05159v2.pdf," ""He is a person"", ""Paris is located on the earth"". Both statements are +correct but meaningless - due to lack of specificity. In this paper, we propose +to measure how specific the language of pre-trained language models (PLMs) is. +To achieve this, we introduce a novel approach to build a benchmark for +specificity testing by forming masked token prediction tasks with prompts. For +instance, given ""Toronto is located in [MASK]."", we want to test whether a more +specific answer will be better filled in by PLMs, e.g., Ontario instead of +Canada. From our evaluations, we show that existing PLMs have only a slight +preference for more specific answers. We identify underlying factors affecting +the specificity and design two prompt-based methods to improve the specificity. +Results show that the specificity of the models can be improved by the proposed +methods without additional training. We hope this work can bring to awareness +the notion of specificity of language models and encourage the research +community to further explore this important but understudied problem. +" +Multilingual Relation Classification via Efficient and Effective Prompting,Yuxuan Chen,http://arxiv.org/pdf/2210.13838v2.pdf,2022-10-25,"['cs.cl', 'cs.lg']",2210.13838v2.pdf," Prompting pre-trained language models has achieved impressive performance on +various NLP tasks, especially in low data regimes. Despite the success of +prompting in monolingual settings, applying prompt-based methods in +multilingual scenarios has been limited to a narrow set of tasks, due to the +high cost of handcrafting multilingual prompts. In this paper, we present the +first work on prompt-based multilingual relation classification (RC), by +introducing an efficient and effective method that constructs prompts from +relation triples and involves only minimal translation for the class labels. We +evaluate its performance in fully supervised, few-shot and zero-shot scenarios, +and analyze its effectiveness across 14 languages, prompt variants, and +English-task training in cross-lingual settings. We find that in both fully +supervised and few-shot scenarios, our prompt method beats competitive +baselines: fine-tuning XLM-R_EM and null prompts. It also outperforms the +random baseline by a large margin in zero-shot experiments. Our method requires +little in-language knowledge and can be used as a strong baseline for similar +multilingual classification tasks. +" +Steps towards prompt-based creation of virtual worlds,Jasmine Roberts,http://arxiv.org/pdf/2211.05875v1.pdf,2022-11-10,"['cs.hc', 'cs.ai', 'cs.lg', 'cs.mm']",2211.05875v1.pdf," Large language models trained for code generation can be applied to speaking +virtual worlds into existence (creating virtual worlds). In this work we show +that prompt-based methods can both accelerate in-VR level editing, as well as +can become part of gameplay rather than just part of game development. As an +example, we present Codex VR Pong which shows non-deterministic game mechanics +using generative processes to not only create static content but also +non-trivial interactions between 3D objects. This demonstration naturally leads +to an integral discussion on how one would evaluate and benchmark experiences +created by generative models - as there are no qualitative or quantitative +metrics that apply in these scenarios. We conclude by discussing impending +challenges of AI-assisted co-creation in VR. +" +SPE: Symmetrical Prompt Enhancement for Fact Probing,Yiyuan Li,http://arxiv.org/pdf/2211.07078v1.pdf,2022-11-14,"['cs.cl', 'cs.ai', 'cs.lg']",2211.07078v1.pdf," Pretrained language models (PLMs) have been shown to accumulate factual +knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs +for the extent of this knowledge through prompts either in discrete or +continuous forms. However, these methods do not consider symmetry of the task: +object prediction and subject prediction. In this work, we propose Symmetrical +Prompt Enhancement (SPE), a continuous prompt-based method for factual probing +in PLMs that leverages the symmetry of the task by constructing symmetrical +prompts for subject and object prediction. Our results on a popular factual +probing dataset, LAMA, show significant improvement of SPE over previous +probing methods. +" +Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction,Jonathan Pilault,http://arxiv.org/pdf/2301.10309v1.pdf,2023-01-24,"['cs.lg', 'cs.ai', 'cs.cl']",2301.10309v1.pdf," Crosslingual conditional generation (e.g., machine translation) has long +enjoyed the benefits of scaling. Nonetheless, there are still issues that scale +alone may not overcome. A source query in one language, for instance, may yield +several translation options in another language without any extra context. Only +one translation could be acceptable however, depending on the translator's +preferences and goals. Choosing the incorrect option might significantly affect +translation usefulness and quality. We propose a novel method interactive-chain +prompting -- a series of question, answering and generation intermediate steps +between a Translator model and a User model -- that reduces translations into a +list of subproblems addressing ambiguities and then resolving such subproblems +before producing the final text to be translated. To check ambiguity resolution +capabilities and evaluate translation quality, we create a dataset exhibiting +different linguistic phenomena which leads to ambiguities at inference for four +languages. To encourage further exploration in this direction, we release all +datasets. We note that interactive-chain prompting, using eight interactions as +exemplars, consistently surpasses prompt-based methods with direct access to +background information to resolve ambiguities. +" +Evaluating the Robustness of Discrete Prompts,Yoichi Ishibashi,http://arxiv.org/pdf/2302.05619v1.pdf,2023-02-11,"['cs.cl', 'cs.ai']",2302.05619v1.pdf," Discrete prompts have been used for fine-tuning Pre-trained Language Models +for diverse NLP tasks. In particular, automatic methods that generate discrete +prompts from a small set of training instances have reported superior +performance. However, a closer look at the learnt prompts reveals that they +contain noisy and counter-intuitive lexical constructs that would not be +encountered in manually-written prompts. This raises an important yet +understudied question regarding the robustness of automatically learnt discrete +prompts when used in downstream tasks. To address this question, we conduct a +systematic study of the robustness of discrete prompts by applying carefully +designed perturbations into an application using AutoPrompt and then measure +their performance in two Natural Language Inference (NLI) datasets. Our +experimental results show that although the discrete prompt-based method +remains relatively robust against perturbations to NLI inputs, they are highly +sensitive to other types of perturbations such as shuffling and deletion of +prompt tokens. Moreover, they generalize poorly across different NLI datasets. +We hope our findings will inspire future work on robust discrete prompt +learning. +" +Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking,Derek Chen,http://arxiv.org/pdf/2302.05932v1.pdf,2023-02-12,['cs.cl'],2302.05932v1.pdf," Prompt-based methods with large pre-trained language models (PLMs) have shown +impressive unaided performance across many NLP tasks. These models improve even +further with the addition of a few labeled in-context exemplars to guide output +generation. However, for more complex tasks such as dialogue state tracking +(DST), designing prompts that reliably convey the desired intent is nontrivial, +leading to unstable results. Furthermore, building in-context exemplars for +dialogue tasks is difficult because conversational contexts are long while +model input lengths are relatively short. To overcome these issues we first +adapt a meta-learning scheme to the dialogue domain which stabilizes the +ability of the model to perform well under various prompts. We additionally +design a novel training method to improve upon vanilla retrieval mechanisms to +find ideal in-context examples. Finally, we introduce a saliency model to limit +dialogue text length, allowing us to include more exemplars per query. In +effect, we are able to achieve highly competitive results for few-shot DST on +MultiWOZ. +" +Zero-Shot Information Extraction via Chatting with ChatGPT,Xiang Wei,http://arxiv.org/pdf/2302.10205v1.pdf,2023-02-20,['cs.cl'],2302.10205v1.pdf," Zero-shot information extraction (IE) aims to build IE systems from the +unannotated text. It is challenging due to involving little human intervention. +Challenging but worthwhile, zero-shot IE reduces the time and effort that data +labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3, +ChatGPT) show promising performance on zero-shot settings, thus inspiring us to +explore prompt-based methods. In this work, we ask whether strong IE models can +be constructed by directly prompting LLMs. Specifically, we transform the +zero-shot IE task into a multi-turn question-answering problem with a two-stage +framework (ChatIE). With the power of ChatGPT, we extensively evaluate our +framework on three IE tasks: entity-relation triple extract, named entity +recognition, and event extraction. Empirical results on six datasets across two +languages show that ChatIE achieves impressive performance and even surpasses +some full-shot models on several datasets (e.g., NYT11-HRL). We believe that +our work could shed light on building IE models with limited resources. +" +Divide and Prompt: Chain of Thought Prompting for Text-to-SQL,Xiping Liu,http://arxiv.org/pdf/2304.11556v1.pdf,2023-04-23,"['cs.cl', 'cs.ai']",2304.11556v1.pdf," Chain-of-thought (CoT) prompting combined with large language models (LLMs) +have achieved encouraging results on complex reasoning tasks. Text-to-SQL is a +critical semantic parsing task that converts natural language questions into +SQL statements, involving a complex reasoning process. However, there is little +work about using CoT prompting to activate LLM's reasoning capabilities on +Text-to-SQL tasks. In this work, we propose a new paradigm for prompting +Text-to-SQL tasks, called Divide-and-Prompt, which first divides the task into +subtasks, and then approach each subtask through CoT. We present 3 +prompting-based methods to enhance the Text-to-SQL ability of LLMs. Experiments +show that these prompts guide LLMs to generate Text-to-SQL with higher +execution accuracy. +" +Few-shot Event Detection: An Empirical Study and a Unified View,Yubo Ma,http://arxiv.org/pdf/2305.01901v2.pdf,2023-05-03,"['cs.cl', 'cs.ai']",2305.01901v2.pdf," Few-shot event detection (ED) has been widely studied, while this brings +noticeable discrepancies, e.g., various motivations, tasks, and experimental +settings, that hinder the understanding of models for future progress.This +paper presents a thorough empirical study, a unified view of ED models, and a +better unified baseline. For fair evaluation, we compare 12 representative +methods on three datasets, which are roughly grouped into prompt-based and +prototype-based models for detailed analysis. Experiments consistently +demonstrate that prompt-based methods, including ChatGPT, still significantly +trail prototype-based methods in terms of overall performance. To investigate +their superior performance, we break down their design elements along several +dimensions and build a unified framework on prototype-based methods. Under such +unified view, each prototype-method can be viewed a combination of different +modules from these design elements. We further combine all advantageous modules +and propose a simple yet effective baseline, which outperforms existing methods +by a large margin (e.g., 2.7% F1 gains under low-resource setting). +" +PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions,Anthony Chen,http://arxiv.org/pdf/2305.14908v1.pdf,2023-05-24,['cs.cl'],2305.14908v1.pdf," The remarkable capabilities of large language models have been accompanied by +a persistent drawback: the generation of false and unsubstantiated claims +commonly known as ""hallucinations"". To combat this issue, recent research has +introduced approaches that involve editing and attributing the outputs of +language models, particularly through prompt-based editing. However, the +inference cost and speed of using large language models for editing currently +bottleneck prompt-based methods. These bottlenecks motivate the training of +compact editors, which is challenging due to the scarcity of training data for +this purpose. To overcome these challenges, we exploit the power of large +language models to introduce corruptions (i.e., noise) into text and +subsequently fine-tune compact editors to denoise the corruptions by +incorporating relevant evidence. Our methodology is entirely unsupervised and +provides us with faux hallucinations for training in any domain. Our Petite +Unsupervised Research and Revision model, PURR, not only improves attribution +over existing editing methods based on fine-tuning and prompting, but also +achieves faster execution times by orders of magnitude. +" +Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis,Zikai Zhou,http://arxiv.org/pdf/2306.01312v2.pdf,2023-06-02,['cs.cl'],2306.01312v2.pdf," Multimodal Sentiment Analysis (MSA) has been a popular topic in natural +language processing nowadays, at both sentence and aspect level. However, the +existing approaches almost require large-size labeled datasets, which bring +about large consumption of time and resources. Therefore, it is practical to +explore the method for few-shot sentiment analysis in cross-modalities. +Previous works generally execute on textual modality, using the prompt-based +methods, mainly two types: hand-crafted prompts and learnable prompts. The +existing approach in few-shot multi-modality sentiment analysis task has +utilized both methods, separately. We further design a hybrid pattern that can +combine one or more fixed hand-crafted prompts and learnable prompts and +utilize the attention mechanisms to optimize the prompt encoder. The +experiments on both sentence-level and aspect-level datasets prove that we get +a significant outperformance. +" +Scaling Sentence Embeddings with Large Language Models,Ting Jiang,http://arxiv.org/pdf/2307.16645v1.pdf,2023-07-31,['cs.cl'],2307.16645v1.pdf," Large language models (LLMs) have recently garnered significant interest. +With in-context learning, LLMs achieve impressive results in various natural +language tasks. However, the application of LLMs to sentence embeddings remains +an area of ongoing research. In this work, we propose an in-context +learning-based method aimed at improving sentence embeddings performance. Our +approach involves adapting the previous prompt-based representation method for +autoregressive models, constructing a demonstration set that enables LLMs to +perform in-context learning, and scaling up the LLMs to different model sizes. +Through extensive experiments, in-context learning enables LLMs to generate +high-quality sentence embeddings without any fine-tuning. It helps LLMs achieve +performance comparable to current contrastive learning methods. By scaling +model size, we find scaling to more than tens of billion parameters harms the +performance on semantic textual similarity (STS) tasks. However, the largest +model outperforms other counterparts and achieves the new state-of-the-art +result on transfer tasks. We also fine-tune LLMs with current contrastive +learning approach, and the 2.7B OPT model, incorporating our prompt-based +method, surpasses the performance of 4.8B ST5, achieving the new +state-of-the-art results on STS tasks. Our code is available at +https://github.com/kongds/scaling_sentemb. +" +Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation,Tianyi Liu,http://arxiv.org/pdf/2112.05587v2.pdf,2021-12-10,"['cs.cv', 'cs.cl', 'cs.lg']",2112.05587v2.pdf," Most existing vision-language pre-training methods focus on understanding +tasks and use BERT-like objectives (masked language modeling and image-text +matching) during pretraining. Although they perform well in many understanding +downstream tasks, e.g., visual question answering, image-text retrieval and +visual entailment, they do not possess the ability to generate. To tackle this +problem, we propose Unified multimodal pre-training for both Vision-Language +understanding and generation (UniVL). The proposed UniVL is capable of handling +both understanding tasks and generative tasks. We augment existing pretraining +paradigms that only use random masks with causal masks, i.e., triangular masks +that mask out future tokens, such that the pre-trained models can have +autoregressive generation abilities by design. We formulate several previous +understanding tasks as a text generation task and propose to use prompt-based +method for fine-tuning on different downstream tasks. Our experiments show that +there is a trade-off between understanding tasks and generation tasks while +using the same model, and a feasible way to improve both tasks is to use more +data. Our UniVL framework attains comparable performance to recent +vision-language pre-training methods on both understanding tasks and generation +tasks. Moreover, we demostrate that prompt-based finetuning is more +data-efficient - it outperforms discriminative methods in few-shot scenarios. +" +Learning to Transfer Prompts for Text Generation,Junyi Li,http://arxiv.org/pdf/2205.01543v2.pdf,2022-05-03,['cs.cl'],2205.01543v2.pdf," Pretrained language models (PLMs) have made remarkable progress in text +generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in +a data-scarce situation. Therefore, it is non-trivial to develop a general and +lightweight model that can adapt to various text generation tasks based on +PLMs. To fulfill this purpose, the recent prompt-based learning offers a +potential solution. In this paper, we improve this technique and propose a +novel prompt-based method (PTG) for text generation in a transferable setting. +First, PTG learns a set of source prompts for various source generation tasks +and then transfers these prompts as target prompts to perform target generation +tasks. To consider both task- and instance-level information, we design an +adaptive attention mechanism to derive the target prompts. For each data +instance, PTG learns a specific target prompt by attending to highly relevant +source prompts. In extensive experiments, PTG yields competitive or better +results than fine-tuning methods. We release our source prompts as an open +resource, where users can add or reuse them to improve new text generation +tasks for future research. Code and data can be available at +https://github.com/RUCAIBox/Transfer-Prompts-for-Text-Generation. +" +On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method,Zorik Gekhman,http://arxiv.org/pdf/2206.14796v2.pdf,2022-06-29,"['cs.cl', 'cs.ai', 'cs.lg']",2206.14796v2.pdf," Most works on modeling the conversation history in Conversational Question +Answering (CQA) report a single main result on a common CQA benchmark. While +existing models show impressive results on CQA leaderboards, it remains unclear +whether they are robust to shifts in setting (sometimes to more realistic +ones), training data size (e.g. from large to small sets) and domain. In this +work, we design and conduct the first large-scale robustness study of history +modeling approaches for CQA. We find that high benchmark scores do not +necessarily translate to strong robustness, and that various methods can +perform extremely differently under different settings. Equipped with the +insights from our study, we design a novel prompt-based history modeling +approach, and demonstrate its strong robustness across various settings. Our +approach is inspired by existing methods that highlight historic answers in the +passage. However, instead of highlighting by modifying the passage token +embeddings, we add textual prompts directly in the passage text. Our approach +is simple, easy-to-plug into practically any model, and highly effective, thus +we recommend it as a starting point for future model developers. We also hope +that our study and insights will raise awareness to the importance of +robustness-focused evaluation, in addition to obtaining high leaderboard +scores, leading to better CQA systems. +" +GPTs at Factify 2022: Prompt Aided Fact-Verification,Pawan Kumar Sahu,http://arxiv.org/pdf/2206.14913v1.pdf,2022-06-29,['cs.cl'],2206.14913v1.pdf," One of the most pressing societal issues is the fight against false news. The +false claims, as difficult as they are to expose, create a lot of damage. To +tackle the problem, fact verification becomes crucial and thus has been a topic +of interest among diverse research communities. Using only the textual form of +data we propose our solution to the problem and achieve competitive results +with other approaches. We present our solution based on two approaches - PLM +(pre-trained language model) based method and Prompt based method. The +PLM-based approach uses the traditional supervised learning, where the model is +trained to take 'x' as input and output prediction 'y' as P(y|x). Whereas, +Prompt-based learning reflects the idea to design input to fit the model such +that the original objective may be re-framed as a problem of (masked) language +modeling. We may further stimulate the rich knowledge provided by PLMs to +better serve downstream tasks by employing extra prompts to fine-tune PLMs. Our +experiments showed that the proposed method performs better than just +fine-tuning PLMs. We achieved an F1 score of 0.6946 on the FACTIFY dataset and +a 7th position on the competition leader-board. +" +Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study,Xin Xu,http://arxiv.org/pdf/2210.10678v3.pdf,2022-10-19,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2210.10678v3.pdf," This paper presents an empirical study to build relation extraction systems +in low-resource settings. Based upon recent pre-trained language models, we +comprehensively investigate three schemes to evaluate the performance in +low-resource settings: (i) different types of prompt-based methods with +few-shot labeled data; (ii) diverse balancing methods to address the +long-tailed distribution issue; (iii) data augmentation technologies and +self-training to generate more labeled in-domain data. We create a benchmark +with 8 relation extraction (RE) datasets covering different languages, domains +and contexts and perform extensive comparisons over the proposed schemes with +combinations. Our experiments illustrate: (i) Though prompt-based tuning is +beneficial in low-resource RE, there is still much potential for improvement, +especially in extracting relations from cross-sentence contexts with multiple +relational triples; (ii) Balancing methods are not always helpful for RE with +long-tailed distribution; (iii) Data augmentation complements existing +baselines and can bring much performance gain, while self-training may not +consistently achieve advancement to low-resource RE. Code and datasets are in +https://github.com/zjunlp/LREBench. +" +PromptFusion: Decoupling Stability and Plasticity for Continual Learning,Haoran Chen,http://arxiv.org/pdf/2303.07223v1.pdf,2023-03-13,['cs.cv'],2303.07223v1.pdf," Continual learning refers to the capability of continuously learning from a +stream of data. Current research mainly focuses on relieving catastrophic +forgetting, and most of their success is at the cost of limiting the +performance of newly incoming tasks. Such a trade-off is referred to as the +stabilityplasticity dilemma and is a more general and challenging problem for +continual learning. However, the inherent conflict between these two concepts +makes it seemingly impossible to devise a satisfactory solution to both of them +simultaneously. Therefore, we ask, ""is it possible to divide them into two +problems to conquer independently?"" To this end, we propose a +prompt-tuning-based method termed PromptFusion to enable the decoupling of +stability and plasticity. Specifically, PromptFusion consists of a carefully +designed Stabilizer module that deals with catastrophic forgetting and a +Booster module to learn new knowledge concurrently. During training, +PromptFusion first passes an input image to the two modules separately. Then +the resulting logits are further fused with a learnable weight parameter. +Finally, a weight mask is applied to the derived logits to balance between old +and new classes. Extensive experiments show that our method achieves promising +results on popular continual learning datasets for both class-incremental and +domain incremental settings. Especially on Split-Imagenet-R, one of the most +challenging datasets for class-incremental learning, our method exceeds +state-of-the-art prompt-based methods L2P and DualPrompt by more than 10%. +" +Progressive Visual Prompt Learning with Contrastive Feature Re-formation,Chen Xu,http://arxiv.org/pdf/2304.08386v1.pdf,2023-04-17,['cs.cv'],2304.08386v1.pdf," Prompt learning has been designed as an alternative to fine-tuning for +adapting Vision-language (V-L) models to the downstream tasks. Previous works +mainly focus on text prompt while visual prompt works are limited for V-L +models. The existing visual prompt methods endure either mediocre performance +or unstable training process, indicating the difficulty of visual prompt +learning. In this paper, we propose a new Progressive Visual Prompt (ProVP) +structure to strengthen the interactions among prompts of different layers. +More importantly, our ProVP could effectively propagate the image embeddings to +deep layers and behave partially similar to an instance adaptive prompt method. +To alleviate generalization deterioration, we further propose a new contrastive +feature re-formation, which prevents the serious deviation of the prompted +visual feature from the fixed CLIP visual feature distribution. Combining both, +our method (ProVP-Ref) is evaluated on 11 image benchmark datasets and achieves +7/11 state-of-theart results on both few-shot and base-to-novel settings. To +the best of our knowledge, we are the first to demonstrate the superior +performance of visual prompts in V-L models to previous prompt-based methods in +downstream tasks. Meanwhile, it implies that our ProVP-Ref shows the best +capability to adapt and to generalize. +" +SelfEvolve: A Code Evolution Framework via Large Language Models,Shuyang Jiang,http://arxiv.org/pdf/2306.02907v1.pdf,2023-06-05,"['cs.cl', 'cs.se']",2306.02907v1.pdf," Large language models (LLMs) have already revolutionized code generation, +after being pretrained on publicly available code data. However, while various +methods have been proposed to augment LLMs with retrieved knowledge and enhance +the quality of code generation, the performance of these retrieval-based +methods is limited by the strength of the retrievers used. In addition, while +LLMs show great emergent ability, they still struggle to produce the correct +code in one turn. To address these challenges, we propose a novel two-step +pipeline, called \autoknow, that leverages LLMs as both knowledge providers and +self-reflective programmers. Unlike retrieval-based methods, \autoknow~obtains +the knowledge from input prompts and generates intermediate code based on the +generated knowledge. After that, \autoknow~asks LLM to act as an expert +programmer to perform debugging for the generated code. This is achieved by +receiving the error message from the interpreter, without requiring special +test cases for correctness verification. We evaluate \autoknow~on three code +generation datasets, including DS-1000 for data science code, HumanEval for +software engineering code, and TransCoder for C++-to-Python translation. Our +empirical experiments show that \autoknow~outperforms strong baselines by a +significant margin on all datasets. We also conduct exhaustive analytical +experiments to validate the effectiveness of the two stages of \autoknow, and +find that both are superior to other prompting-based methods. Further +scalability analysis demonstrates that \autoknow~can be adapted to other more +advanced models, such as GPT-4, and bring consistent efficacy improvement. +" +Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting,Melanie Sclar,http://arxiv.org/pdf/2310.11324v1.pdf,2023-10-17,"['cs.cl', 'cs.ai', 'cs.lg']",2310.11324v1.pdf," As large language models (LLMs) are adopted as a fundamental component of +language technologies, it is crucial to accurately characterize their +performance. Because choices in prompt design can strongly influence model +behavior, this design process is critical in effectively using any modern +pre-trained generative language model. In this work, we focus on LLM +sensitivity to a quintessential class of meaning-preserving design choices: +prompt formatting. We find that several widely used open-source LLMs are +extremely sensitive to subtle changes in prompt formatting in few-shot +settings, with performance differences of up to 76 accuracy points when +evaluated using LLaMA-2-13B. Sensitivity remains even when increasing model +size, the number of few-shot examples, or performing instruction tuning. Our +analysis suggests that work evaluating LLMs with prompting-based methods would +benefit from reporting a range of performance across plausible prompt formats, +instead of the currently-standard practice of reporting performance on a single +format. We also show that format performance only weakly correlates between +models, which puts into question the methodological validity of comparing +models with an arbitrarily chosen, fixed prompt format. To facilitate +systematic analysis we propose FormatSpread, an algorithm that rapidly +evaluates a sampled set of plausible prompt formats for a given task, and +reports the interval of expected performance without accessing model weights. +Furthermore, we present a suite of analyses that characterize the nature of +this sensitivity, including exploring the influence of particular atomic +perturbations and the internal representation of particular formats. +" +GPT-3-driven pedagogical agents for training children's curious question-asking skills,Rania Abdelghani,http://arxiv.org/pdf/2211.14228v6.pdf,2022-11-25,"['cs.cl', 'cs.hc']",2211.14228v6.pdf," In order to train children's ability to ask curiosity-driven questions, +previous research has explored designing specific exercises relying on +providing semantic and linguistic cues to help formulate such questions. But +despite showing pedagogical efficiency, this method is still limited as it +relies on generating the said cues by hand, which can be a very costly process. +In this context, we propose to leverage advances in the natural language +processing field (NLP) and investigate the efficiency of using a large language +model (LLM) for automating the production of the pedagogical content of a +curious question-asking (QA) training. We study generating the said content +using the ""prompt-based"" method that consists of explaining the task to the LLM +in natural text. We evaluate the output using human experts annotations and +comparisons with hand-generated content. Results suggested indeed the relevance +and usefulness of this content. We also conduct a field study in primary school +(75 children aged 9-10), where we evaluate children's QA performance when +having this training. We compare 3 types of content : 1) hand-generated content +that proposes ""closed"" cues leading to predefined questions; 2) GPT-3-generated +content that proposes the same type of cues; 3) GPT-3-generated content that +proposes ""open"" cues leading to several possible questions. We see a similar QA +performance between the two ""closed"" trainings (showing the scalability of the +approach using GPT-3), and a better one for participants with the ""open"" +training. These results suggest the efficiency of using LLMs to support +children in generating more curious questions, using a natural language +prompting approach that affords usability by teachers and other users not +specialists of AI techniques. Furthermore, results also show that open-ended +content may be more suitable for training curious question-asking skills. +" +Towards using Few-Shot Prompt Learning for Automating Model Completion,Meriem Ben Chaaben,http://arxiv.org/pdf/2212.03404v1.pdf,2022-12-07,"['cs.se', 'cs.cl']",2212.03404v1.pdf," We propose a simple yet a novel approach to improve completion in domain +modeling activities. Our approach exploits the power of large language models +by using few-shot prompt learning without the need to train or fine-tune those +models with large datasets that are scarce in this field. We implemented our +approach and tested it on the completion of static and dynamic domain diagrams. +Our initial evaluation shows that such an approach is effective and can be +integrated in different ways during the modeling activities. +" +Are Prompt-based Models Clueless?,Pride Kavumba,http://arxiv.org/pdf/2205.09295v2.pdf,2022-05-19,['cs.cl'],2205.09295v2.pdf," Finetuning large pre-trained language models with a task-specific head has +advanced the state-of-the-art on many natural language understanding +benchmarks. However, models with a task-specific head require a lot of training +data, making them susceptible to learning and exploiting dataset-specific +superficial cues that do not generalize to other datasets. Prompting has +reduced the data requirement by reusing the language model head and formatting +the task input to match the pre-training objective. Therefore, it is expected +that few-shot prompt-based models do not exploit superficial cues. This paper +presents an empirical examination of whether few-shot prompt-based models also +exploit superficial cues. Analyzing few-shot prompt-based models on MNLI, SNLI, +HANS, and COPA has revealed that prompt-based models also exploit superficial +cues. While the models perform well on instances with superficial cues, they +often underperform or only marginally outperform random accuracy on instances +without superficial cues. +" +Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models,Ratish Puduppully,http://arxiv.org/pdf/2305.13085v2.pdf,2023-05-22,['cs.cl'],2305.13085v2.pdf," This study investigates machine translation between related languages i.e., +languages within the same family that share linguistic characteristics such as +word order and lexical similarity. Machine translation through few-shot +prompting leverages a small set of translation pair examples to generate +translations for test sentences. This procedure requires the model to learn how +to generate translations while simultaneously ensuring that token ordering is +maintained to produce a fluent and accurate translation. We propose that for +related languages, the task of machine translation can be simplified by +leveraging the monotonic alignment characteristic of such languages. We +introduce DecoMT, a novel approach of few-shot prompting that decomposes the +translation process into a sequence of word chunk translations. Through +automatic and human evaluation conducted on multiple related language pairs +across various language families, we demonstrate that our proposed approach of +decomposed prompting surpasses multiple established few-shot baseline +approaches. For example, DecoMT outperforms the strong few-shot prompting BLOOM +model with an average improvement of 8 chrF++ scores across the examined +languages. +" +Multilingual Large Language Models Are Not (Yet) Code-Switchers,Ruochen Zhang,http://arxiv.org/pdf/2305.14235v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14235v2.pdf," Multilingual Large Language Models (LLMs) have recently shown great +capabilities in a wide range of tasks, exhibiting state-of-the-art performance +through zero-shot or few-shot prompting methods. While there have been +extensive studies on their abilities in monolingual tasks, the investigation of +their potential in the context of code-switching (CSW), the practice of +alternating languages within an utterance, remains relatively uncharted. In +this paper, we provide a comprehensive empirical analysis of various +multilingual LLMs, benchmarking their performance across four tasks: sentiment +analysis, machine translation, summarization and word-level language +identification. Our results indicate that despite multilingual LLMs exhibiting +promising outcomes in certain tasks using zero or few-shot prompting, they +still underperform in comparison to fine-tuned models of much smaller scales. +We argue that current ""multilingualism"" in LLMs does not inherently imply +proficiency with code-switching texts, calling for future research to bridge +this discrepancy. +" +"Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango",Aman Madaan,http://arxiv.org/pdf/2209.07686v2.pdf,2022-09-16,"['cs.cl', 'cs.ai', 'cs.lg']",2209.07686v2.pdf," The past decade has witnessed dramatic gains in natural language processing +and an unprecedented scaling of large language models. These developments have +been accelerated by the advent of few-shot techniques such as chain of thought +(CoT) prompting. Specifically, CoT pushes the performance of large language +models in a few-shot setup by augmenting the prompts with intermediate steps. +Despite impressive results across various tasks, the reasons behind their +success have not been explored. This work uses counterfactual prompting to +develop a deeper understanding of CoT-based few-shot prompting mechanisms in +large language models. We first systematically identify and define the key +components of a prompt: symbols, patterns, and text. Then, we devise and +conduct an exhaustive set of experiments across four different tasks, by +querying the model with counterfactual prompts where only one of these +components is altered. Our experiments across three models (PaLM, GPT-3, and +CODEX) reveal several surprising findings and brings into question the +conventional wisdom around few-shot prompting. First, the presence of factual +patterns in a prompt is practically immaterial to the success of CoT. Second, +our results conclude that the primary role of intermediate steps may not be to +facilitate learning how to solve a task. The intermediate steps are rather a +beacon for the model to realize what symbols to replicate in the output to form +a factual answer. Further, text imbues patterns with commonsense knowledge and +meaning. Our empirical and qualitative analysis reveals that a symbiotic +relationship between text and patterns explains the success of few-shot +prompting: text helps extract commonsense from the question to help patterns, +and patterns enforce task understanding and direct text generation. +" +Understanding How Model Size Affects Few-shot Instruction Prompting,Ayrton San Joaquin,http://arxiv.org/pdf/2212.01907v1.pdf,2022-12-04,"['cs.cl', 'cs.lg', 'stat.ml']",2212.01907v1.pdf," Large Language Models are affected by the phenomena of memorizing and +forgetting their training data. But how do these vary by model size? We work +towards this question by investigating how the model size affects the model's +ability to discriminate a word's meaning in a given context. We introduce a +dataset called DeltaWords, which evaluates a model's ability to follow +instructions to select a sentence which replaces the target word with its +antonym. We show a weak inverse scaling trend, where task accuracy degrades as +model size increase, under extremely few-shot prompting regimes. We show that +increasing the number of examples tend to disproportionately benefit larger +models than smaller models. +" +Prompted LLMs as Chatbot Modules for Long Open-domain Conversation,Gibbeum Lee,http://arxiv.org/pdf/2305.04533v1.pdf,2023-05-08,"['cs.cl', 'cs.ai', 'cs.lg']",2305.04533v1.pdf," In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for +creating high-quality conversational agents without the need for fine-tuning. +Our method utilizes pre-trained large language models (LLMs) as individual +modules for long-term consistency and flexibility, by using techniques such as +few-shot prompting, chain-of-thought (CoT), and external memory. Our human +evaluation results show that MPC is on par with fine-tuned chatbot models in +open-domain conversations, making it an effective solution for creating +consistent and engaging chatbots. +" +Internet-augmented language models through few-shot prompting for open-domain question answering,Angeliki Lazaridou,http://arxiv.org/pdf/2203.05115v2.pdf,2022-03-10,"['cs.cl', 'cs.lg']",2203.05115v2.pdf," In this work, we aim to capitalize on the unique few-shot capabilities of +large-scale language models (LSLMs) to overcome some of their challenges with +respect to grounding to factual and up-to-date information. Motivated by +semi-parametric language models (LMs), which ground their decisions in external +retrieved evidence, we use few-shot prompting to learn to condition LMs on +information returned from the web using Google Search, a broad and constantly +updated knowledge source. Our approach does not involve fine-tuning or learning +additional parameters, thus making it applicable to any LM, offering therefore +a strong baseline. Indeed, we find that LMs conditioned on the web surpass +performance of closed-book models of similar, or even larger, model sizes in +open-domain question answering. Finally, we find that increasing the +inference-time compute of models, achieved via using multiple retrieved +evidences to generate multiple answers followed by a reranking stage that uses +scores generated by the same LMs, leads to better performance and alleviates +lower performance of smaller few-shot LMs. All in all, our findings suggest +that it might be beneficial to slow down the race towards the biggest model and +instead shift attention towards finding more effective ways to use models, +including but not limited to, better prompting or increasing inference-time +compute. +" +Decomposed Prompting: A Modular Approach for Solving Complex Tasks,Tushar Khot,http://arxiv.org/pdf/2210.02406v2.pdf,2022-10-05,['cs.cl'],2210.02406v2.pdf," Few-shot prompting is a surprisingly powerful way to use Large Language +Models (LLMs) to solve various tasks. However, this approach struggles as the +task complexity increases or when the individual reasoning steps of the task +themselves are hard to learn, especially when embedded in more complex tasks. +To address this, we propose Decomposed Prompting, a new approach to solve +complex tasks by decomposing them (via prompting) into simpler sub-tasks that +can be delegated to a library of prompting-based LLMs dedicated to these +sub-tasks. This modular structure allows each prompt to be optimized for its +specific sub-task, further decomposed if necessary, and even easily replaced +with more effective prompts, trained models, or symbolic functions if desired. +We show that the flexibility and modularity of Decomposed Prompting allows it +to outperform prior work on few-shot prompting using GPT3. On symbolic +reasoning tasks, we can further decompose sub-tasks that are hard for LLMs into +even simpler solvable sub-tasks. When the complexity comes from the input +length, we can recursively decompose the task into the same task but with +smaller inputs. We also evaluate our approach on textual multi-step reasoning +tasks: on long-context multi-hop QA task, we can more effectively teach the +sub-tasks via our separate sub-tasks prompts; and on open-domain multi-hop QA, +we can incorporate a symbolic information retrieval within our decomposition +framework, leading to improved performance on both tasks. Datasets, Code and +Prompts available at https://github.com/allenai/DecomP. +" +Language Model Crossover: Variation through Few-Shot Prompting,Elliot Meyerson,http://arxiv.org/pdf/2302.12170v2.pdf,2023-02-23,['cs.ne'],2302.12170v2.pdf," This paper pursues the insight that language models naturally enable an +intelligent variation operator similar in spirit to evolutionary crossover. In +particular, language models of sufficient scale demonstrate in-context +learning, i.e. they can learn from associations between a small number of input +patterns to generate outputs incorporating such associations (also called +few-shot prompting). This ability can be leveraged to form a simple but +powerful variation operator, i.e. to prompt a language model with a few +text-based genotypes (such as code, plain-text sentences, or equations), and to +parse its corresponding output as those genotypes' offspring. The promise of +such language model crossover (which is simple to implement and can leverage +many different open-source language models) is that it enables a simple +mechanism to evolve semantically-rich text representations (with few +domain-specific tweaks), and naturally benefits from current progress in +language models. Experiments in this paper highlight the versatility of +language-model crossover, through evolving binary bit-strings, sentences, +equations, text-to-image prompts, and Python code. The conclusion is that +language model crossover is a promising method for evolving genomes +representable as text. +" +Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes,Cheng-Yu Hsieh,http://arxiv.org/pdf/2305.02301v2.pdf,2023-05-03,"['cs.cl', 'cs.ai', 'cs.lg']",2305.02301v2.pdf," Deploying large language models (LLMs) is challenging because they are memory +inefficient and compute-intensive for practical applications. In reaction, +researchers train smaller task-specific models by either finetuning with human +labels or distilling using LLM-generated labels. However, finetuning and +distillation require large amounts of training data to achieve comparable +performance to LLMs. We introduce Distilling step-by-step, a new mechanism that +(a) trains smaller models that outperform LLMs, and (b) achieves so by +leveraging less training data needed by finetuning or distillation. Our method +extracts LLM rationales as additional supervision for training small models +within a multi-task framework. We present three findings across 4 NLP +benchmarks: First, compared to both finetuning and distillation, our mechanism +achieves better performance with much fewer labeled/unlabeled training +examples. Second, compared to few-shot prompted LLMs, we achieve better +performance using substantially smaller model sizes. Third, we reduce both the +model size and the amount of data required to outperform LLMs; our finetuned +770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% +of available data on a benchmark, whereas standard finetuning the same T5 model +struggles to match even by using 100% of the dataset. We release the code at: +https://github.com/google-research/distilling-step-by-step . +" +Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning,Zhanming Jie,http://arxiv.org/pdf/2305.18170v2.pdf,2023-05-29,['cs.cl'],2305.18170v2.pdf," Chain-of-thought (CoT) prompting with large language models has proven +effective in numerous natural language processing tasks, but designing prompts +that generalize well to diverse problem types can be challenging, especially in +the context of math word problem (MWP) solving. Additionally, it is common to +have a large amount of training data that have a better diversity coverage but +CoT annotations are not available, which limits the use of supervised learning +techniques. To address these issues, we investigate two approaches to leverage +the training data in a few-shot prompting scenario: dynamic program prompting +and program distillation. Our approach is largely inspired by Gao et al., +(2022), where they proposed to replace the CoT with the programs as the +intermediate reasoning step. Such a prompting strategy allows us to accurately +verify the answer correctness through program execution in MWP solving. Our +dynamic program prompting involves annotating the training data by sampling +correct programs from a large language model, while program distillation +involves adapting a smaller model to the program-annotated training data. Our +experiments on three standard MWP datasets demonstrate the effectiveness of +these approaches, yielding significant improvements over previous baselines for +prompting and fine-tuning. Our results suggest that leveraging a large amount +of training data can improve the generalization ability of prompts and boost +the performance of fine-tuned small models in MWP solving. +" +Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis,Md. Arid Hasan,http://arxiv.org/pdf/2308.10783v1.pdf,2023-08-21,"['cs.cl', 'cs.lg', '68t50', 'i.2.7']",2308.10783v1.pdf," The rapid expansion of the digital world has propelled sentiment analysis +into a critical tool across diverse sectors such as marketing, politics, +customer service, and healthcare. While there have been significant +advancements in sentiment analysis for widely spoken languages, low-resource +languages, such as Bangla, remain largely under-researched due to resource +constraints. Furthermore, the recent unprecedented performance of Large +Language Models (LLMs) in various applications highlights the need to evaluate +them in the context of low-resource languages. In this study, we present a +sizeable manually annotated dataset encompassing 33,605 Bangla news tweets and +Facebook comments. We also investigate zero- and few-shot in-context learning +with several language models, including Flan-T5, GPT-4, and Bloomz, offering a +comparative analysis against fine-tuned models. Our findings suggest that +monolingual transformer-based models consistently outperform other models, even +in zero and few-shot scenarios. To foster continued exploration, we intend to +make this dataset and our research tools publicly available to the broader +research community. In the spirit of further research, we plan to make this +dataset and our experimental resources publicly accessible to the wider +research community. +" +FOLIO: Natural Language Reasoning with First-Order Logic,Simeng Han,http://arxiv.org/pdf/2209.00840v1.pdf,2022-09-02,['cs.cl'],2209.00840v1.pdf," We present FOLIO, a human-annotated, open-domain, and logically complex and +diverse dataset for reasoning in natural language (NL), equipped with first +order logic (FOL) annotations. FOLIO consists of 1,435 examples (unique +conclusions), each paired with one of 487 sets of premises which serve as rules +to be used to deductively reason for the validity of each conclusion. The +logical correctness of premises and conclusions is ensured by their parallel +FOL annotations, which are automatically verified by our FOL inference engine. +In addition to the main NL reasoning task, NL-FOL pairs in FOLIO automatically +constitute a new NL-FOL translation dataset using FOL as the logical form. Our +experiments on FOLIO systematically evaluate the FOL reasoning ability of +supervised fine-tuning on medium-sized language models (BERT, RoBERTa) and +few-shot prompting on large language models (GPT-NeoX, OPT, GPT-3, Codex). For +NL-FOL translation, we experiment with GPT-3 and Codex. Our results show that +one of the most capable Large Language Model (LLM) publicly available, GPT-3 +davinci, achieves only slightly better than random results with few-shot +prompting on a subset of FOLIO, and the model is especially bad at predicting +the correct truth values for False and Unknown conclusions. Our dataset and +code are available at https://github.com/Yale-LILY/FOLIO. +" +Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them,Mirac Suzgun,http://arxiv.org/pdf/2210.09261v1.pdf,2022-10-17,"['cs.cl', 'cs.ai']",2210.09261v1.pdf," BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that +focuses on tasks believed to be beyond the capabilities of current language +models. Language models have already made good progress on this benchmark, with +the best model in the BIG-Bench paper outperforming average reported +human-rater results on 65% of the BIG-Bench tasks via few-shot prompting. But +on what tasks do language models fall short of average human-rater performance, +and are those tasks actually unsolvable by current language models? + In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we +call BIG-Bench Hard (BBH). These are the task for which prior language model +evaluations did not outperform the average human-rater. We find that applying +chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the +average human-rater performance on 10 of the 23 tasks, and Codex +(code-davinci-002) to surpass the average human-rater performance on 17 of the +23 tasks. Since many tasks in BBH require multi-step reasoning, few-shot +prompting without CoT, as done in the BIG-Bench evaluations (Srivastava et al., +2022), substantially underestimates the best performance and capabilities of +language models, which is better captured via CoT prompting. As further +analysis, we explore the interaction between CoT and model scale on BBH, +finding that CoT enables emergent task performance on several BBH tasks with +otherwise flat scaling curves. +" +Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data,Xuhai Xu,http://arxiv.org/pdf/2307.14385v3.pdf,2023-07-26,"['cs.cl', '68u35', 'h.5.2; i.2.m']",2307.14385v3.pdf," Advances in large language models (LLMs) have empowered a variety of +applications. However, there is still a significant gap in research when it +comes to understanding and enhancing the capabilities of LLMs in the field of +mental health. In this work, we present the first comprehensive evaluation of +multiple LLMs, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4, on +various mental health prediction tasks via online text data. We conduct a broad +range of experiments, covering zero-shot prompting, few-shot prompting, and +instruction fine-tuning. The results indicate a promising yet limited +performance of LLMs with zero-shot and few-shot prompt designs for the mental +health tasks. More importantly, our experiments show that instruction +finetuning can significantly boost the performance of LLMs for all tasks +simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5, +outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9% +on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%. +They further perform on par with the state-of-the-art task-specific language +model. We also conduct an exploratory case study on LLMs' capability on the +mental health reasoning tasks, illustrating the promising capability of certain +models such as GPT-4. We summarize our findings into a set of action guidelines +for potential methods to enhance LLMs' capability for mental health tasks. +Meanwhile, we also emphasize the important limitations before achieving +deployability in real-world mental health settings, such as known racial and +gender bias. We highlight the important ethical risks accompanying this line of +research. +" +Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm,Laria Reynolds,http://arxiv.org/pdf/2102.07350v1.pdf,2021-02-15,"['cs.cl', 'cs.ai']",2102.07350v1.pdf," Prevailing methods for mapping large generative language models to supervised +tasks may fail to sufficiently probe models' novel capabilities. Using GPT-3 as +a case study, we show that 0-shot prompts can significantly outperform few-shot +prompts. We suggest that the function of few-shot examples in these cases is +better described as locating an already learned task rather than meta-learning. +This analysis motivates rethinking the role of prompts in controlling and +evaluating powerful language models. In this work, we discuss methods of prompt +programming, emphasizing the usefulness of considering prompts through the lens +of natural language. We explore techniques for exploiting the capacity of +narratives and cultural anchors to encode nuanced intentions and techniques for +encouraging deconstruction of a problem into components before producing a +verdict. Informed by this more encompassing theory of prompt programming, we +also introduce the idea of a metaprompt that seeds the model to generate its +own natural language prompts for a range of tasks. Finally, we discuss how +these more general methods of interacting with language models can be +incorporated into existing and future benchmarks and practical applications. +" +Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity,Yao Lu,http://arxiv.org/pdf/2104.08786v2.pdf,2021-04-18,"['cs.cl', 'cs.ai']",2104.08786v2.pdf," When primed with only a handful of training samples, very large, pretrained +language models such as GPT-3 have shown competitive results when compared to +fully-supervised, fine-tuned, large, pretrained language models. We demonstrate +that the order in which the samples are provided can make the difference +between near state-of-the-art and random guess performance: essentially some +permutations are ""fantastic"" and some not. We analyse this phenomenon in +detail, establishing that: it is present across model sizes (even for the +largest current models), it is not related to a specific subset of samples, and +that a given good permutation for one model is not transferable to another. +While one could use a development set to determine which permutations are +performant, this would deviate from the true few-shot setting as it requires +additional annotated data. Instead, we use the generative nature of language +models to construct an artificial development set and based on entropy +statistics of the candidate permutations on this set, we identify performant +prompts. Our method yields a 13% relative improvement for GPT-family models +across eleven different established text classification tasks. +" +Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning,Prasetya Ajie Utama,http://arxiv.org/pdf/2109.04144v1.pdf,2021-09-09,"['cs.cl', 'cs.ai']",2109.04144v1.pdf," Recent prompt-based approaches allow pretrained language models to achieve +strong performances on few-shot finetuning by reformulating downstream tasks as +a language modeling problem. In this work, we demonstrate that, despite its +advantages on low data regimes, finetuned prompt-based models for sentence pair +classification tasks still suffer from a common pitfall of adopting inference +heuristics based on lexical overlap, e.g., models incorrectly assuming a +sentence pair is of the same meaning because they consist of the same set of +words. Interestingly, we find that this particular inference heuristic is +significantly less present in the zero-shot evaluation of the prompt-based +model, indicating how finetuning can be destructive to useful knowledge learned +during the pretraining. We then show that adding a regularization that +preserves pretraining weights is effective in mitigating this destructive +tendency of few-shot finetuning. Our evaluation on three datasets demonstrates +promising improvements on the three corresponding challenge datasets used to +diagnose the inference heuristics. +" +Towards Zero-Label Language Learning,Zirui Wang,http://arxiv.org/pdf/2109.09193v1.pdf,2021-09-19,"['cs.cl', 'cs.lg']",2109.09193v1.pdf," This paper explores zero-label learning in Natural Language Processing (NLP), +whereby no human-annotated data is used anywhere during training and models are +trained purely on synthetic data. At the core of our framework is a novel +approach for better leveraging the powerful pretrained language models. +Specifically, inspired by the recent success of few-shot inference on GPT-3, we +present a training data creation procedure named Unsupervised Data Generation +(UDG), which leverages few-shot prompts to synthesize high-quality training +data without real human annotations. Our method enables zero-label learning as +we train task-specific models solely on the synthetic data, yet we achieve +better or comparable results from strong baseline models trained on +human-labeled data. Furthermore, when mixed with labeled data, our approach +serves as a highly effective data augmentation procedure, achieving new +state-of-the-art results on the SuperGLUE benchmark. +" +P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization,Sha Li,http://arxiv.org/pdf/2202.07615v3.pdf,2022-02-15,['cs.cl'],2202.07615v3.pdf," We propose P4E, an identify-and-localize event detection framework that +integrates the best of few-shot prompting and structured prediction. Our +framework decomposes event detection into an identification task and a +localization task. For the identification task, which we formulate as +multi-label classification, we leverage cloze-based prompting to align our +objective with the pre-training task of language models, allowing our model to +quickly adapt to new event types. We then employ an event type-agnostic +sequence labeling model to localize the event trigger conditioned on the +identification output. This heterogeneous model design allows P4E to quickly +learn new event types without sacrificing the ability to make structured +predictions. Our experiments demonstrate the effectiveness of our proposed +design, and P4E shows superior performance for few-shot event detection on +benchmark datasets FewEvent and MAVEN and comparable performance to SOTA for +fully-supervised event detection on ACE. +" +Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models,Mirac Suzgun,http://arxiv.org/pdf/2205.11503v1.pdf,2022-05-23,['cs.cl'],2205.11503v1.pdf," We propose a method for arbitrary textual style transfer (TST)--the task of +transforming a text into any given style--utilizing general-purpose pre-trained +language models. Our method, Prompt-and-Rerank, is based on a mathematical +formulation of the TST task, decomposing it into three constituent components: +textual similarity, target style strength, and fluency. Specifically, our +method first uses zero-shot or few-shot prompting to obtain a set of candidate +generations in the target style, and then re-ranks these candidates according +to a combination of the three components above. Empirically, our method enables +small pre-trained language models to perform on par with state-of-the-art +large-scale models while consuming two orders of magnitude less compute and +memory. Finally, we conduct a systematic investigation of the effect of model +size and prompt design (e.g., prompt paraphrasing and delimiter-pair choice) on +style transfer quality across seven diverse textual style transfer datasets. +" +Bootstrapping Multilingual Semantic Parsers using Large Language Models,Abhijeet Awasthi,http://arxiv.org/pdf/2210.07313v2.pdf,2022-10-13,"['cs.cl', 'cs.lg']",2210.07313v2.pdf," Despite cross-lingual generalization demonstrated by pre-trained multilingual +models, the translate-train paradigm of transferring English datasets across +multiple languages remains to be a key mechanism for training task-specific +multilingual models. However, for many low-resource languages, the availability +of a reliable translation service entails significant amounts of costly +human-annotated translation pairs. Further, translation services may continue +to be brittle due to domain mismatch between task-specific input text and +general-purpose text used for training translation models. For multilingual +semantic parsing, we demonstrate the effectiveness and flexibility offered by +large language models (LLMs) for translating English datasets into several +languages via few-shot prompting. Through extensive comparisons on two public +datasets, MTOP and MASSIVE, spanning 50 languages and several domains, we show +that our method of translating data using LLMs outperforms a strong +translate-train baseline on 41 out of 50 languages. We study the key design +choices that enable more effective multilingual data translation via prompted +LLMs. +" +Prompting GPT-3 To Be Reliable,Chenglei Si,http://arxiv.org/pdf/2210.09150v2.pdf,2022-10-17,['cs.cl'],2210.09150v2.pdf," Large language models (LLMs) show impressive abilities via few-shot +prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use +in real-world language applications. However, the crucial problem of how to +improve the reliability of GPT-3 is still under-explored. While reliability is +a broad and vaguely defined term, we decompose reliability into four main +facets that correspond to the existing framework of ML safety and are +well-recognized to be important: generalizability, social biases, calibration, +and factuality. Our core contribution is to establish simple and effective +prompts that improve GPT-3's reliability as it: 1) generalizes +out-of-distribution, 2) balances demographic distribution and uses natural +language instructions to reduce social biases, 3) calibrates output +probabilities, and 4) updates the LLM's factual knowledge and reasoning chains. +With appropriate prompts, GPT-3 is more reliable than smaller-scale supervised +models on all these facets. We release all processed datasets, evaluation +scripts, and model predictions. Our systematic empirical study not only sheds +new insights on the reliability of prompting LLMs, but more importantly, our +prompting strategies can help practitioners more reliably use LLMs like GPT-3. +" +Exploring The Landscape of Distributional Robustness for Question Answering Models,Anas Awadalla,http://arxiv.org/pdf/2210.12517v1.pdf,2022-10-22,"['cs.cl', 'cs.lg']",2210.12517v1.pdf," We conduct a large empirical evaluation to investigate the landscape of +distributional robustness in question answering. Our investigation spans over +350 models and 16 question answering datasets, including a diverse set of +architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter +tuning, in-context learning, etc.). We find that, in many cases, model +variations do not affect robustness and in-distribution performance alone +determines out-of-distribution performance. Moreover, our findings indicate +that i) zero-shot and in-context learning methods are more robust to +distribution shifts than fully fine-tuned models; ii) few-shot prompt +fine-tuned models exhibit better robustness than few-shot fine-tuned span +prediction models; iii) parameter-efficient and robustness enhancing training +methods provide no significant robustness improvements. In addition, we +publicly release all evaluations to encourage researchers to further analyze +robustness trends for question answering models. +" +"""Covid vaccine is against Covid but Oxford vaccine is made at Oxford!"" Semantic Interpretation of Proper Noun Compounds",Keshav Kolluru,http://arxiv.org/pdf/2210.13039v1.pdf,2022-10-24,['cs.cl'],2210.13039v1.pdf," Proper noun compounds, e.g., ""Covid vaccine"", convey information in a +succinct manner (a ""Covid vaccine"" is a ""vaccine that immunizes against the +Covid disease""). These are commonly used in short-form domains, such as news +headlines, but are largely ignored in information-seeking applications. To +address this limitation, we release a new manually annotated dataset, ProNCI, +consisting of 22.5K proper noun compounds along with their free-form semantic +interpretations. ProNCI is 60 times larger than prior noun compound datasets +and also includes non-compositional examples, which have not been previously +explored. We experiment with various neural models for automatically generating +the semantic interpretations from proper noun compounds, ranging from few-shot +prompting to supervised learning, with varying degrees of knowledge about the +constituent nouns. We find that adding targeted knowledge, particularly about +the common noun, results in performance gains of upto 2.8%. Finally, we +integrate our model generated interpretations with an existing Open IE system +and observe an 7.5% increase in yield at a precision of 85%. The dataset and +code are available at https://github.com/dair-iitd/pronci. +" +Prompting PaLM for Translation: Assessing Strategies and Performance,David Vilar,http://arxiv.org/pdf/2211.09102v3.pdf,2022-11-16,['cs.cl'],2211.09102v3.pdf," Large language models (LLMs) that have been trained on multilingual but not +parallel text exhibit a remarkable ability to translate between languages. We +probe this ability in an in-depth study of the pathways language model (PaLM), +which has demonstrated the strongest machine translation (MT) performance among +similarly-trained LLMs to date. We investigate various strategies for choosing +translation examples for few-shot prompting, concluding that example quality is +the most important factor. Using optimized prompts, we revisit previous +assessments of PaLM's MT capabilities with more recent test sets, modern MT +metrics, and human evaluation, and find that its performance, while impressive, +still lags that of state-of-the-art supervised systems. We conclude by +providing an analysis of PaLM's MT output which reveals some interesting +properties and prospects for future work. +" +PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models,Minghua Liu,http://arxiv.org/pdf/2212.01558v2.pdf,2022-12-03,"['cs.cv', 'cs.ro']",2212.01558v2.pdf," Generalizable 3D part segmentation is important but challenging in vision and +robotics. Training deep models via conventional supervised methods requires +large-scale 3D datasets with fine-grained part annotations, which are costly to +collect. This paper explores an alternative way for low-shot part segmentation +of 3D point clouds by leveraging a pretrained image-language model, GLIP, which +achieves superior performance on open-vocabulary 2D detection. We transfer the +rich knowledge from 2D to 3D through GLIP-based part detection on point cloud +rendering and a novel 2D-to-3D label lifting algorithm. We also utilize +multi-view 3D priors and few-shot prompt tuning to boost performance +significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets +shows that our method enables excellent zero-shot 3D part segmentation. Our +few-shot version not only outperforms existing few-shot approaches by a large +margin but also achieves highly competitive results compared to the fully +supervised counterpart. Furthermore, we demonstrate that our method can be +directly applied to iPhone-scanned point clouds without significant domain +gaps. +" +Natural Language to Code Generation in Interactive Data Science Notebooks,Pengcheng Yin,http://arxiv.org/pdf/2212.09248v1.pdf,2022-12-19,"['cs.cl', 'cs.se']",2212.09248v1.pdf," Computational notebooks, such as Jupyter notebooks, are interactive computing +environments that are ubiquitous among data scientists to perform data +wrangling and analytic tasks. To measure the performance of AI pair programmers +that automatically synthesize programs for those tasks given natural language +(NL) intents from users, we build ARCADE, a benchmark of 1082 code generation +problems using the pandas data analysis framework in data science notebooks. +ARCADE features multiple rounds of NL-to-code problems from the same notebook. +It requires a model to understand rich multi-modal contexts, such as existing +notebook cells and their execution states as well as previous turns of +interaction. To establish a strong baseline on this challenging task, we +develop PaChiNCo, a 62B code language model (LM) for Python computational +notebooks, which significantly outperforms public code LMs. Finally, we explore +few-shot prompting strategies to elicit better code with step-by-step +decomposition and NL explanation, showing the potential to improve the +diversity and explainability of model predictions. +" +LAMBADA: Backward Chaining for Automated Reasoning in Natural Language,Mehran Kazemi,http://arxiv.org/pdf/2212.13894v2.pdf,2022-12-20,"['cs.ai', 'cs.lg']",2212.13894v2.pdf," Remarkable progress has been made on automated reasoning with natural text, +by using Language Models (LMs) and methods such as Chain-of-Thought and +Selection-Inference. These techniques search for proofs in the forward +direction from axioms to the conclusion, which suffers from a combinatorial +explosion of the search space, and thus high failure rates for problems +requiring longer chains of reasoning. The classical automated reasoning +literature has shown that reasoning in the backward direction (i.e. from the +intended conclusion to supporting axioms) is significantly more efficient at +proof-finding. Importing this intuition into the LM setting, we develop a +Backward Chaining algorithm, called LAMBADA, that decomposes reasoning into +four sub-modules. These sub-modules are simply implemented by few-shot prompted +LM inference. We show that LAMBADA achieves sizable accuracy boosts over +state-of-the-art forward reasoning methods on challenging logical reasoning +datasets, particularly when deep and accurate proof chains are required. +" +Can GPT-3 Perform Statutory Reasoning?,Andrew Blair-Stanek,http://arxiv.org/pdf/2302.06100v2.pdf,2023-02-13,"['cs.cl', 'cs.ai']",2302.06100v2.pdf," Statutory reasoning is the task of reasoning with facts and statutes, which +are rules written in natural language by a legislature. It is a basic legal +skill. In this paper we explore the capabilities of the most capable GPT-3 +model, text-davinci-003, on an established statutory-reasoning dataset called +SARA. We consider a variety of approaches, including dynamic few-shot +prompting, chain-of-thought prompting, and zero-shot prompting. While we +achieve results with GPT-3 that are better than the previous best published +results, we also identify several types of clear errors it makes. We +investigate why these errors happen. We discover that GPT-3 has imperfect prior +knowledge of the actual U.S. statutes on which SARA is based. More importantly, +we create simple synthetic statutes, which GPT-3 is guaranteed not to have seen +during training. We find GPT-3 performs poorly at answering straightforward +questions about these simple synthetic statutes. +" +STREET: A Multi-Task Structured Reasoning and Explanation Benchmark,Danilo Ribeiro,http://arxiv.org/pdf/2302.06729v1.pdf,2023-02-13,"['cs.cl', 'cs.ai', 'i.2.7; i.2.6']",2302.06729v1.pdf," We introduce STREET, a unified multi-task and multi-domain natural language +reasoning and explanation benchmark. Unlike most existing question-answering +(QA) datasets, we expect models to not only answer questions, but also produce +step-by-step structured explanations describing how premises in the question +are used to produce intermediate conclusions that can prove the correctness of +a certain answer. We perform extensive evaluation with popular language models +such as few-shot prompting GPT-3 and fine-tuned T5. We find that these models +still lag behind human performance when producing such structured reasoning +steps. We believe this work will provide a way for the community to better +train and test systems on multi-step reasoning and explanations in natural +language. +" +ADELT: Transpilation Between Deep Learning Frameworks,Linyuan Gong,http://arxiv.org/pdf/2303.03593v1.pdf,2023-03-07,"['cs.cl', 'cs.lg']",2303.03593v1.pdf," We propose Adversarial DEep Learning Transpiler (ADELT) for source-to-source +transpilation between deep learning frameworks. Unlike prior approaches, we +decouple the transpilation of code skeletons and the mapping of API keywords +(an API function name or a parameter name). ADELT transpile code skeletons +using few-shot prompting on big language models. Based on contextual embeddings +extracted by a BERT for code, we train aligned API embeddings in a +domain-adversarial setup, upon which we generate a dictionary for keyword +translation. The model is trained on our unlabeled DL corpus from web crawl +data, without using any hand-crafted rules and parallel data. Our method +outperforms state-of-the-art transpilers on multiple transpilation pairs +including PyTorch-Keras and PyTorch-MXNet by 15.9pts and 12.0pts in exact match +scores respectively. +" +Query2doc: Query Expansion with Large Language Models,Liang Wang,http://arxiv.org/pdf/2303.07678v2.pdf,2023-03-14,"['cs.ir', 'cs.cl']",2303.07678v2.pdf," This paper introduces a simple yet effective query expansion approach, +denoted as query2doc, to improve both sparse and dense retrieval systems. The +proposed method first generates pseudo-documents by few-shot prompting large +language models (LLMs), and then expands the query with generated +pseudo-documents. LLMs are trained on web-scale text corpora and are adept at +knowledge memorization. The pseudo-documents from LLMs often contain highly +relevant information that can aid in query disambiguation and guide the +retrievers. Experimental results demonstrate that query2doc boosts the +performance of BM25 by 3% to 15% on ad-hoc IR datasets, such as MS-MARCO and +TREC DL, without any model fine-tuning. Furthermore, our method also benefits +state-of-the-art dense retrievers in terms of both in-domain and out-of-domain +results. +" +How to Design Translation Prompts for ChatGPT: An Empirical Study,Yuan Gao,http://arxiv.org/pdf/2304.02182v2.pdf,2023-04-05,['cs.cl'],2304.02182v2.pdf," The recently released ChatGPT has demonstrated surprising abilities in +natural language understanding and natural language generation. Machine +translation relies heavily on the abilities of language understanding and +generation. Thus, in this paper, we explore how to assist machine translation +with ChatGPT. We adopt several translation prompts on a wide range of +translations. Our experimental results show that ChatGPT with designed +translation prompts can achieve comparable or better performance over +commercial translation systems for high-resource language translations. We +further evaluate the translation quality using multiple references, and ChatGPT +achieves superior performance compared to commercial systems. We also conduct +experiments on domain-specific translations, the final results show that +ChatGPT is able to comprehend the provided domain keyword and adjust +accordingly to output proper translations. At last, we perform few-shot prompts +that show consistent improvement across different base prompts. Our work +provides empirical evidence that ChatGPT still has great potential in +translations. +" +Boosted Prompt Ensembles for Large Language Models,Silviu Pitis,http://arxiv.org/pdf/2304.05970v1.pdf,2023-04-12,"['cs.cl', 'cs.lg']",2304.05970v1.pdf," Methods such as chain-of-thought prompting and self-consistency have pushed +the frontier of language model reasoning performance with no additional +training. To further improve performance, we propose a prompt ensembling method +for large language models, which uses a small dataset to construct a set of few +shot prompts that together comprise a ``boosted prompt ensemble''. The few shot +examples for each prompt are chosen in a stepwise fashion to be ``hard'' +examples on which the previous step's ensemble is uncertain. We show that this +outperforms single-prompt output-space ensembles and bagged prompt-space +ensembles on the GSM8k and AQuA datasets, among others. We propose both +train-time and test-time versions of boosted prompting that use different +levels of available annotation and conduct a detailed empirical study of our +algorithm. +" +Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models,Jimmy Wei,http://arxiv.org/pdf/2304.13835v3.pdf,2023-04-26,"['cs.cl', 'cs.lg']",2304.13835v3.pdf," Current dialogue research primarily studies pairwise (two-party) +conversations, and does not address the everyday setting where more than two +speakers converse together. In this work, we both collect and evaluate +multi-party conversations to study this more general case. We use the LIGHT +environment to construct grounded conversations, where each participant has an +assigned character to role-play. We thus evaluate the ability of language +models to act as one or more characters in such conversations. Models require +two skills that pairwise-trained models appear to lack: (1) being able to +decide when to talk; (2) producing coherent utterances grounded on multiple +characters. We compare models trained on our new dataset to existing +pairwise-trained dialogue models, as well as large language models with +few-shot prompting. We find that our new dataset, MultiLIGHT, which we will +publicly release, can help bring significant improvements in the group setting. +" +Transferring Procedural Knowledge across Commonsense Tasks,Yifan Jiang,http://arxiv.org/pdf/2304.13867v2.pdf,2023-04-26,['cs.cl'],2304.13867v2.pdf," Stories about everyday situations are an essential part of human +communication, motivating the need to develop AI agents that can reliably +understand these stories. Despite the long list of supervised methods for story +completion and procedural understanding, current AI has no mechanisms to +automatically track and explain procedures in unseen stories. To bridge this +gap, we study the ability of AI models to transfer procedural knowledge to +novel narrative tasks in a transparent manner. We design LEAP: a comprehensive +framework that integrates state-of-the-art modeling architectures, training +regimes, and augmentation strategies based on both natural and synthetic +stories. To address the lack of densely annotated training data, we devise a +robust automatic labeler based on few-shot prompting to enhance the augmented +data. Our experiments with in- and out-of-domain tasks reveal insights into the +interplay of different architectures, training regimes, and augmentation +strategies. LEAP's labeler has a clear positive impact on out-of-domain +datasets, while the resulting dense annotation provides native explainability. +" +Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning,Zhengzhong Liang,http://arxiv.org/pdf/2305.00061v1.pdf,2023-04-28,"['cs.cl', 'cs.ai']",2305.00061v1.pdf," Languages models have been successfully applied to a variety of reasoning +tasks in NLP, yet the language models still suffer from compositional +generalization. In this paper we present Explainable Verbal Reasoner Plus +(EVR+), a reasoning framework that enhances language models' compositional +reasoning ability by (1) allowing the model to explicitly generate and execute +symbolic operators, and (2) allowing the model to decompose a complex task into +several simpler ones in a flexible manner. Compared with its predecessor +Explainable Verbal Reasoner (EVR) and other previous approaches adopting +similar ideas, our framework supports more diverse types of reasoning such as +nested loops and different types of recursion. To evaluate our reasoning +framework, we build a synthetic dataset with five tasks that require +compositional reasoning. Results show that our reasoning framework can enhance +the language model's compositional generalization performance on the five +tasks, using a fine-tuned language model. We also discussed the possibility and +the challenges to combine our reasoning framework with a few-shot prompted +language model. +" +Revisiting Relation Extraction in the era of Large Language Models,Somin Wadhwa,http://arxiv.org/pdf/2305.05003v1.pdf,2023-05-08,['cs.cl'],2305.05003v1.pdf," Relation extraction (RE) is the core NLP task of inferring semantic +relationships between entities from text. Standard supervised RE techniques +entail training modules to tag tokens comprising entity spans and then predict +the relationship between them. Recent work has instead treated the problem as a +\emph{sequence-to-sequence} task, linearizing relations between entities as +target strings to be generated conditioned on the input. Here we push the +limits of this approach, using larger language models (GPT-3 and Flan-T5 large) +than considered in prior work and evaluating their performance on standard RE +tasks under varying levels of supervision. We address issues inherent to +evaluating generative approaches to RE by doing human evaluations, in lieu of +relying on exact matching. Under this refined evaluation, we find that: (1) +Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly +equivalent to existing fully supervised models; (2) Flan-T5 is not as capable +in the few-shot setting, but supervising and fine-tuning it with +Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA +results. We release this model as a new baseline for RE tasks. +" +Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models,Varun Nair,http://arxiv.org/pdf/2305.05982v1.pdf,2023-05-10,"['cs.cl', 'cs.ai', 'cs.lg']",2305.05982v1.pdf," A medical provider's summary of a patient visit serves several critical +purposes, including clinical decision-making, facilitating hand-offs between +providers, and as a reference for the patient. An effective summary is required +to be coherent and accurately capture all the medically relevant information in +the dialogue, despite the complexity of patient-generated language. Even minor +inaccuracies in visit summaries (for example, summarizing ""patient does not +have a fever"" when a fever is present) can be detrimental to the outcome of +care for the patient. + This paper tackles the problem of medical conversation summarization by +discretizing the task into several smaller dialogue-understanding tasks that +are sequentially built upon. First, we identify medical entities and their +affirmations within the conversation to serve as building blocks. We study +dynamically constructing few-shot prompts for tasks by conditioning on relevant +patient information and use GPT-3 as the backbone for our experiments. We also +develop GPT-derived summarization metrics to measure performance against +reference summaries quantitatively. Both our human evaluation study and metrics +for medical correctness show that summaries generated using this approach are +clinically accurate and outperform the baseline approach of summarizing the +dialog in a zero-shot, single-prompt setting. +" +ZARA: Improving Few-Shot Self-Rationalization for Small Language Models,Wei-Lin Chen,http://arxiv.org/pdf/2305.07355v2.pdf,2023-05-12,['cs.cl'],2305.07355v2.pdf," Language models (LMs) that jointly generate end-task answers as well as +free-text rationales are known as self-rationalization models. Recent works +demonstrate great performance gain for self-rationalization by few-shot +prompting LMs with rationale-augmented exemplars. However, the ability to +benefit from explanations only emerges with large-scale LMs, which have poor +accessibility. In this work, we explore the less-studied setting of leveraging +explanations for small LMs to improve few-shot self-rationalization. We first +revisit the relationship between rationales and answers. Inspired by the +implicit mental process of how human beings assess explanations, we present a +novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to +automatically construct pseudo-parallel data for self-training by reducing the +problem of plausibility judgement to natural language inference. Experimental +results show ZARA achieves SOTA performance on the FEB benchmark, for both the +task accuracy and the explanation metric. In addition, we conduct human and +quantitative evaluation validating ZARA's ability to automatically identify +plausible and accurate rationale-answer pairs. +" +Natural Language Decomposition and Interpretation of Complex Utterances,Harsh Jhamtani,http://arxiv.org/pdf/2305.08677v1.pdf,2023-05-15,['cs.cl'],2305.08677v1.pdf," Natural language interfaces often require supervised data to translate user +requests into programs, database queries, or other structured intent +representations. During data collection, it can be difficult to anticipate and +formalize the full range of user needs -- for example, in a system designed to +handle simple requests (like $\textit{find my meetings tomorrow}$ or +$\textit{move my meeting with my manager to noon})$, users may also express +more elaborate requests (like $\textit{swap all my calls on Monday and +Tuesday}$). We introduce an approach for equipping a simple language-to-code +model to handle complex utterances via a process of hierarchical natural +language decomposition. Our approach uses a pre-trained language model to +decompose a complex utterance into a sequence of smaller natural language +steps, then interprets each step using the language-to-code model. To test our +approach, we collect and release DeCU -- a new NL-to-program benchmark to +evaluate Decomposition of Complex Utterances. Experiments show that the +proposed approach enables the interpretation of complex utterances with almost +no complex training data, while outperforming standard few-shot prompting +approaches. +" +Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models,Emily Reif,http://arxiv.org/pdf/2305.11364v2.pdf,2023-05-19,"['cs.cl', 'cs.ai']",2305.11364v2.pdf," Large language models (LLMs) can be used to generate smaller, more refined +datasets via few-shot prompting for benchmarking, fine-tuning or other use +cases. However, understanding and evaluating these datasets is difficult, and +the failure modes of LLM-generated data are still not well understood. +Specifically, the data can be repetitive in surprising ways, not only +semantically but also syntactically and lexically. We present LinguisticLens, a +novel inter-active visualization tool for making sense of and analyzing +syntactic diversity of LLM-generated datasets. LinguisticLens clusters text +along syntactic, lexical, and semantic axes. It supports hierarchical +visualization of a text dataset, allowing users to quickly scan for an overview +and inspect individual examples. The live demo is available at +shorturl.at/zHOUV. +" +Improved Compositional Generalization by Generating Demonstrations for Meta-Learning,Sam Spilsbury,http://arxiv.org/pdf/2305.13092v1.pdf,2023-05-22,['cs.cl'],2305.13092v1.pdf," Meta-learning and few-shot prompting are viable methods to induce certain +types of compositional behaviour. However, these methods can be very sensitive +to the choice of support examples used. Choosing good supports from the +training data for a given test query is already a difficult problem, but in +some cases solving this may not even be enough. We consider a grounded language +learning problem (gSCAN) where good support examples for certain test splits +might not even exist in the training data, or would be infeasible to search +for. We design an agent which instead generates possible supports which are +relevant to the test query and current state of the world, then uses these +supports via meta-learning to solve the test query. We show substantially +improved performance on a previously unsolved compositional behaviour split +without a loss of performance on other splits. Further experiments show that in +this case, searching for relevant demonstrations even with an oracle function +is not sufficient to attain good performance when using meta-learning. +" +SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations,Jesus Solano,http://arxiv.org/pdf/2305.13235v2.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.13235v2.pdf," Explaining the decisions of neural models is crucial for ensuring their +trustworthiness at deployment time. Using Natural Language Explanations (NLEs) +to justify a model's predictions has recently gained increasing interest. +However, this approach usually demands large datasets of human-written NLEs for +the ground-truth answers, which are expensive and potentially infeasible for +some applications. For models to generate high-quality NLEs when only a few +NLEs are available, the fine-tuning of Pre-trained Language Models (PLMs) in +conjunction with prompt-based learning recently emerged. However, PLMs +typically have billions of parameters, making fine-tuning expensive. We propose +SparseFit, a sparse few-shot fine-tuning strategy that leverages discrete +prompts to jointly generate predictions and NLEs. We experiment with SparseFit +on the T5 model and four datasets and compare it against state-of-the-art +parameter-efficient fine-tuning techniques. We perform automatic and human +evaluations to assess the quality of the model-generated NLEs, finding that +fine-tuning only 6.8% of the model parameters leads to competitive results for +both the task performance and the quality of the NLEs. +" +Towards Legally Enforceable Hate Speech Detection for Public Forums,Chu Fei Luo,http://arxiv.org/pdf/2305.13677v2.pdf,2023-05-23,['cs.cl'],2305.13677v2.pdf," Hate speech causes widespread and deep-seated societal issues. Proper +enforcement of hate speech laws is key for protecting groups of people against +harmful and discriminatory language. However, determining what constitutes hate +speech is a complex task that is highly open to subjective interpretations. +Existing works do not align their systems with enforceable definitions of hate +speech, which can make their outputs inconsistent with the goals of regulators. +This research introduces a new perspective and task for enforceable hate speech +detection centred around legal definitions, and a dataset annotated on +violations of eleven possible definitions by legal experts. Given the challenge +of identifying clear, legally enforceable instances of hate speech, we augment +the dataset with expert-generated samples and an automatically mined challenge +set. We experiment with grounding the model decision in these definitions using +zero-shot and few-shot prompting. We then report results on several large +language models (LLMs). With this task definition, automatic hate speech +detection can be more closely aligned to enforceable laws, and hence assist in +more rigorous enforcement of legal protections against harmful speech in public +forums. +" +PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents,Simeng Sun,http://arxiv.org/pdf/2305.14564v1.pdf,2023-05-23,['cs.cl'],2305.14564v1.pdf," Strategies such as chain-of-thought prompting improve the performance of +large language models (LLMs) on complex reasoning tasks by decomposing input +examples into intermediate steps. However, it remains unclear how to apply such +methods to reason over long input documents, in which both the decomposition +and the output of each intermediate step are non-trivial to obtain. In this +work, we propose PEARL, a prompting framework to improve reasoning over long +documents, which consists of three stages: action mining, plan formulation, and +plan execution. More specifically, given a question about a long document, +PEARL decomposes the question into a sequence of actions (e.g., SUMMARIZE, +FIND_EVENT, FIND_RELATION) and then executes them over the document to obtain +the answer. Each stage of PEARL is implemented via zero-shot or few-shot +prompting of LLMs (in our work, GPT-4) with minimal human input. We evaluate +PEARL on a challenging subset of the QuALITY dataset, which contains questions +that require complex reasoning over long narrative texts. PEARL outperforms +zero-shot and chain-of-thought prompting on this dataset, and ablation +experiments show that each stage of PEARL is critical to its performance. +Overall, PEARL is a first step towards leveraging LLMs to reason over long +documents. +" +Large Language Model Distillation Doesn't Need a Teacher,Ananya Harsh Jha,http://arxiv.org/pdf/2305.14864v1.pdf,2023-05-24,['cs.cl'],2305.14864v1.pdf," Knowledge distillation trains a smaller student model to match the output +distribution of a larger teacher to maximize the end-task performance under +computational constraints. However, existing literature on language model +distillation primarily focuses on compressing encoder-only models that are then +specialized by task-specific supervised finetuning. We need to rethink this +setup for more recent large language models with tens to hundreds of billions +of parameters. Task-specific finetuning is impractical at this scale, and model +performance is often measured using zero/few-shot prompting. Thus, in this +work, we advocate for task-agnostic zero-shot evaluated distillation for large +language models without access to end-task finetuning data. We propose a +teacher-free task-agnostic distillation method, which uses a truncated version +of the larger model for initialization, and continues pretraining this model +using a language modeling objective. Our teacher-free method shines in a +distillation regime where it is infeasible to fit both the student and teacher +into the GPU memory. Despite its simplicity, our method can effectively reduce +the model size by 50\%, matching or outperforming the vanilla distillation +method on perplexity and accuracy on 13 zero-shot end-tasks while being 1.5x +computationally efficient. +" +Revisiting non-English Text Simplification: A Unified Multilingual Benchmark,Michael J. Ryan,http://arxiv.org/pdf/2305.15678v1.pdf,2023-05-25,"['cs.cl', 'cs.ai']",2305.15678v1.pdf," Recent advancements in high-quality, large-scale English resources have +pushed the frontier of English Automatic Text Simplification (ATS) research. +However, less work has been done on multilingual text simplification due to the +lack of a diverse evaluation benchmark that covers complex-simple sentence +pairs in many languages. This paper introduces the MultiSim benchmark, a +collection of 27 resources in 12 distinct languages containing over 1.7 million +complex-simple sentence pairs. This benchmark will encourage research in +developing more effective multilingual text simplification models and +evaluation metrics. Our experiments using MultiSim with pre-trained +multilingual language models reveal exciting performance improvements from +multilingual training in non-English settings. We observe strong performance +from Russian in zero-shot cross-lingual transfer to low-resource languages. We +further show that few-shot prompting with BLOOM-176b achieves comparable +quality to reference simplifications outperforming fine-tuned models in most +languages. We validate these findings through human evaluation. +" +Do GPTs Produce Less Literal Translations?,Vikas Raunak,http://arxiv.org/pdf/2305.16806v4.pdf,2023-05-26,"['cs.cl', 'cs.ai']",2305.16806v4.pdf," Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose +language models capable of addressing many natural language generation or +understanding tasks. On the task of Machine Translation (MT), multiple works +have investigated few-shot prompting mechanisms to elicit better translations +from LLMs. However, there has been relatively little investigation on how such +translations differ qualitatively from the translations generated by standard +Neural Machine Translation (NMT) models. In this work, we investigate these +differences in terms of the literalness of translations produced by the two +systems. Using literalness measures involving word alignment and monotonicity, +we find that translations out of English (E-X) from GPTs tend to be less +literal, while exhibiting similar or better scores on MT quality metrics. We +demonstrate that this finding is borne out in human evaluations as well. We +then show that these differences are especially pronounced when translating +sentences that contain idiomatic expressions. +" +Log Parsing: How Far Can ChatGPT Go?,Van-Hoang Le,http://arxiv.org/pdf/2306.01590v2.pdf,2023-06-02,"['cs.se', 'cs.ai']",2306.01590v2.pdf," Software logs play an essential role in ensuring the reliability and +maintainability of large-scale software systems, as they are often the sole +source of runtime information. Log parsing, which converts raw log messages +into structured data, is an important initial step towards downstream log +analytics. In recent studies, ChatGPT, the current cutting-edge large language +model (LLM), has been widely applied to a wide range of software engineering +tasks. However, its performance in automated log parsing remains unclear. In +this paper, we evaluate ChatGPT's ability to undertake log parsing by +addressing two research questions. (1) Can ChatGPT effectively parse logs? (2) +How does ChatGPT perform with different prompting methods? Our results show +that ChatGPT can achieve promising results for log parsing with appropriate +prompts, especially with few-shot prompting. Based on our findings, we outline +several challenges and opportunities for ChatGPT-based log parsing. +" +Large Language Model Augmented Narrative Driven Recommendations,Sheshera Mysore,http://arxiv.org/pdf/2306.02250v2.pdf,2023-06-04,"['cs.ir', 'cs.cl']",2306.02250v2.pdf," Narrative-driven recommendation (NDR) presents an information access problem +where users solicit recommendations with verbose descriptions of their +preferences and context, for example, travelers soliciting recommendations for +points of interest while describing their likes/dislikes and travel +circumstances. These requests are increasingly important with the rise of +natural language-based conversational interfaces for search and recommendation +systems. However, NDR lacks abundant training data for models, and current +platforms commonly do not support these requests. Fortunately, classical +user-item interaction datasets contain rich textual data, e.g., reviews, which +often describe user preferences and context - this may be used to bootstrap +training for NDR models. In this work, we explore using large language models +(LLMs) for data augmentation to train NDR models. We use LLMs for authoring +synthetic narrative queries from user-item interactions with few-shot prompting +and train retrieval models for NDR on synthetic queries and user-item +interaction data. Our experiments demonstrate that this is an effective +strategy for training small-parameter retrieval models that outperform other +retrieval and LLM baselines for narrative-driven recommendation. +" +Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering,Zixian Huang,http://arxiv.org/pdf/2306.04508v1.pdf,2023-06-07,"['cs.cl', 'cs.ai']",2306.04508v1.pdf," Whereas the recent emergence of large language models (LLMs) like ChatGPT has +exhibited impressive general performance, it still has a large gap with +fully-supervised models on specific tasks such as multi-span question +answering. Previous researches found that in-context learning is an effective +approach to exploiting LLM, by using a few task-related labeled data as +demonstration examples to construct a few-shot prompt for answering new +questions. A popular implementation is to concatenate a few questions and their +correct answers through simple templates, informing LLM of the desired output. +In this paper, we propose a novel way of employing labeled data such that it +also informs LLM of some undesired output, by extending demonstration examples +with feedback about answers predicted by an off-the-shelf model, e.g., correct, +incorrect, or incomplete. Experiments on three multi-span question answering +datasets as well as a keyphrase extraction dataset show that our new prompting +strategy consistently improves LLM's in-context learning performance. +" +Product Information Extraction using ChatGPT,Alexander Brinkmann,http://arxiv.org/pdf/2306.14921v1.pdf,2023-06-23,"['cs.cl', 'cs.ir']",2306.14921v1.pdf," Structured product data in the form of attribute/value pairs is the +foundation of many e-commerce applications such as faceted product search, +product comparison, and product recommendation. Product offers often only +contain textual descriptions of the product attributes in the form of titles or +free text. Hence, extracting attribute/value pairs from textual product +descriptions is an essential enabler for e-commerce applications. In order to +excel, state-of-the-art product information extraction methods require large +quantities of task-specific training data. The methods also struggle with +generalizing to out-of-distribution attributes and attribute values that were +not a part of the training data. Due to being pre-trained on huge amounts of +text as well as due to emergent effects resulting from the model size, Large +Language Models like ChatGPT have the potential to address both of these +shortcomings. This paper explores the potential of ChatGPT for extracting +attribute/value pairs from product descriptions. We experiment with different +zero-shot and few-shot prompt designs. Our results show that ChatGPT achieves a +performance similar to a pre-trained language model but requires much smaller +amounts of training data and computation for fine-tuning. +" +SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical Summarization,Yash Mathur,http://arxiv.org/pdf/2306.17384v1.pdf,2023-06-30,['cs.cl'],2306.17384v1.pdf," Medical dialogue summarization is challenging due to the unstructured nature +of medical conversations, the use of medical terminology in gold summaries, and +the need to identify key information across multiple symptom sets. We present a +novel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA +2023 Shared Task. Our approach for section-wise summarization (Task A) is a +two-stage process of selecting semantically similar dialogues and using the +top-k similar dialogues as in-context examples for GPT-4. For full-note +summarization (Task B), we use a similar solution with k=1. We achieved 3rd +place in Task A (2nd among all teams), 4th place in Task B Division Wise +Summarization (2nd among all teams), 15th place in Task A Section Header +Classification (9th among all teams), and 8th place among all teams in Task B. +Our results highlight the effectiveness of few-shot prompting for this task, +though we also identify several weaknesses of prompting-based approaches. We +compare GPT-4 performance with several finetuned baselines. We find that GPT-4 +summaries are more abstractive and shorter. We make our code publicly +available. +" +Building Cooperative Embodied Agents Modularly with Large Language Models,Hongxin Zhang,http://arxiv.org/pdf/2307.02485v1.pdf,2023-07-05,"['cs.ai', 'cs.cl', 'cs.cv']",2307.02485v1.pdf," Large Language Models (LLMs) have demonstrated impressive planning abilities +in single-agent embodied tasks across various domains. However, their capacity +for planning and communication in multi-agent cooperation remains unclear, even +though these are crucial skills for intelligent embodied agents. In this paper, +we present a novel framework that utilizes LLMs for multi-agent cooperation and +tests it in various embodied environments. Our framework enables embodied +agents to plan, communicate, and cooperate with other embodied agents or humans +to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs, +such as GPT-4, can surpass strong planning-based methods and exhibit emergent +effective communication using our framework without requiring fine-tuning or +few-shot prompting. We also discover that LLM-based agents that communicate in +natural language can earn more trust and cooperate more effectively with +humans. Our research underscores the potential of LLMs for embodied AI and lays +the foundation for future research in multi-agent cooperation. Videos can be +found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/. +" +MultiQG-TI: Towards Question Generation from Multi-modal Sources,Zichao Wang,http://arxiv.org/pdf/2307.04643v1.pdf,2023-07-07,"['cs.cl', 'cs.ai']",2307.04643v1.pdf," We study the new problem of automatic question generation (QG) from +multi-modal sources containing images and texts, significantly expanding the +scope of most of the existing work that focuses exclusively on QG from only +textual sources. We propose a simple solution for our new problem, called +MultiQG-TI, which enables a text-only question generator to process visual +input in addition to textual input. Specifically, we leverage an image-to-text +model and an optical character recognition model to obtain the textual +description of the image and extract any texts in the image, respectively, and +then feed them together with the input texts to the question generator. We only +fine-tune the question generator while keeping the other components fixed. On +the challenging ScienceQA dataset, we demonstrate that MultiQG-TI significantly +outperforms ChatGPT with few-shot prompting, despite having hundred-times less +trainable parameters. Additional analyses empirically confirm the necessity of +both visual and textual signals for QG and show the impact of various modeling +choices. +" +Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?,Cheng-En Wu,http://arxiv.org/pdf/2307.11978v1.pdf,2023-07-22,"['cs.cv', 'cs.ai', 'cs.lg']",2307.11978v1.pdf," Vision-language models such as CLIP learn a generic text-image embedding from +large-scale training data. A vision-language model can be adapted to a new +classification task through few-shot prompt tuning. We find that such a prompt +tuning process is highly robust to label noises. This intrigues us to study the +key reasons contributing to the robustness of the prompt tuning paradigm. We +conducted extensive experiments to explore this property and find the key +factors are: 1) the fixed classname tokens provide a strong regularization to +the optimization of the model, reducing gradients induced by the noisy samples; +2) the powerful pre-trained image-text embedding that is learned from diverse +and generic web data provides strong prior knowledge for image classification. +Further, we demonstrate that noisy zero-shot predictions from CLIP can be used +to tune its own prompt, significantly enhancing prediction accuracy in the +unsupervised setting. The code is available at https://github.com/CEWu/PTNL. +" +Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions,Skyler Wu,http://arxiv.org/pdf/2307.13339v1.pdf,2023-07-25,"['cs.cl', 'cs.ai']",2307.13339v1.pdf," Chain-of-thought (CoT) prompting has been shown to empirically improve the +accuracy of large language models (LLMs) on various question answering tasks. +While understanding why CoT prompting is effective is crucial to ensuring that +this phenomenon is a consequence of desired model behavior, little work has +addressed this; nonetheless, such an understanding is a critical prerequisite +for responsible model deployment. We address this question by leveraging +gradient-based feature attribution methods which produce saliency scores that +capture the influence of input tokens on model output. Specifically, we probe +several open-source LLMs to investigate whether CoT prompting affects the +relative importances they assign to particular input tokens. Our results +indicate that while CoT prompting does not increase the magnitude of saliency +scores attributed to semantically relevant tokens in the prompt compared to +standard few-shot prompting, it increases the robustness of saliency scores to +question perturbations and variations in model output. +" +Low-Parameter Federated Learning with Large Language Models,Jingang Jiang,http://arxiv.org/pdf/2307.13896v1.pdf,2023-07-26,['cs.dc'],2307.13896v1.pdf," We study few-shot Natural Language Understanding (NLU) tasks with Large +Language Models (LLMs) in federated learning (FL) scenarios. It is a +challenging task due to limited labeled data and communication capacities in +FL, especially with mobile devices. Recent studies show LLMs can be prompted to +perform few-shot NLU tasks like sentiment analysis and arithmetic reasoning. +However, the huge sizes of LLMs result in high computation and communication +costs, making classical FL schemes impractical. To address these challenges, we +propose Low-Parameter Federated Learning (LP-FL). LP-FL combines few-shot +prompt learning from LLMs with efficient communication and federating +techniques. Our approach enables federated clients to assign soft labels to +unlabeled data using gradually learned knowledge from the global model. Through +iterative soft-label assigning, we continually expand the labeled set during +the FL process. Additionally, to reduce computation and communication costs, +LP-FL utilizes the Low-Rank Adaptation (LoRA) technique for compact learnable +parameter construction, efficient local model fine-tuning, and affordable +global model federation. LP-FL consistently outperforms Full-Parameter +Federated Learning (FP-FL) in sentiment analysis tasks across various FL +settings. Its resistance to overfitting allows LP-FL to equal or surpass +centralized training in few-shot scenarios. +" +Large Language Model Prompt Chaining for Long Legal Document Classification,Dietrich Trautmann,http://arxiv.org/pdf/2308.04138v1.pdf,2023-08-08,['cs.cl'],2308.04138v1.pdf," Prompting is used to guide or steer a language model in generating an +appropriate response that is consistent with the desired outcome. Chaining is a +strategy used to decompose complex tasks into smaller, manageable components. +In this study, we utilize prompt chaining for extensive legal document +classification tasks, which present difficulties due to their intricate +domain-specific language and considerable length. Our approach begins with the +creation of a concise summary of the original document, followed by a semantic +search for related exemplar texts and their corresponding annotations from a +training corpus. Finally, we prompt for a label - based on the task - to +assign, by leveraging the in-context learning from the few-shot prompt. We +demonstrate that through prompt chaining, we can not only enhance the +performance over zero-shot, but also surpass the micro-F1 score achieved by +larger models, such as ChatGPT zero-shot, using smaller models. +" +FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models,Liwen Zhang,http://arxiv.org/pdf/2308.09975v1.pdf,2023-08-19,['cs.cl'],2308.09975v1.pdf," Large language models (LLMs) have demonstrated exceptional performance in +various natural language processing tasks, yet their efficacy in more +challenging and domain-specific tasks remains largely unexplored. This paper +presents FinEval, a benchmark specifically designed for the financial domain +knowledge in the LLMs. FinEval is a collection of high-quality multiple-choice +questions covering Finance, Economy, Accounting, and Certificate. It includes +4,661 questions spanning 34 different academic subjects. To ensure a +comprehensive model performance evaluation, FinEval employs a range of prompt +types, including zero-shot and few-shot prompts, as well as answer-only and +chain-of-thought prompts. Evaluating state-of-the-art Chinese and English LLMs +on FinEval, the results show that only GPT-4 achieved an accuracy close to 70% +in different prompt settings, indicating significant growth potential for LLMs +in the financial domain knowledge. Our work offers a more comprehensive +financial knowledge evaluation benchmark, utilizing data of mock exams and +covering a wide range of evaluated LLMs. +" +Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries,Noel Ngu,http://arxiv.org/pdf/2308.11189v1.pdf,2023-08-22,"['cs.cl', 'cs.ai', 'cs.lg']",2308.11189v1.pdf," Error prediction in large language models often relies on domain-specific +information. In this paper, we present measures for quantification of error in +the response of a large language model based on the diversity of responses to a +given prompt - hence independent of the underlying application. We describe how +three such measures - based on entropy, Gini impurity, and centroid distance - +can be employed. We perform a suite of experiments on multiple datasets and +temperature settings to demonstrate that these measures strongly correlate with +the probability of failure. Additionally, we present empirical results +demonstrating how these measures can be applied to few-shot prompting, +chain-of-thought reasoning, and error detection. +" +Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis,Chang Liu,http://arxiv.org/pdf/2308.11224v2.pdf,2023-08-22,"['cs.ai', 'cs.cl']",2308.11224v2.pdf," Large Language Models (LLMs) have garnered considerable interest within both +academic and industrial. Yet, the application of LLMs to graph data remains +under-explored. In this study, we evaluate the capabilities of four LLMs in +addressing several analytical problems with graph data. We employ four distinct +evaluation metrics: Comprehension, Correctness, Fidelity, and Rectification. +Our results show that: 1) LLMs effectively comprehend graph data in natural +language and reason with graph topology. 2) GPT models can generate logical and +coherent results, outperforming alternatives in correctness. 3) All examined +LLMs face challenges in structural reasoning, with techniques like zero-shot +chain-of-thought and few-shot prompting showing diminished efficacy. 4) GPT +models often produce erroneous answers in multi-answer tasks, raising concerns +in fidelity. 5) GPT models exhibit elevated confidence in their outputs, +potentially hindering their rectification capacities. Notably, GPT-4 has +demonstrated the capacity to rectify responses from GPT-3.5-turbo and its own +previous iterations. The code is available at: +https://github.com/Ayame1006/LLMtoGraph. +" +Prompt2Model: Generating Deployable Models from Natural Language Instructions,Vijay Viswanathan,http://arxiv.org/pdf/2308.12261v1.pdf,2023-08-23,['cs.cl'],2308.12261v1.pdf," Large language models (LLMs) enable system builders today to create competent +NLP systems through prompting, where they only need to describe the task in +natural language and provide a few examples. However, in other ways, LLMs are a +step backward from traditional special-purpose NLP models; they require +extensive computational resources for deployment and can be gated behind APIs. +In this paper, we propose Prompt2Model, a general-purpose method that takes a +natural language task description like the prompts provided to LLMs, and uses +it to train a special-purpose model that is conducive to deployment. This is +done through a multi-step process of retrieval of existing datasets and +pretrained models, dataset generation using LLMs, and supervised fine-tuning on +these retrieved and generated datasets. Over three tasks, we demonstrate that +given the same few-shot prompt as input, Prompt2Model trains models that +outperform the results of a strong LLM, gpt-3.5-turbo, by an average of 20% +while being up to 700 times smaller. We also show that this data can be used to +obtain reliable performance estimates of model performance, enabling model +developers to assess model reliability before deployment. Prompt2Model is +available open-source at https://github.com/neulab/prompt2model. +" +Prompt a Robot to Walk with Large Language Models,Yen-Jen Wang,http://arxiv.org/pdf/2309.09969v1.pdf,2023-09-18,"['cs.ro', 'cs.lg', 'cs.sy', 'eess.sy']",2309.09969v1.pdf," Large language models (LLMs) pre-trained on vast internet-scale data have +showcased remarkable capabilities across diverse domains. Recently, there has +been escalating interest in deploying LLMs for robotics, aiming to harness the +power of foundation models in real-world settings. However, this approach faces +significant challenges, particularly in grounding these models in the physical +world and in generating dynamic robot motions. To address these issues, we +introduce a novel paradigm in which we use few-shot prompts collected from the +physical environment, enabling the LLM to autoregressively generate low-level +control commands for robots without task-specific fine-tuning. Experiments +across various robots and environments validate that our method can effectively +prompt a robot to walk. We thus illustrate how LLMs can proficiently function +as low-level feedback controllers for dynamic motion control even in +high-dimensional robotic systems. The project website and source code can be +found at: https://prompt2walk.github.io/ . +" +SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models,Shyam Sundar Kannan,http://arxiv.org/pdf/2309.10062v1.pdf,2023-09-18,['cs.ro'],2309.10062v1.pdf," In this work, we introduce SMART-LLM, an innovative framework designed for +embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task +Planning using Large Language Models (LLMs), harnesses the power of LLMs to +convert high-level task instructions provided as input into a multi-robot task +plan. It accomplishes this by executing a series of stages, including task +decomposition, coalition formation, and task allocation, all guided by +programmatic LLM prompts within the few-shot prompting paradigm. We create a +benchmark dataset designed for validating the multi-robot task planning +problem, encompassing four distinct categories of high-level instructions that +vary in task complexity. Our evaluation experiments span both simulation and +real-world scenarios, demonstrating that the proposed model can achieve +promising results for generating multi-robot task plans. The experimental +videos, code, and datasets from the work can be found at +https://sites.google.com/view/smart-llm/. +" +EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning,Rajasekhar Reddy Mekala,http://arxiv.org/pdf/2309.10687v2.pdf,2023-09-16,['cs.cl'],2309.10687v2.pdf," Language models are achieving impressive performance on various tasks by +aggressively adopting inference-time prompting techniques, such as zero-shot +and few-shot prompting. In this work, we introduce EchoPrompt, a simple yet +effective approach that prompts the model to rephrase its queries before +answering them. EchoPrompt is adapted for both zero-shot and few-shot +in-context learning with standard and chain-of-thought prompting. Experimental +results show that EchoPrompt yields substantial improvements across all these +settings for four families of causal language models. These improvements are +observed across various numerical reasoning (e.g. GSM8K, SVAMP), reading +comprehension (e.g. DROP), and logical reasoning (e.g. Coin Flipping) tasks. On +average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002 +by 5% in numerical tasks and 13% in reading comprehension tasks. We investigate +the factors contributing to EchoPrompt's effectiveness through ablation +studies, which reveal that both the original query and the model-generated +rephrased version are instrumental in its performance gains. Our empirical +results indicate that EchoPrompt is an effective technique that enhances +in-context learning performance. We recommend incorporating EchoPrompt into +various baseline prompting strategies to achieve performance boosts. +" +Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models,Haoyu Gao,http://arxiv.org/pdf/2309.12940v1.pdf,2023-09-22,"['cs.cl', 'cs.ai']",2309.12940v1.pdf," Task-oriented dialogue (TOD) systems facilitate users in executing various +activities via multi-turn dialogues, but Large Language Models (LLMs) often +struggle to comprehend these intricate contexts. In this study, we propose a +novel ""Self-Explanation"" prompting strategy to enhance the comprehension +abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires +the model to analyze each dialogue utterance before task execution, thereby +improving performance across various dialogue-centric tasks. Experimental +results from six benchmark datasets confirm that our method consistently +outperforms other zero-shot prompts and matches or exceeds the efficacy of +few-shot prompts, demonstrating its potential as a powerful tool in enhancing +LLMs' comprehension in complex dialogue tasks. +" +Language Models as Knowledge Bases for Visual Word Sense Disambiguation,Anastasia Kritharoula,http://arxiv.org/pdf/2310.01960v1.pdf,2023-10-03,"['cs.cl', 'cs.ai']",2310.01960v1.pdf," Visual Word Sense Disambiguation (VWSD) is a novel challenging task that lies +between linguistic sense disambiguation and fine-grained multimodal retrieval. +The recent advancements in the development of visiolinguistic (VL) transformers +suggest some off-the-self implementations with encouraging results, which +however we argue that can be further improved. To this end, we propose some +knowledge-enhancement techniques towards improving the retrieval performance of +VL transformers via the usage of Large Language Models (LLMs) as Knowledge +Bases. More specifically, knowledge stored in LLMs is retrieved with the help +of appropriate prompts in a zero-shot manner, achieving performance +advancements. Moreover, we convert VWSD to a purely textual question-answering +(QA) problem by considering generated image captions as multiple-choice +candidate answers. Zero-shot and few-shot prompting strategies are leveraged to +explore the potential of such a transformation, while Chain-of-Thought (CoT) +prompting in the zero-shot setting is able to reveal the internal reasoning +steps an LLM follows to select the appropriate candidate. In total, our +presented approach is the first one to analyze the merits of exploiting +knowledge stored in LLMs in different ways to solve WVSD. +" +Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning,Mohamed Aghzal,http://arxiv.org/pdf/2310.03249v1.pdf,2023-10-05,['cs.cl'],2310.03249v1.pdf," Large language models (LLMs) have achieved remarkable success across a wide +spectrum of tasks; however, they still face limitations in scenarios that +demand long-term planning and spatial reasoning. To facilitate this line of +research, in this work, we propose a new benchmark, termed $\textbf{P}$ath +$\textbf{P}$lanning from $\textbf{N}$atural $\textbf{L}$anguage +($\textbf{PPNL}$). Our benchmark evaluates LLMs' spatial-temporal reasoning by +formulating ''path planning'' tasks that require an LLM to navigate to target +locations while avoiding obstacles and adhering to constraints. Leveraging this +benchmark, we systematically investigate LLMs including GPT-4 via different +few-shot prompting methodologies and BART and T5 of various sizes via +fine-tuning. Our experimental results show the promise of few-shot GPT-4 in +spatial reasoning, when it is prompted to reason and act interleavedly, +although it still fails to make long-term temporal reasoning. In contrast, +while fine-tuned LLMs achieved impressive results on in-distribution reasoning +tasks, they struggled to generalize to larger environments or environments with +more obstacles. +" +Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning,Hongfu Liu,http://arxiv.org/pdf/2310.08923v1.pdf,2023-10-13,['cs.cl'],2310.08923v1.pdf," Large Language models (LLMs) possess the capability to engage In-context +Learning (ICL) by leveraging a few demonstrations pertaining to a new +downstream task as conditions. However, this particular learning paradigm +suffers from high instability stemming from substantial variances induced by +factors such as the input distribution of selected examples, their ordering, +and prompt formats. In this work, we demonstrate that even when all these +factors are held constant, the random selection of examples still results in +high variance. Consequently, we aim to explore the informative ability of data +examples by quantifying the Information Gain (IG) obtained in prediction after +observing a given example candidate. Then we propose to sample those with +maximum IG. Additionally, we identify the presence of template bias, which can +lead to unfair evaluations of IG during the sampling process. To mitigate this +bias, we introduce Calibration Before Sampling strategy. The experimental +results illustrate that our proposed method can yield an average relative +improvement of 14.3% across six classification tasks using three LLMs. +" +Ecologically Valid Explanations for Label Variation in NLI,Nan-Jiang Jiang,http://arxiv.org/pdf/2310.13850v1.pdf,2023-10-20,['cs.cl'],2310.13850v1.pdf," Human label variation, or annotation disagreement, exists in many natural +language processing (NLP) tasks, including natural language inference (NLI). To +gain direct evidence of how NLI label variation arises, we build LiveNLI, an +English dataset of 1,415 ecologically valid explanations (annotators explain +the NLI labels they chose) for 122 MNLI items (at least 10 explanations per +item). The LiveNLI explanations confirm that people can systematically vary on +their interpretation and highlight within-label variation: annotators sometimes +choose the same label for different reasons. This suggests that explanations +are crucial for navigating label interpretations in general. We few-shot prompt +large language models to generate explanations but the results are +inconsistent: they sometimes produces valid and informative explanations, but +it also generates implausible ones that do not support the label, highlighting +directions for improvement. +" +API-Assisted Code Generation for Question Answering on Varied Table Structures,Yihan Cao,http://arxiv.org/pdf/2310.14687v1.pdf,2023-10-23,"['cs.cl', 'cs.ai']",2310.14687v1.pdf," A persistent challenge to table question answering (TableQA) by generating +executable programs has been adapting to varied table structures, typically +requiring domain-specific logical forms. In response, this paper introduces a +unified TableQA framework that: (1) provides a unified representation for +structured tables as multi-index Pandas data frames, (2) uses Python as a +powerful querying language, and (3) uses few-shot prompting to translate NL +questions into Python programs, which are executable on Pandas data frames. +Furthermore, to answer complex relational questions with extended program +functionality and external knowledge, our framework allows customized APIs that +Python programs can call. We experiment with four TableQA datasets that involve +tables of different structures -- relational, multi-table, and hierarchical +matrix shapes -- and achieve prominent improvements over past state-of-the-art +systems. In ablation studies, we (1) show benefits from our multi-index +representation and APIs over baselines that use only an LLM, and (2) +demonstrate that our approach is modular and can incorporate additional APIs. +" +Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models,Gangwoo Kim,http://arxiv.org/pdf/2310.14696v1.pdf,2023-10-23,['cs.cl'],2310.14696v1.pdf," Questions in open-domain question answering are often ambiguous, allowing +multiple interpretations. One approach to handling them is to identify all +possible interpretations of the ambiguous question (AQ) and to generate a +long-form answer addressing them all, as suggested by Stelmakh et al., (2022). +While it provides a comprehensive response without bothering the user for +clarification, considering multiple dimensions of ambiguity and gathering +corresponding knowledge remains a challenge. To cope with the challenge, we +propose a novel framework, Tree of Clarifications (ToC): It recursively +constructs a tree of disambiguations for the AQ -- via few-shot prompting +leveraging external knowledge -- and uses it to generate a long-form answer. +ToC outperforms existing baselines on ASQA in a few-shot setup across the +metrics, while surpassing fully-supervised baselines trained on the whole +training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at +https://github.com/gankim/tree-of-clarifications. +" +Dissecting In-Context Learning of Translations in GPTs,Vikas Raunak,http://arxiv.org/pdf/2310.15987v1.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.15987v1.pdf," Most of the recent work in leveraging Large Language Models (LLMs) such as +GPT-3 for Machine Translation (MT) has focused on selecting the few-shot +samples for prompting. In this work, we try to better understand the role of +demonstration attributes for the in-context learning of translations through +perturbations of high-quality, in-domain demonstrations. We find that +asymmetric perturbation of the source-target mappings yield vastly different +results. We show that the perturbation of the source side has surprisingly +little impact, while target perturbation can drastically reduce translation +quality, suggesting that it is the output text distribution that provides the +most important learning signal during in-context learning of translations. We +propose a method named Zero-Shot-Context to add this signal automatically in +Zero-Shot prompting. We demonstrate that it improves upon the zero-shot +translation performance of GPT-3, even making it competitive with few-shot +prompted translations. +" +Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models,Smita Nannaware,http://arxiv.org/pdf/2311.02702v1.pdf,2023-11-05,"['cs.cl', 'cs.ai']",2311.02702v1.pdf," A restaurant dinner may become a memorable experience due to an unexpected +aspect enjoyed by the customer, such as an origami-making station in the +waiting area. If aspects that are atypical for a restaurant experience were +known in advance, they could be leveraged to make recommendations that have the +potential to engender serendipitous experiences, further increasing user +satisfaction. Although relatively rare, whenever encountered, atypical aspects +often end up being mentioned in reviews due to their memorable quality. +Correspondingly, in this paper we introduce the task of detecting atypical +aspects in customer reviews. To facilitate the development of extraction +models, we manually annotate benchmark datasets of reviews in three domains - +restaurants, hotels, and hair salons, which we use to evaluate a number of +language models, ranging from fine-tuning the instruction-based text-to-text +transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5. +" +SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data,Ruoxi Sun,http://arxiv.org/pdf/2311.02883v1.pdf,2023-11-06,['cs.cl'],2311.02883v1.pdf," Text-to-SQL aims to automate the process of generating SQL queries on a +database from natural language text. In this work, we propose ""SQLPrompt"", +tailored to improve the few-shot prompting capabilities of Text-to-SQL for +Large Language Models (LLMs). Our methods include innovative prompt design, +execution-based consistency decoding strategy which selects the SQL with the +most consistent execution outcome among other SQL proposals, and a method that +aims to improve performance by diversifying the SQL proposals during +consistency selection with different prompt designs (""MixPrompt"") and +foundation models (""MixLLMs""). We show that \emph{SQLPrompt} outperforms +previous approaches for in-context learning with few labeled data by a large +margin, closing the gap with finetuning state-of-the-art with thousands of +labeled data. +" +OLaLa: Ontology Matching with Large Language Models,Sven Hertling,http://arxiv.org/pdf/2311.03837v1.pdf,2023-11-07,"['cs.ir', 'cs.cl']",2311.03837v1.pdf," Ontology (and more generally: Knowledge Graph) Matching is a challenging task +where information in natural language is one of the most important signals to +process. With the rise of Large Language Models, it is possible to incorporate +this knowledge in a better way into the matching pipeline. A number of +decisions still need to be taken, e.g., how to generate a prompt that is useful +to the model, how information in the KG can be formulated in prompts, which +Large Language Model to choose, how to provide existing correspondences to the +model, how to generate candidates, etc. In this paper, we present a prototype +that explores these questions by applying zero-shot and few-shot prompting with +multiple open Large Language Models to different tasks of the Ontology +Alignment Evaluation Initiative (OAEI). We show that with only a handful of +examples and a well-designed prompt, it is possible to achieve results that are +en par with supervised matching systems which use a much larger portion of the +ground truth. +" +Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue,Lena Reed,http://arxiv.org/pdf/2110.08094v2.pdf,2021-10-15,['cs.cl'],2110.08094v2.pdf," One challenge with open-domain dialogue systems is the need to produce +truthful, high-quality responses on any topic. We aim to improve the quality +and coverage of Athena, an Alexa Prize dialogue system. We experiment with +few-shot prompt-based learning, comparing GPT-Neo to Jurassic-1, for the +movies, music, TV, sports, and video game domains, both within and +cross-domain, with different prompt set sizes (2, 3, 10), formats, and meaning +representations consisting of either sets of WikiData KG triples, or dialogue +acts. Our evaluation uses BLEURT and human metrics, and shows that with 10-shot +prompting, Athena-Jurassic's performance is significantly better for coherence +and semantic accuracy. Experiments with 2-shot cross-domain prompts results in +a huge performance drop for Athena-GPT-Neo, whose semantic accuracy falls to +0.41, and whose untrue hallucination rate increases to 12%. Experiments with +dialogue acts for video games show that with 10-shot prompting, both models +learn to control dialogue acts, but Athena-Jurassic has significantly higher +coherence, and only 4% untrue hallucinations. Our results suggest that +Athena-Jurassic produces high enough quality outputs to be useful in live +systems with real users. To our knowledge, these are the first results +demonstrating that few-shot semantic prompt-based learning can create NLGs that +generalize to new domains, and produce high-quality, semantically-controlled, +conversational responses directly from meaning representations. +" +Code as Policies: Language Model Programs for Embodied Control,Jacky Liang,http://arxiv.org/pdf/2209.07753v4.pdf,2022-09-16,['cs.ro'],2209.07753v4.pdf," Large language models (LLMs) trained on code completion have been shown to be +capable of synthesizing simple Python programs from docstrings [1]. We find +that these code-writing LLMs can be re-purposed to write robot policy code, +given natural language commands. Specifically, policy code can express +functions or feedback loops that process perception outputs (e.g.,from object +detectors [2], [3]) and parameterize control primitive APIs. When provided as +input several example language commands (formatted as comments) followed by +corresponding policy code (via few-shot prompting), LLMs can take in new +commands and autonomously re-compose API calls to generate new policy code +respectively. By chaining classic logic structures and referencing third-party +libraries (e.g., NumPy, Shapely) to perform arithmetic, LLMs used in this way +can write robot policies that (i) exhibit spatial-geometric reasoning, (ii) +generalize to new instructions, and (iii) prescribe precise values (e.g., +velocities) to ambiguous descriptions (""faster"") depending on context (i.e., +behavioral commonsense). This paper presents code as policies: a robot-centric +formulation of language model generated programs (LMPs) that can represent +reactive policies (e.g., impedance controllers), as well as waypoint-based +policies (vision-based pick and place, trajectory-based control), demonstrated +across multiple real robot platforms. Central to our approach is prompting +hierarchical code-gen (recursively defining undefined functions), which can +write more complex code and also improves state-of-the-art to solve 39.8% of +problems on the HumanEval [1] benchmark. Code and videos are available at +https://code-as-policies.github.io +" +Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus,Gang Li,http://arxiv.org/pdf/2209.14927v4.pdf,2022-09-29,"['cs.cv', 'cs.hc', 'cs.lg']",2209.14927v4.pdf," Mobile UI understanding is important for enabling various interaction tasks +such as UI automation and accessibility. Previous mobile UI modeling often +depends on the view hierarchy information of a screen, which directly provides +the structural data of the UI, with the hope to bypass challenging tasks of +visual modeling from screen pixels. However, view hierarchies are not always +available, and are often corrupted with missing object descriptions or +misaligned structure information. As a result, despite the use of view +hierarchies could offer short-term gains, it may ultimately hinder the +applicability and performance of the model. In this paper, we propose +Spotlight, a vision-only approach for mobile UI understanding. Specifically, we +enhance a vision-language model that only takes the screenshot of the UI and a +region of interest on the screen -- the focus -- as the input. This general +architecture of Spotlight is easily scalable and capable of performing a range +of UI modeling tasks. Our experiments show that our model establishes SoTA +results on several representative UI tasks and outperforms previous methods +that use both screenshots and view hierarchies as inputs. Furthermore, we +explore multi-task learning and few-shot prompting capacities of the proposed +models, demonstrating promising results in the multi-task learning direction. +" +Grounding Language with Visual Affordances over Unstructured Data,Oier Mees,http://arxiv.org/pdf/2210.01911v3.pdf,2022-10-04,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.lg']",2210.01911v3.pdf," Recent works have shown that Large Language Models (LLMs) can be applied to +ground natural language to a wide variety of robot skills. However, in +practice, learning multi-task, language-conditioned robotic skills typically +requires large-scale data collection and frequent human intervention to reset +the environment or help correcting the current policies. In this work, we +propose a novel approach to efficiently learn general-purpose +language-conditioned robot skills from unstructured, offline and reset-free +data in the real world by exploiting a self-supervised visuo-lingual affordance +model, which requires annotating as little as 1% of the total data with +language. We evaluate our method in extensive experiments both in simulated and +real-world robotic tasks, achieving state-of-the-art performance on the +challenging CALVIN benchmark and learning over 25 distinct visuomotor +manipulation tasks with a single policy in the real world. We find that when +paired with LLMs to break down abstract natural language instructions into +subgoals via few-shot prompting, our method is capable of completing +long-horizon, multi-tier tasks in the real world, while requiring an order of +magnitude less data than previous approaches. Code and videos are available at +http://hulc2.cs.uni-freiburg.de +" +MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting,Oscar Mañas,http://arxiv.org/pdf/2210.07179v2.pdf,2022-10-13,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2210.07179v2.pdf," Large pre-trained models have proved to be remarkable zero- and +(prompt-based) few-shot learners in unimodal vision and language tasks. We +propose MAPL, a simple and parameter-efficient method that reuses frozen +pre-trained unimodal models and leverages their strong generalization +capabilities in multimodal vision-language (VL) settings. MAPL learns a +lightweight mapping between the representation spaces of unimodal models using +aligned image-text data, and can generalize to unseen VL tasks from just a few +in-context examples. The small number of trainable parameters makes MAPL +effective at low-data and in-domain learning. Moreover, MAPL's modularity +enables easy extension to other pre-trained models. Extensive experiments on +several visual question answering and image captioning benchmarks show that +MAPL achieves superior or competitive performance compared to similar methods +while training orders of magnitude fewer parameters. MAPL can be trained in +just a few hours using modest computational resources and public datasets. We +release our code and pre-trained model weights at +https://github.com/mair-lab/mapl. +" +Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning,Xiangyu Peng,http://arxiv.org/pdf/2210.12587v3.pdf,2022-10-23,['cs.cl'],2210.12587v3.pdf," Prompt tuning approaches, which learn task-specific soft prompts for a +downstream task conditioning on frozen pre-trained models, have attracted +growing interest due to its parameter efficiency. With large language models +and sufficient training data, prompt tuning performs comparably to full-model +tuning. However, with limited training samples in few-shot settings, prompt +tuning fails to match the performance of full-model fine-tuning. In this work, +we focus on improving the few-shot performance of prompt tuning by transferring +knowledge from soft prompts of source tasks. Recognizing the good +generalization capabilities of ensemble methods in low-data regime, we first +experiment and show that a simple ensemble of model predictions based on +different source prompts, outperforms existing multi-prompt knowledge transfer +approaches such as source prompt fusion in the few-shot setting. Motivated by +this observation, we further investigate model ensembles and propose +Sample-specific Ensemble of Source Models (SESoM). SESoM learns to adjust the +contribution of each source model for each target sample separately when +ensembling source model outputs. Through this way, SESoM inherits the superior +generalization of model ensemble approaches and simultaneously captures the +sample-specific competence of each source prompt. We conduct experiments across +a diverse set of eight NLP tasks using models of different scales (T5-{base, +large, XL}) and find that SESoM consistently outperforms the existing models of +the same as well as larger parametric scale by a large margin. +" +Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations,Swarnadeep Saha,http://arxiv.org/pdf/2211.07517v1.pdf,2022-11-14,"['cs.cl', 'cs.ai']",2211.07517v1.pdf," Recent work on explainable NLP has shown that few-shot prompting can enable +large pretrained language models (LLMs) to generate grammatical and factual +natural language explanations for data labels. In this work, we study the +connection between explainability and sample hardness by investigating the +following research question - ""Are LLMs and humans equally good at explaining +data labels for both easy and hard samples?"" We answer this question by first +collecting human-written explanations in the form of generalizable commonsense +rules on the task of Winograd Schema Challenge (Winogrande dataset). We compare +these explanations with those generated by GPT-3 while varying the hardness of +the test samples as well as the in-context samples. We observe that (1) GPT-3 +explanations are as grammatical as human explanations regardless of the +hardness of the test samples, (2) for easy examples, GPT-3 generates highly +supportive explanations but human explanations are more generalizable, and (3) +for hard examples, human explanations are significantly better than GPT-3 +explanations both in terms of label-supportiveness and generalizability +judgements. We also find that hardness of the in-context examples impacts the +quality of GPT-3 explanations. Finally, we show that the supportiveness and +generalizability aspects of human explanations are also impacted by sample +hardness, although by a much smaller margin than models. Supporting code and +data are available at https://github.com/swarnaHub/ExplanationHardness +" +Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges,Fabricio Goes,http://arxiv.org/pdf/2212.11214v1.pdf,2022-12-21,['cs.ai'],2212.11214v1.pdf," This paper presents the Crowd Score, a novel method to assess the funniness +of jokes using large language models (LLMs) as AI judges. Our method relies on +inducing different personalities into the LLM and aggregating the votes of the +AI judges into a single score to rate jokes. We validate the votes using an +auditing technique that checks if the explanation for a particular vote is +reasonable using the LLM. We tested our methodology on 52 jokes in a crowd of +four AI voters with different humour types: affiliative, self-enhancing, +aggressive and self-defeating. Our results show that few-shot prompting leads +to better results than zero-shot for the voting question. Personality induction +showed that aggressive and self-defeating voters are significantly more +inclined to find more jokes funny of a set of aggressive/self-defeating jokes +than the affiliative and self-enhancing voters. The Crowd Score follows the +same trend as human judges by assigning higher scores to jokes that are also +considered funnier by human judges. We believe that our methodology could be +applied to other creative domains such as story, poetry, slogans, etc. It could +both help the adoption of a flexible and accurate standard approach to compare +different work in the CC community under a common metric and by minimizing +human participation in assessing creative artefacts, it could accelerate the +prototyping of creative artefacts and reduce the cost of hiring human +participants to rate creative artefacts. +" +CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models,Hossein Hajipour,http://arxiv.org/pdf/2302.04012v2.pdf,2023-02-08,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.se']",2302.04012v2.pdf," Large language models (LLMs) for automatic code generation have achieved +breakthroughs in several programming tasks. Their advances in competition-level +programming problems have made them an essential pillar of AI-assisted pair +programming, and tools such as GitHub Copilot have emerged as part of the daily +programming workflow used by millions of developers. The training data for +these models is usually collected from the Internet (e.g., from open-source +repositories) and is likely to contain faults and security vulnerabilities. +This unsanitized training data can cause the language models to learn these +vulnerabilities and propagate them during the code generation procedure. While +these models have been extensively assessed for their ability to produce +functionally correct programs, there remains a lack of comprehensive +investigations and benchmarks addressing the security aspects of these models. + In this work, we propose a method to systematically study the security issues +of code language models to assess their susceptibility to generating vulnerable +code. To this end, we introduce the first approach to automatically find +generated code that contains vulnerabilities in black-box code generation +models. To achieve this, we present an approach to approximate inversion of the +black-box code generation models based on few-shot prompting. We evaluate the +effectiveness of our approach by examining code language models in generating +high-risk security weaknesses. Furthermore, we establish a collection of +diverse non-secure prompts for various vulnerability scenarios using our +method. This dataset forms a benchmark for evaluating and comparing the +security weaknesses in code language models. +" +ART: Automatic multi-step reasoning and tool-use for large language models,Bhargavi Paranjape,http://arxiv.org/pdf/2303.09014v1.pdf,2023-03-16,['cs.cl'],2303.09014v1.pdf," Large language models (LLMs) can perform complex reasoning in few- and +zero-shot settings by generating intermediate chain of thought (CoT) reasoning +steps. Further, each reasoning step can rely on external tools to support +computation beyond the core LLM capabilities (e.g. search/running code). Prior +work on CoT prompting and tool use typically requires hand-crafting +task-specific demonstrations and carefully scripted interleaving of model +generations with tool use. We introduce Automatic Reasoning and Tool-use (ART), +a framework that uses frozen LLMs to automatically generate intermediate +reasoning steps as a program. Given a new task to solve, ART selects +demonstrations of multi-step reasoning and tool use from a task library. At +test time, ART seamlessly pauses generation whenever external tools are called, +and integrates their output before resuming generation. ART achieves a +substantial improvement over few-shot prompting and automatic CoT on unseen +tasks in the BigBench and MMLU benchmarks, and matches performance of +hand-crafted CoT prompts on a majority of these tasks. ART is also extensible, +and makes it easy for humans to improve performance by correcting errors in +task-specific programs or incorporating new tools, which we demonstrate by +drastically improving performance on select tasks with minimal human +intervention. +" +Fairness-guided Few-shot Prompting for Large Language Models,Huan Ma,http://arxiv.org/pdf/2303.13217v3.pdf,2023-03-23,"['cs.cl', 'cs.ai']",2303.13217v3.pdf," Large language models have demonstrated surprising ability to perform +in-context learning, i.e., these models can be directly applied to solve +numerous downstream tasks by conditioning on a prompt constructed by a few +input-output examples. However, prior research has shown that in-context +learning can suffer from high instability due to variations in training +examples, example order, and prompt formats. Therefore, the construction of an +appropriate prompt is essential for improving the performance of in-context +learning. In this paper, we revisit this problem from the view of predictive +bias. Specifically, we introduce a metric to evaluate the predictive bias of a +fixed prompt against labels or a given attributes. Then we empirically show +that prompts with higher bias always lead to unsatisfactory predictive quality. +Based on this observation, we propose a novel search strategy based on the +greedy search to identify the near-optimal prompt for improving the performance +of in-context learning. We perform comprehensive experiments with +state-of-the-art mainstream models such as GPT-3 on various downstream tasks. +Our results indicate that our method can enhance the model's in-context +learning performance in an effective and interpretable manner. +" +Is ChatGPT a Good Recommender? A Preliminary Study,Junling Liu,http://arxiv.org/pdf/2304.10149v3.pdf,2023-04-20,['cs.ir'],2304.10149v3.pdf," Recommendation systems have witnessed significant advancements and have been +widely used over the past decades. However, most traditional recommendation +methods are task-specific and therefore lack efficient generalization ability. +Recently, the emergence of ChatGPT has significantly advanced NLP tasks by +enhancing the capabilities of conversational models. Nonetheless, the +application of ChatGPT in the recommendation domain has not been thoroughly +investigated. In this paper, we employ ChatGPT as a general-purpose +recommendation model to explore its potential for transferring extensive +linguistic and world knowledge acquired from large-scale corpora to +recommendation scenarios. Specifically, we design a set of prompts and evaluate +ChatGPT's performance on five recommendation scenarios. Unlike traditional +recommendation methods, we do not fine-tune ChatGPT during the entire +evaluation process, relying only on the prompts themselves to convert +recommendation tasks into natural language tasks. Further, we explore the use +of few-shot prompting to inject interaction information that contains user +potential interest to help ChatGPT better understand user needs and interests. +Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT +has achieved promising results in certain tasks and is capable of reaching the +baseline level in others. We conduct human evaluations on two +explainability-oriented tasks to more accurately evaluate the quality of +contents generated by different models. And the human evaluations show ChatGPT +can truly understand the provided information and generate clearer and more +reasonable results. We hope that our study can inspire researchers to further +explore the potential of language models like ChatGPT to improve recommendation +performance and contribute to the advancement of the recommendation systems +field. +" +Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting,Miles Turpin,http://arxiv.org/pdf/2305.04388v1.pdf,2023-05-07,"['cs.cl', 'cs.ai']",2305.04388v1.pdf," Large Language Models (LLMs) can achieve strong performance on many tasks by +producing step-by-step reasoning before giving a final output, often referred +to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT +explanations as the LLM's process for solving a task. However, we find that CoT +explanations can systematically misrepresent the true reason for a model's +prediction. We demonstrate that CoT explanations can be heavily influenced by +adding biasing features to model inputs -- e.g., by reordering the +multiple-choice options in a few-shot prompt to make the answer always ""(A)"" -- +which models systematically fail to mention in their explanations. When we bias +models toward incorrect answers, they frequently generate CoT explanations +supporting those answers. This causes accuracy to drop by as much as 36% on a +suite of 13 tasks from BIG-Bench Hard, when testing with GPT-3.5 from OpenAI +and Claude 1.0 from Anthropic. On a social-bias task, model explanations +justify giving answers in line with stereotypes without mentioning the +influence of these social biases. Our findings indicate that CoT explanations +can be plausible yet misleading, which risks increasing our trust in LLMs +without guaranteeing their safety. CoT is promising for explainability, but our +results highlight the need for targeted efforts to evaluate and improve +explanation faithfulness. +" +Skill-Based Few-Shot Selection for In-Context Learning,Shengnan An,http://arxiv.org/pdf/2305.14210v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14210v2.pdf," In-context learning is the paradigm that adapts large language models to +downstream tasks by providing a few examples. Few-shot selection -- selecting +appropriate examples for each test instance separately -- is important for +in-context learning. In this paper, we propose Skill-KNN, a skill-based +few-shot selection method for in-context learning. The key advantages of +Skill-KNN include: (1) it addresses the problem that existing methods based on +pre-trained embeddings can be easily biased by surface natural language +features that are not important for the target task; (2) it does not require +training or fine-tuning of any models, making it suitable for frequently +expanding or changing example banks. The key insight is to optimize the inputs +fed into the embedding model, rather than tuning the model itself. Technically, +Skill-KNN generates the skill-based descriptions for each test case and +candidate example by utilizing a pre-processing few-shot prompting, thus +eliminating unimportant surface features. Experimental results across five +cross-domain semantic parsing datasets and six backbone models show that +Skill-KNN significantly outperforms existing methods. +" +USB: A Unified Summarization Benchmark Across Tasks and Domains,Kundan Krishna,http://arxiv.org/pdf/2305.14296v1.pdf,2023-05-23,"['cs.cl', 'cs.lg']",2305.14296v1.pdf," An abundance of datasets exist for training and evaluating models on the task +of summary generation.However, these datasets are often derived heuristically, +and lack sufficient annotations to support research into all aspects of +summarization, such as evidence extraction and controllable summarization. We +introduce a benchmark comprising 8 tasks that require multi-dimensional +understanding of summarization, e.g., surfacing evidence for a summary, +assessing its correctness, and gauging its relevance to different topics. We +compare various methods on this benchmark and discover that on multiple tasks, +moderately-sized fine-tuned models consistently outperform much larger few-shot +prompted language models. For factuality related tasks, we also evaluate +existing heuristics to create training data and find that training on them +performs worse than training on $20\times$ less human-labeled data. Our +benchmark consists of data from 6 different domains, allowing us to study +cross-domain performance of trained models. We find that for some tasks, the +amount of training data matters more than the domain where it comes from, while +for other tasks training specifically on data from the target domain, even if +limited, is more beneficial. Our work fulfills the need for a well-annotated +summarization benchmark with diverse tasks, and provides useful insights about +the impact of the quality, size and domain of training data. +" +Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement,Zhiheng Xi,http://arxiv.org/pdf/2305.14497v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14497v1.pdf," Prompting methods such as Chain-of-Thought (CoT) have shed new light on +enhancing the reasoning capabilities of large language models, and researchers +have extensively explored the generation process of rationales and answers. +However, they have overlooked the potential challenges posed by the poor +quality of reasoning problems, which may influence the reasoning performance +significantly. In this work, we propose Self-Polish (SP), a novel method that +facilitates the model's problem-solving process by prompting them to +progressively refine the given problems to be more comprehensible and solvable. +Specifically, the method teaches models to eliminate irrelevant information, +rearrange the logic structure and organize local conditions into new ones +parallelly. SP is orthogonal to all other prompting methods, making it +convenient to integrate with state-of-the-art techniques for further +improvement. We conduct thorough experiments on five benchmarks to illustrate +the effectiveness of the proposed method. For example, with Text-davinci-003, +our method boosts the performance of standard few-shot prompting by $8.0\%$ on +GSM8K and $17.8\%$ on MultiArith; it also improves the performance of CoT by +$6.0\%$ on GSM8K and $6.0\%$ on MathQA, respectively. Furthermore, our method +also showcases impressive performance on robustness evaluation. +" +SciFix: Outperforming GPT3 on Scientific Factual Error Correction,Dhananjay Ashok,http://arxiv.org/pdf/2305.14707v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14707v2.pdf," Due to the prohibitively high cost of creating error correction datasets, +most Factual Claim Correction methods rely on a powerful verification model to +guide the correction process. This leads to a significant drop in performance +in domains like scientific claims, where good verification models do not always +exist. In this work, we introduce SciFix, a scientific claim correction system +that does not require a verifier but can outperform existing methods by a +considerable margin -- achieving correction accuracy of 84% on the SciFact +dataset, 77% on SciFact-Open and 72% on the CovidFact dataset, compared to next +best accuracies of 7%, 5%, and 15% on the same datasets respectively. Our +method leverages the power of prompting with LLMs during training to create a +richly annotated dataset that can be used for fully supervised training and +regularization. We additionally use a claim-aware decoding procedure to improve +the quality of corrected claims. Our method outperforms the very LLM that was +used to generate the annotated dataset -- with Few-Shot Prompting on GPT3.5 +achieving 58%, 61%, and 64% on the respective datasets, a consistently lower +correction accuracy, despite using nearly 800 times as many parameters as our +model. +" +LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections,M. Jehanzeb Mirza,http://arxiv.org/pdf/2305.18287v2.pdf,2023-05-29,"['cs.cv', 'cs.cl']",2305.18287v2.pdf," Recently, large-scale pre-trained Vision and Language (VL) models have set a +new state-of-the-art (SOTA) in zero-shot visual classification enabling +open-vocabulary recognition of potentially unlimited set of categories defined +as simple language prompts. However, despite these great advances, the +performance of these zeroshot classifiers still falls short of the results of +dedicated (closed category set) classifiers trained with supervised fine +tuning. In this paper we show, for the first time, how to reduce this gap +without any labels and without any paired VL data, using an unlabeled image +collection and a set of texts auto-generated using a Large Language Model (LLM) +describing the categories of interest and effectively substituting labeled +visual instances of those categories. Using our label-free approach, we are +able to attain significant performance improvements over the zero-shot +performance of the base VL model and other contemporary methods and baselines +on a wide variety of datasets, demonstrating absolute improvement of up to +11.7% (3.8% on average) in the label-free setting. Moreover, despite our +approach being label-free, we observe 1.3% average gains over leading few-shot +prompting baselines that do use 5-shot supervision. +" +"Better patching using LLM prompting, via Self-Consistency",Toufique Ahmed,http://arxiv.org/pdf/2306.00108v2.pdf,2023-05-31,"['cs.se', 'cs.lg']",2306.00108v2.pdf," Large Language models (LLMs) can be induced to solve non-trivial problems +with ""few-shot"" prompts including illustrative problem-solution examples. Now +if the few-shots also include ""chain of thought"" (CoT) explanations, which are +of the form problem-explanation-solution, LLMs will generate a ""explained"" +solution, and perform even better. Recently an exciting, substantially better +technique, self-consistency [1] (S-C) has emerged, based on the intuition that +there are many plausible explanations for the right solution; when the LLM is +sampled repeatedly to generate a pool of explanation-solution pairs, for a +given problem, the most frequently occurring solutions in the pool (ignoring +the explanations) tend to be even more likely to be correct! Unfortunately, the +use of this highly-performant S-C (or even CoT) approach in software +engineering settings is hampered by the lack of explanations; most software +datasets lack explanations. In this paper, we describe an application of the +S-C approach to program repair, using the commit log on the fix as the +explanation, only in the illustrative few-shots. We achieve state-of-the art +results, beating previous approaches to prompting-based program repair, on the +MODIT dataset; we also find evidence suggesting that the correct commit +messages are helping the LLM learn to produce better patches. +" +Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence,John J. Nay,http://arxiv.org/pdf/2306.07075v1.pdf,2023-06-12,"['cs.cl', 'cs.ai', 'cs.cy']",2306.07075v1.pdf," Better understanding of Large Language Models' (LLMs) legal analysis +abilities can contribute to improving the efficiency of legal services, +governing artificial intelligence, and leveraging LLMs to identify +inconsistencies in law. This paper explores LLM capabilities in applying tax +law. We choose this area of law because it has a structure that allows us to +set up automated validation pipelines across thousands of examples, requires +logical reasoning and maths skills, and enables us to test LLM capabilities in +a manner relevant to real-world economic lives of citizens and companies. Our +experiments demonstrate emerging legal understanding capabilities, with +improved performance in each subsequent OpenAI model release. We experiment +with retrieving and utilising the relevant legal authority to assess the impact +of providing additional legal context to LLMs. Few-shot prompting, presenting +examples of question-answer pairs, is also found to significantly enhance the +performance of the most advanced model, GPT-4. The findings indicate that LLMs, +particularly when combined with prompting enhancements and the correct legal +texts, can perform at high levels of accuracy but not yet at expert tax lawyer +levels. As LLMs continue to advance, their ability to reason about law +autonomously could have significant implications for the legal profession and +AI governance. +" +DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks,Caixin Kang,http://arxiv.org/pdf/2306.09124v2.pdf,2023-06-15,"['cs.cv', 'cs.ai', 'cs.cr', 'cs.lg']",2306.09124v2.pdf," Adversarial attacks, particularly patch attacks, pose significant threats to +the robustness and reliability of deep learning models. Developing reliable +defenses against patch attacks is crucial for real-world applications, yet +current research in this area is not satisfactory. In this paper, we propose +DIFFender, a novel defense method that leverages a text-guided diffusion model +to defend against adversarial patches. DIFFender includes two main stages: +patch localization and patch restoration. In the localization stage, we find +and exploit an intriguing property of the diffusion model to effectively +identify the locations of adversarial patches. In the restoration stage, we +employ the diffusion model to reconstruct the adversarial regions in the images +while preserving the integrity of the visual content. Importantly, these two +stages are carefully guided by a unified diffusion model, thus we can utilize +the close interaction between them to improve the whole defense performance. +Moreover, we propose a few-shot prompt-tuning algorithm to fine-tune the +diffusion model, enabling the pre-trained diffusion model to easily adapt to +the defense task. We conduct extensive experiments on the image classification +and face recognition tasks, demonstrating that our proposed method exhibits +superior robustness under strong adaptive attacks and generalizes well across +various scenarios, diverse classifiers, and multiple patch attack methods. +" +Teaching Arithmetic to Small Transformers,Nayoung Lee,http://arxiv.org/pdf/2307.03381v1.pdf,2023-07-07,['cs.lg'],2307.03381v1.pdf," Large language models like GPT-4 exhibit emergent capabilities across +general-purpose tasks, such as basic arithmetic, when trained on extensive text +data, even though these tasks are not explicitly encoded by the unsupervised, +next-token prediction objective. This study investigates how small +transformers, trained from random initialization, can efficiently learn +arithmetic operations such as addition, multiplication, and elementary +functions like square root, using the next-token prediction objective. We first +demonstrate that conventional training data is not the most effective for +arithmetic learning, and simple formatting changes can significantly improve +accuracy. This leads to sharp phase transitions as a function of training data +scale, which, in some cases, can be explained through connections to low-rank +matrix completion. Building on prior work, we then train on chain-of-thought +style data that includes intermediate step results. Even in the complete +absence of pretraining, this approach significantly and simultaneously improves +accuracy, sample complexity, and convergence speed. We also study the interplay +between arithmetic and text data during training and examine the effects of +few-shot prompting, pretraining, and model scale. Additionally, we discuss +length generalization challenges. Our work highlights the importance of +high-quality, instructive data that considers the particular characteristics of +the next-word prediction objective for rapidly eliciting arithmetic +capabilities. +" +Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking,Angela Ramirez,http://arxiv.org/pdf/2307.14440v1.pdf,2023-07-26,['cs.cl'],2307.14440v1.pdf," Dialogue systems need to produce responses that realize multiple types of +dialogue acts (DAs) with high semantic fidelity. In the past, natural language +generators (NLGs) for dialogue were trained on large parallel corpora that map +from a domain-specific DA and its semantic attributes to an output utterance. +Recent work shows that pretrained language models (LLMs) offer new +possibilities for controllable NLG using prompt-based learning. Here we develop +a novel few-shot overgenerate-and-rank approach that achieves the controlled +generation of DAs. We compare eight few-shot prompt styles that include a novel +method of generating from textual pseudo-references using a textual style +transfer approach. We develop six automatic ranking functions that identify +outputs with both the correct DA and high semantic accuracy at generation time. +We test our approach on three domains and four LLMs. To our knowledge, this is +the first work on NLG for dialogue that automatically ranks outputs using both +DA and attribute accuracy. For completeness, we compare our results to +fine-tuned few-shot models trained with 5 to 100 instances per DA. Our results +show that several prompt settings achieve perfect DA accuracy, and near perfect +semantic accuracy (99.81%) and perform better than few-shot fine-tuning. +" +Contextual Biasing of Named-Entities with Large Language Models,Chuanneng Sun,http://arxiv.org/pdf/2309.00723v2.pdf,2023-09-01,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as', '68t10', 'i.2.7']",2309.00723v2.pdf," This paper studies contextual biasing with Large Language Models (LLMs), +where during second-pass rescoring additional contextual information is +provided to a LLM to boost Automatic Speech Recognition (ASR) performance. We +propose to leverage prompts for a LLM without fine tuning during rescoring +which incorporate a biasing list and few-shot examples to serve as additional +information when calculating the score for the hypothesis. In addition to +few-shot prompt learning, we propose multi-task training of the LLM to predict +both the entity class and the next token. To improve the efficiency for +contextual biasing and to avoid exceeding LLMs' maximum sequence lengths, we +propose dynamic prompting, where we select the most likely class using the +class tag prediction, and only use entities in this class as contexts for next +token prediction. Word Error Rate (WER) evaluation is performed on i) an +internal calling, messaging, and dictation dataset, and ii) the SLUE-Voxpopuli +dataset. Results indicate that biasing lists and few-shot examples can achieve +17.8% and 9.6% relative improvement compared to first pass ASR, and that +multi-task training and dynamic prompting can achieve 20.0% and 11.3% relative +WER improvement, respectively. +" +MindAgent: Emergent Gaming Interaction,Ran Gong,http://arxiv.org/pdf/2309.09971v2.pdf,2023-09-18,"['cs.ai', 'cs.hc', 'cs.ma']",2309.09971v2.pdf," Large Language Models (LLMs) have the capacity of performing complex +scheduling in a multi-agent system and can coordinate these agents into +completing sophisticated tasks that require extensive collaboration. However, +despite the introduction of numerous gaming frameworks, the community has +insufficient benchmarks towards building general multi-agents collaboration +infrastructure that encompass both LLM and human-NPCs collaborations. In this +work, we propose a novel infrastructure - MindAgent - to evaluate planning and +coordination emergent capabilities for gaming interaction. In particular, our +infrastructure leverages existing gaming framework, to i) require understanding +of the coordinator for a multi-agent system, ii) collaborate with human players +via un-finetuned proper instructions, and iii) establish an in-context learning +on few-shot prompt with feedback. Furthermore, we introduce CUISINEWORLD, a new +gaming scenario and related benchmark that dispatch a multi-agent collaboration +efficiency and supervise multiple agents playing the game simultaneously. We +conduct comprehensive evaluations with new auto-metric CoS for calculating the +collaboration efficiency. Finally, our infrastructure can be deployed into +real-world gaming scenarios in a customized VR version of CUISINEWORLD and +adapted in existing broader Minecraft gaming domain. We hope our findings on +LLMs and the new infrastructure for general-purpose scheduling and coordination +can help shed light on how such skills can be obtained by learning from large +language corpora. +" +DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines,Omar Khattab,http://arxiv.org/pdf/2310.03714v1.pdf,2023-10-05,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2310.03714v1.pdf," The ML community is rapidly exploring techniques for prompting language +models (LMs) and for stacking them into pipelines that solve complex tasks. +Unfortunately, existing LM pipelines are typically implemented using hard-coded +""prompt templates"", i.e. lengthy strings discovered via trial and error. Toward +a more systematic approach for developing and optimizing LM pipelines, we +introduce DSPy, a programming model that abstracts LM pipelines as text +transformation graphs, i.e. imperative computational graphs where LMs are +invoked through declarative modules. DSPy modules are parameterized, meaning +they can learn (by creating and collecting demonstrations) how to apply +compositions of prompting, finetuning, augmentation, and reasoning techniques. +We design a compiler that will optimize any DSPy pipeline to maximize a given +metric. We conduct two case studies, showing that succinct DSPy programs can +express and optimize sophisticated LM pipelines that reason about math word +problems, tackle multi-hop retrieval, answer complex questions, and control +agent loops. Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and +llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot +prompting (generally by over 25% and 65%, respectively) and pipelines with +expert-created demonstrations (by up to 5-46% and 16-40%, respectively). On top +of that, DSPy programs compiled to open and relatively small LMs like +770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely +on expert-written prompt chains for proprietary GPT-3.5. DSPy is available at +https://github.com/stanfordnlp/dspy +" +InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations,Nils Feldhus,http://arxiv.org/pdf/2310.05592v2.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.hc']",2310.05592v2.pdf," While recently developed NLP explainability methods let us open the black box +in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is +an interactive tool offering a conversational interface. Such a dialogue system +can help users explore datasets and models with explanations in a +contextualized manner, e.g. via clarification or follow-up questions, and +through a natural language interface. We adapt the conversational explanation +framework TalkToModel (Slack et al., 2022) to the NLP domain, add new +NLP-specific operations such as free-text rationalization, and illustrate its +generalizability on three NLP tasks (dialogue act classification, question +answering, hate speech detection). To recognize user queries for explanations, +we evaluate fine-tuned and few-shot prompting models and implement a novel +Adapter-based approach. We then conduct two user studies on (1) the perceived +correctness and helpfulness of the dialogues, and (2) the simulatability, i.e. +how objectively helpful dialogical explanations are for humans in figuring out +the model's predicted label when it's not shown. We found rationalization and +feature attribution were helpful in explaining the model behavior. Moreover, +users could more reliably predict the model outcome based on an explanation +dialogue rather than one-off explanations. +" +FireAct: Toward Language Agent Fine-tuning,Baian Chen,http://arxiv.org/pdf/2310.05915v1.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05915v1.pdf," Recent efforts have augmented language models (LMs) with external tools or +environments, leading to the development of language agents that can reason and +act. However, most of these agents rely on few-shot prompting techniques with +off-the-shelf LMs. In this paper, we investigate and argue for the overlooked +direction of fine-tuning LMs to obtain language agents. Using a setup of +question answering (QA) with a Google search API, we explore a variety of base +LMs, prompting methods, fine-tuning data, and QA tasks, and find language +agents are consistently improved after fine-tuning their backbone LMs. For +example, fine-tuning Llama2-7B with 500 agent trajectories generated by GPT-4 +leads to a 77% HotpotQA performance increase. Furthermore, we propose FireAct, +a novel approach to fine-tuning LMs with trajectories from multiple tasks and +prompting methods, and show having more diverse fine-tuning data can further +improve agents. Along with other findings regarding scaling effects, +robustness, generalization, efficiency and cost, our work establishes +comprehensive benefits of fine-tuning LMs for agents, and provides an initial +set of experimental designs, insights, as well as open questions toward +language agent fine-tuning. +" +Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning,Duarte M. Alves,http://arxiv.org/pdf/2310.13448v1.pdf,2023-10-20,['cs.cl'],2310.13448v1.pdf," Large language models (LLMs) are a promising avenue for machine translation +(MT). However, current LLM-based MT systems are brittle: their effectiveness +highly depends on the choice of few-shot examples and they often require extra +post-processing due to overgeneration. Alternatives such as finetuning on +translation instructions are computationally expensive and may weaken +in-context learning capabilities, due to overspecialization. In this paper, we +provide a closer look at this problem. We start by showing that adapter-based +finetuning with LoRA matches the performance of traditional finetuning while +reducing the number of training parameters by a factor of 50. This method also +outperforms few-shot prompting and eliminates the need for post-processing or +in-context examples. However, we show that finetuning generally degrades +few-shot performance, hindering adaptation capabilities. Finally, to obtain the +best of both worlds, we propose a simple approach that incorporates few-shot +examples during finetuning. Experiments on 10 language pairs show that our +proposed approach recovers the original few-shot capabilities while keeping the +added benefits of finetuning. +" +On Bilingual Lexicon Induction with Large Language Models,Yaoyiran Li,http://arxiv.org/pdf/2310.13995v1.pdf,2023-10-21,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2310.13995v1.pdf," Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that +still, to a large extent, relies on calculating cross-lingual word +representations. Inspired by the global paradigm shift in NLP towards Large +Language Models (LLMs), we examine the potential of the latest generation of +LLMs for the development of bilingual lexicons. We ask the following research +question: Is it possible to prompt and fine-tune multilingual LLMs (mLLMs) for +BLI, and how does this approach compare against and complement current BLI +approaches? To this end, we systematically study 1) zero-shot prompting for +unsupervised BLI and 2) few-shot in-context prompting with a set of seed +translation pairs, both without any LLM fine-tuning, as well as 3) standard +BLI-oriented fine-tuning of smaller LLMs. We experiment with 18 open-source +text-to-text mLLMs of different sizes (from 0.3B to 13B parameters) on two +standard BLI benchmarks covering a range of typologically diverse languages. +Our work is the first to demonstrate strong BLI capabilities of text-to-text +mLLMs. The results reveal that few-shot prompting with in-context examples from +nearest neighbours achieves the best performance, establishing new +state-of-the-art BLI scores for many language pairs. We also conduct a series +of in-depth analyses and ablation studies, providing more insights on BLI with +(m)LLMs, also along with their limitations. +" +An Early Evaluation of GPT-4V(ision),Yang Wu,http://arxiv.org/pdf/2310.16534v1.pdf,2023-10-25,"['cs.cl', 'cs.cv']",2310.16534v1.pdf," In this paper, we evaluate different abilities of GPT-4V including visual +understanding, language understanding, visual puzzle solving, and understanding +of other modalities such as depth, thermal, video, and audio. To estimate +GPT-4V's performance, we manually construct 656 test instances and carefully +evaluate the results of GPT-4V. The highlights of our findings are as follows: +(1) GPT-4V exhibits impressive performance on English visual-centric benchmarks +but fails to recognize simple Chinese texts in the images; (2) GPT-4V shows +inconsistent refusal behavior when answering questions related to sensitive +traits such as gender, race, and age; (3) GPT-4V obtains worse results than +GPT-4 (API) on language understanding tasks including general language +understanding benchmarks and visual commonsense knowledge evaluation +benchmarks; (4) Few-shot prompting can improve GPT-4V's performance on both +visual understanding and language understanding; (5) GPT-4V struggles to find +the nuances between two similar images and solve the easy math picture puzzles; +(6) GPT-4V shows non-trivial performance on the tasks of similar modalities to +image, such as video and thermal. Our experimental results reveal the ability +and limitations of GPT-4V and we hope our paper can provide some insights into +the application and research of GPT-4V. +" +"""You Are An Expert Linguistic Annotator"": Limits of LLMs as Analyzers of Abstract Meaning Representation",Allyson Ettinger,http://arxiv.org/pdf/2310.17793v1.pdf,2023-10-26,"['cs.cl', 'cs.ai']",2310.17793v1.pdf," Large language models (LLMs) show amazing proficiency and fluency in the use +of language. Does this mean that they have also acquired insightful linguistic +knowledge about the language, to an extent that they can serve as an ""expert +linguistic annotator""? In this paper, we examine the successes and limitations +of the GPT-3, ChatGPT, and GPT-4 models in analysis of sentence meaning +structure, focusing on the Abstract Meaning Representation (AMR; Banarescu et +al. 2013) parsing formalism, which provides rich graphical representations of +sentence meaning structure while abstracting away from surface forms. We +compare models' analysis of this semantic structure across two settings: 1) +direct production of AMR parses based on zero- and few-shot prompts, and 2) +indirect partial reconstruction of AMR via metalinguistic natural language +queries (e.g., ""Identify the primary event of this sentence, and the predicate +corresponding to that event.""). Across these settings, we find that models can +reliably reproduce the basic format of AMR, and can often capture core event, +argument, and modifier structure -- however, model outputs are prone to +frequent and major errors, and holistic analysis of parse acceptability shows +that even with few-shot demonstrations, models have virtually 0% success in +producing fully accurate parses. Eliciting natural language responses produces +similar patterns of errors. Overall, our findings indicate that these models +out-of-the-box can capture aspects of semantic structure, but there remain key +limitations in their ability to support fully accurate semantic analyses or +parses. +" +Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting,Benjamin Yan,http://arxiv.org/pdf/2310.17811v2.pdf,2023-10-26,"['cs.ai', 'cs.cl']",2310.17811v2.pdf," Automatically generated reports from medical images promise to improve the +workflow of radiologists. Existing methods consider an image-to-report modeling +task by directly generating a fully-fledged report from an image. However, this +conflates the content of the report (e.g., findings and their attributes) with +its style (e.g., format and choice of words), which can lead to clinically +inaccurate reports. To address this, we propose a two-step approach for +radiology report generation. First, we extract the content from an image; then, +we verbalize the extracted content into a report that matches the style of a +specific radiologist. For this, we leverage RadGraph -- a graph representation +of reports -- together with large language models (LLMs). In our quantitative +evaluations, we find that our approach leads to beneficial performance. Our +human evaluation with clinical raters highlights that the AI-generated reports +are indistinguishably tailored to the style of individual radiologist despite +leveraging only a few examples as context. +" +Multi-lingual Evaluation of Code Generation Models,Ben Athiwaratkun,http://arxiv.org/pdf/2210.14868v3.pdf,2022-10-26,"['cs.lg', 'cs.cl']",2210.14868v3.pdf," We present new benchmarks on evaluation code generation models: MBXP and +Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming +languages and are generated using a scalable conversion framework that +transpiles prompts and test cases from the original Python datasets into the +corresponding data in the target language. Using these benchmarks, we are able +to assess the performance of code generation models in a multi-lingual fashion, +and discovered generalization ability of language models on out-of-domain +languages, advantages of multi-lingual models over mono-lingual, the ability of +few-shot prompting to teach the model new languages, and zero-shot translation +abilities even on mono-lingual settings. Furthermore, we use our code +generation model to perform large-scale bootstrapping to obtain synthetic +canonical solutions in several languages, which can be used for other +code-related evaluations such as code insertion, robustness, or summarization +tasks. Overall, our benchmarks represents a significant step towards a deeper +understanding of language models' code generation abilities. We publicly +release our code and datasets at https://github.com/amazon-research/mxeval. +" +PAL: Program-aided Language Models,Luyu Gao,http://arxiv.org/pdf/2211.10435v2.pdf,2022-11-18,"['cs.cl', 'cs.ai']",2211.10435v2.pdf," Large language models (LLMs) have recently demonstrated an impressive ability +to perform arithmetic and symbolic reasoning tasks, when provided with a few +examples at test time (""few-shot prompting""). Much of this success can be +attributed to prompting methods such as ""chain-of-thought'', which employ LLMs +for both understanding the problem description by decomposing it into steps, as +well as solving each step of the problem. While LLMs seem to be adept at this +sort of step-by-step decomposition, LLMs often make logical and arithmetic +mistakes in the solution part, even when the problem is decomposed correctly. +In this paper, we present Program-Aided Language models (PAL): a novel approach +that uses the LLM to read natural language problems and generate programs as +the intermediate reasoning steps, but offloads the solution step to a runtime +such as a Python interpreter. With PAL, decomposing the natural language +problem into runnable steps remains the only learning task for the LLM, while +solving is delegated to the interpreter. We demonstrate this synergy between a +neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and +algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all +these natural language reasoning tasks, generating code using an LLM and +reasoning using a Python interpreter leads to more accurate results than much +larger models. For example, PAL using Codex achieves state-of-the-art few-shot +accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B +which uses chain-of-thought by absolute 15% top-1. Our code and data are +publicly available at http://reasonwithpal.com/ . +" +Learning Performance-Improving Code Edits,Alexander Shypula,http://arxiv.org/pdf/2302.07867v4.pdf,2023-02-15,"['cs.se', 'cs.ai', 'cs.lg', 'cs.pf']",2302.07867v4.pdf," With the waning of Moore's law, optimizing program performance has become a +major focus of software research. However, high-level optimizations such as API +and algorithm changes remain elusive due to the difficulty of understanding the +semantics of code. Simultaneously, pretrained large language models (LLMs) have +demonstrated strong capabilities at solving a wide range of programming tasks. +To that end, we introduce a framework for adapting LLMs to high-level program +optimization. First, we curate a dataset of performance-improving edits made by +human programmers of over 77K competitive C++ programming submission pairs, +accompanied by extensive unit tests. A major challenge is the significant +variability of measuring performance on commodity hardware, which can lead to +spurious ""improvements"". To isolate and reliably evaluate the impact of program +optimizations, we design an environment based on the gem5 full system +simulator, the de facto simulator used in academia and industry. Next, we +propose a broad range of adaptation strategies for code optimization; for +prompting, these include retrieval-based few-shot prompting and +chain-of-thought, and for finetuning, these include performance-conditioned +generation and synthetic data augmentation based on self-play. A combination of +these techniques achieves an average speedup of 5.65X on CodeLlama-13B and +6.86X on GPT-3.5, surpassing the best human performance (4.06X). We find our +proposed performance-conditioned generation is particularly effective at +improving performance as well as increasing the fraction of optimized programs. +" +Large Language Models for User Interest Journeys,Konstantina Christakopoulou,http://arxiv.org/pdf/2305.15498v1.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.ir']",2305.15498v1.pdf," Large language models (LLMs) have shown impressive capabilities in natural +language understanding and generation. Their potential for deeper user +understanding and improved personalized user experience on recommendation +platforms is, however, largely untapped. This paper aims to address this gap. +Recommender systems today capture users' interests through encoding their +historical activities on the platforms. The generated user representations are +hard to examine or interpret. On the other hand, if we were to ask people about +interests they pursue in their life, they might talk about their hobbies, like +I just started learning the ukulele, or their relaxation routines, e.g., I like +to watch Saturday Night Live, or I want to plant a vertical garden. We argue, +and demonstrate through extensive experiments, that LLMs as foundation models +can reason through user activities, and describe their interests in nuanced and +interesting ways, similar to how a human would. + We define interest journeys as the persistent and overarching user interests, +in other words, the non-transient ones. These are the interests that we believe +will benefit most from the nuanced and personalized descriptions. We introduce +a framework in which we first perform personalized extraction of interest +journeys, and then summarize the extracted journeys via LLMs, using techniques +like few-shot prompting, prompt-tuning and fine-tuning. Together, our results +in prompting LLMs to name extracted user journeys in a large-scale industrial +platform demonstrate great potential of these models in providing deeper, more +interpretable, and controllable user understanding. We believe LLM powered user +understanding can be a stepping stone to entirely new user experiences on +recommendation platforms that are journey-aware, assistive, and enabling +frictionless conversation down the line. +" +Passive learning of active causal strategies in agents and language models,Andrew Kyle Lampinen,http://arxiv.org/pdf/2305.16183v2.pdf,2023-05-25,"['cs.lg', 'cs.ai', 'cs.cl']",2305.16183v2.pdf," What can be learned about causality and experimentation from passive data? +This question is salient given recent successes of passively-trained language +models in interactive domains such as tool use. Passive learning is inherently +limited. However, we show that purely passive learning can in fact allow an +agent to learn generalizable strategies for determining and using causal +structures, as long as the agent can intervene at test time. We formally +illustrate that learning a strategy of first experimenting, then seeking goals, +can allow generalization from passive learning in principle. We then show +empirically that agents trained via imitation on expert data can indeed +generalize at test time to infer and use causal links which are never present +in the training data; these agents can also generalize experimentation +strategies to novel variable sets never observed in training. We then show that +strategies for causal intervention and exploitation can be generalized from +passive data even in a more complex environment with high-dimensional +observations, with the support of natural language explanations. Explanations +can even allow passive learners to generalize out-of-distribution from +perfectly-confounded training data. Finally, we show that language models, +trained only on passive next-word prediction, can generalize causal +intervention strategies from a few-shot prompt containing examples of +experimentation, together with explanations and reasoning. These results +highlight the surprising power of passive learning of active causal strategies, +and may help to understand the behaviors and capabilities of language models. +" +Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models,Cheng-Yu Hsieh,http://arxiv.org/pdf/2308.00675v1.pdf,2023-08-01,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2308.00675v1.pdf," Today, large language models (LLMs) are taught to use new tools by providing +a few demonstrations of the tool's usage. Unfortunately, demonstrations are +hard to acquire, and can result in undesirable biased usage if the wrong +demonstration is chosen. Even in the rare scenario that demonstrations are +readily available, there is no principled selection protocol to determine how +many and which ones to provide. As tasks grow more complex, the selection +search grows combinatorially and invariably becomes intractable. Our work +provides an alternative to demonstrations: tool documentation. We advocate the +use of tool documentation, descriptions for the individual tool usage, over +demonstrations. We substantiate our claim through three main empirical findings +on 6 tasks across both vision and language modalities. First, on existing +benchmarks, zero-shot prompts with only tool documentation are sufficient for +eliciting proper tool usage, achieving performance on par with few-shot +prompts. Second, on a newly collected realistic tool-use dataset with hundreds +of available tool APIs, we show that tool documentation is significantly more +valuable than demonstrations, with zero-shot documentation significantly +outperforming few-shot without documentation. Third, we highlight the benefits +of tool documentations by tackling image generation and video tracking using +just-released unseen state-of-the-art models as tools. Finally, we highlight +the possibility of using tool documentation to automatically enable new +applications: by using nothing more than the documentation of GroundingDino, +Stable Diffusion, XMem, and SAM, LLMs can re-invent the functionalities of the +just-released Grounded-SAM and Track Anything models. +" +MathAttack: Attacking Large Language Models Towards Math Solving Ability,Zihao Zhou,http://arxiv.org/pdf/2309.01686v1.pdf,2023-09-04,['cs.cl'],2309.01686v1.pdf," With the boom of Large Language Models (LLMs), the research of solving Math +Word Problem (MWP) has recently made great progress. However, there are few +studies to examine the security of LLMs in math solving ability. Instead of +attacking prompts in the use of LLMs, we propose a MathAttack model to attack +MWP samples which are closer to the essence of security in solving math +problems. Compared to traditional text adversarial attack, it is essential to +preserve the mathematical logic of original MWPs during the attacking. To this +end, we propose logical entity recognition to identify logical entries which +are then frozen. Subsequently, the remaining text are attacked by adopting a +word-level attacker. Furthermore, we propose a new dataset RobustMath to +evaluate the robustness of LLMs in math solving ability. Extensive experiments +on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth +show that MathAttack could effectively attack the math solving ability of LLMs. +In the experiments, we observe that (1) Our adversarial samples from +higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy +(e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot +prompts); (2) Complex MWPs (such as more solving steps, longer text, more +numbers) are more vulnerable to attack; (3) We can improve the robustness of +LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our +practice and observation can serve as an important attempt towards enhancing +the robustness of LLMs in math solving ability. We will release our code and +dataset. +" +MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models,Kailai Yang,http://arxiv.org/pdf/2309.13567v2.pdf,2023-09-24,['cs.cl'],2309.13567v2.pdf," With the development of web technology, social media texts are becoming a +rich source for automatic mental health analysis. As traditional discriminative +methods bear the problem of low interpretability, the recent large language +models have been explored for interpretable mental health analysis on social +media, which aims to provide detailed explanations along with predictions. The +results show that ChatGPT can generate approaching-human explanations for its +correct classifications. However, LLMs still achieve unsatisfactory +classification performance in a zero-shot/few-shot manner. Domain-specific +finetuning is an effective solution, but faces 2 challenges: 1) lack of +high-quality training data. 2) no open-source LLMs for interpretable mental +health analysis were released to lower the finetuning cost. To alleviate these +problems, we build the first multi-task and multi-source interpretable mental +health instruction (IMHI) dataset on social media, with 105K data samples. The +raw social media data are collected from 10 existing sources covering 8 mental +health analysis tasks. We use expert-written few-shot prompts and collected +labels to prompt ChatGPT and obtain explanations from its responses. To ensure +the reliability of the explanations, we perform strict automatic and human +evaluations on the correctness, consistency, and quality of generated data. +Based on the IMHI dataset and LLaMA2 foundation models, we train MentalLLaMA, +the first open-source LLM series for interpretable mental health analysis with +instruction-following capability. We also evaluate the performance of +MentalLLaMA on the IMHI evaluation benchmark with 10 test sets, where their +correctness for making predictions and the quality of explanations are +examined. The results show that MentalLLaMA approaches state-of-the-art +discriminative methods in correctness and generates high-quality explanations. +" +FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation,Tu Vu,http://arxiv.org/pdf/2310.03214v1.pdf,2023-10-05,['cs.cl'],2310.03214v1.pdf," Most large language models (LLMs) are trained once and never updated; thus, +they lack the ability to dynamically adapt to our ever-changing world. In this +work, we perform a detailed study of the factuality of LLM-generated text in +the context of answering questions that test current world knowledge. +Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a +diverse range of question and answer types, including questions that require +fast-changing world knowledge as well as questions with false premises that +need to be debunked. We benchmark a diverse array of both closed and +open-source LLMs under a two-mode evaluation procedure that allows us to +measure both correctness and hallucination. Through human evaluations involving +more than 50K judgments, we shed light on limitations of these models and +demonstrate significant room for improvement: for instance, all models +(regardless of model size) struggle on questions that involve fast-changing +knowledge and false premises. Motivated by these results, we present +FreshPrompt, a simple few-shot prompting method that substantially boosts the +performance of an LLM on FreshQA by incorporating relevant and up-to-date +information retrieved from a search engine into the prompt. Our experiments +show that FreshPrompt outperforms both competing search engine-augmented +prompting methods such as Self-Ask (Press et al., 2022) as well as commercial +systems such as Perplexity.AI. Further analysis of FreshPrompt reveals that +both the number of retrieved evidences and their order play a key role in +influencing the correctness of LLM-generated answers. Additionally, instructing +the LLM to generate concise and direct answers helps reduce hallucination +compared to encouraging more verbose answers. To facilitate future work, we +release FreshQA at github.com/freshllms/freshqa and commit to updating it at +regular intervals. +" +A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT,Ce Zhou,http://arxiv.org/pdf/2302.09419v3.pdf,2023-02-18,"['cs.ai', 'cs.cl', 'cs.lg']",2302.09419v3.pdf," Pretrained Foundation Models (PFMs) are regarded as the foundation for +various downstream tasks with different data modalities. A PFM (e.g., BERT, +ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable +parameter initialization for a wide range of downstream applications. BERT +learns bidirectional encoder representations from Transformers, which are +trained on large datasets as contextual language models. Similarly, the +generative pretrained transformer (GPT) method employs Transformers as the +feature extractor and is trained using an autoregressive paradigm on large +datasets. Recently, ChatGPT shows promising success on large language models, +which applies an autoregressive language model with zero shot or few shot +prompting. The remarkable achievements of PFM have brought significant +breakthroughs to various fields of AI. Numerous studies have proposed different +methods, raising the demand for an updated survey. This study provides a +comprehensive review of recent research advancements, challenges, and +opportunities for PFMs in text, image, graph, as well as other data modalities. +The review covers the basic components and existing pretraining methods used in +natural language processing, computer vision, and graph learning. Additionally, +it explores advanced PFMs used for different data modalities and unified PFMs +that consider data quality and quantity. The review also discusses research +related to the fundamentals of PFMs, such as model efficiency and compression, +security, and privacy. Finally, the study provides key implications, future +research directions, challenges, and open problems in the field of PFMs. +Overall, this survey aims to shed light on the research of the PFMs on +scalability, security, logical reasoning ability, cross-domain learning +ability, and the user-friendly interactive ability for artificial general +intelligence. +" +Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model,Su-Youn Yoon,http://arxiv.org/pdf/2305.18638v1.pdf,2023-05-29,"['cs.cl', 'i.2.7']",2305.18638v1.pdf," In this study, we developed an automated short answer grading (ASAG) model +that provided both analytic scores and final holistic scores. Short answer +items typically consist of multiple sub-questions, and providing an analytic +score and the text span relevant to each sub-question can increase the +interpretability of the automated scores. Furthermore, they can be used to +generate actionable feedback for students. Despite these advantages, most +studies have focused on predicting only holistic scores due to the difficulty +in constructing dataset with manual annotations. To address this difficulty, we +used large language model (LLM)-based one-shot prompting and a text similarity +scoring model with domain adaptation using small manually annotated dataset. +The accuracy and quadratic weighted kappa of our model were 0.67 and 0.71 on a +subset of the publicly available ASAG dataset. The model achieved a substantial +improvement over the majority baseline. +" +DePlot: One-shot visual language reasoning by plot-to-table translation,Fangyu Liu,http://arxiv.org/pdf/2212.10505v2.pdf,2022-12-20,"['cs.cl', 'cs.ai', 'cs.cv']",2212.10505v2.pdf," Visual language such as charts and plots is ubiquitous in the human world. +Comprehending plots and charts requires strong reasoning skills. Prior +state-of-the-art (SOTA) models require at least tens of thousands of training +examples and their reasoning capabilities are still much limited, especially on +complex human-written queries. This paper presents the first one-shot solution +to visual language reasoning. We decompose the challenge of visual language +reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over +the translated text. The key in this method is a modality conversion module, +named as DePlot, which translates the image of a plot or chart to a linearized +table. The output of DePlot can then be directly used to prompt a pretrained +large language model (LLM), exploiting the few-shot reasoning capabilities of +LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing +unified task formats and metrics, and train DePlot end-to-end on this task. +DePlot can then be used off-the-shelf together with LLMs in a plug-and-play +fashion. Compared with a SOTA model finetuned on more than >28k data points, +DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over +finetuned SOTA on human-written queries from the task of chart QA. +" +CHAI-DT: A Framework for Prompting Conversational Generative AI Agents to Actively Participate in Co-Creation,Brandon Harwood,http://arxiv.org/pdf/2305.03852v1.pdf,2023-05-05,"['cs.hc', 'cs.ai']",2305.03852v1.pdf," This paper explores the potential for utilizing generative AI models in +group-focused co-creative frameworks to enhance problem solving and ideation in +business innovation and co-creation contexts, and proposes a novel prompting +technique for conversational generative AI agents which employ methods inspired +by traditional 'human-to-human' facilitation and instruction to enable active +contribution to Design Thinking, a co-creative framework. Through experiments +using this prompting technique, we gather evidence that conversational +generative transformers (i.e. ChatGPT) have the capability to contribute +context-specific, useful, and creative input into Design Thinking activities. +We also discuss the potential benefits, limitations, and risks associated with +using generative AI models in co-creative ideation and provide recommendations +for future research. +" +AceCoder: Utilizing Existing Code to Enhance Code Generation,Jia Li,http://arxiv.org/pdf/2303.17780v3.pdf,2023-03-31,"['cs.se', 'cs.ai']",2303.17780v3.pdf," Large Language Models (LLMs) have shown great success in code generation. +LLMs take as the input a prompt and output the code. A key question is how to +make prompts (i.e., Prompting Techniques). Existing prompting techniques are +designed for natural language generation and have low accuracy in code +generation. + In this paper, we propose a new prompting technique named AceCoder. Our +motivation is that code generation meets two unique challenges (i.e., +requirement understanding and code implementation). AceCoder contains two novel +mechanisms (i.e., guided code generation and example retrieval) to solve these +challenges. (1) Guided code generation asks LLMs first to analyze requirements +and output an intermediate preliminary (e.g., test cases). The preliminary is +used to clarify requirements and tell LLMs ""what to write"". (2) Example +retrieval selects similar programs as examples in prompts, which provide lots +of relevant content (e.g., algorithms, APIs) and teach LLMs ""how to write"". We +apply AceCoder to three LLMs (e.g., Codex) and evaluate it on three public +benchmarks using the Pass@k. Results show that AceCoder can significantly +improve the performance of LLMs on code generation. (1) In terms of Pass@1, +AceCoder outperforms the state-of-the-art baseline by up to 56.4% in MBPP, +70.7% in MBJP, and 88.4% in MBJSP. (2) AceCoder is effective in LLMs with +different sizes (i.e., 6B to 13B) and different languages (i.e., Python, Java, +and JavaScript). (3) Human evaluation shows human developers prefer programs +from AceCoder. +" +Compositional Semantic Parsing with Large Language Models,Andrew Drozdov,http://arxiv.org/pdf/2209.15003v2.pdf,2022-09-29,"['cs.cl', 'cs.ai']",2209.15003v2.pdf," Humans can reason compositionally when presented with new tasks. Previous +research shows that appropriate prompting techniques enable large language +models (LLMs) to solve artificial compositional generalization tasks such as +SCAN. In this work, we identify additional challenges in more realistic +semantic parsing tasks with larger vocabulary and refine these prompting +techniques to address them. Our best method is based on least-to-most +prompting: it decomposes the problem using prompting-based syntactic parsing, +then uses this decomposition to select appropriate exemplars and to +sequentially generate the semantic parse. This method allows us to set a new +state of the art for CFQ while requiring only 1% of the training data used by +traditional approaches. Due to the general nature of our approach, we expect +similar efforts will lead to new results in other tasks and domains, especially +for knowledge-intensive applications. +" +EvEntS ReaLM: Event Reasoning of Entity States via Language Models,Evangelia Spiliopoulou,http://arxiv.org/pdf/2211.05392v1.pdf,2022-11-10,['cs.cl'],2211.05392v1.pdf," This paper investigates models of event implications. Specifically, how well +models predict entity state-changes, by targeting their understanding of +physical attributes. Nominally, Large Language models (LLM) have been exposed +to procedural knowledge about how objects interact, yet our benchmarking shows +they fail to reason about the world. Conversely, we also demonstrate that +existing approaches often misrepresent the surprising abilities of LLMs via +improper task encodings and that proper model prompting can dramatically +improve performance of reported baseline results across multiple tasks. In +particular, our results indicate that our prompting technique is especially +useful for unseen attributes (out-of-domain) or when only limited data is +available. +" +GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4,Tom Kocmi,http://arxiv.org/pdf/2310.13988v1.pdf,2023-10-21,['cs.cl'],2310.13988v1.pdf," This paper introduces GEMBA-MQM, a GPT-based evaluation metric designed to +detect translation quality errors, specifically for the quality estimation +setting without the need for human reference translations. Based on the power +of large language models (LLM), GEMBA-MQM employs a fixed three-shot prompting +technique, querying the GPT-4 model to mark error quality spans. Compared to +previous works, our method has language-agnostic prompts, thus avoiding the +need for manual prompt preparation for new languages. + While preliminary results indicate that GEMBA-MQM achieves state-of-the-art +accuracy for system ranking, we advise caution when using it in academic works +to demonstrate improvements over other methods due to its dependence on the +proprietary, black-box GPT model. +" +Utilizing Language Models for Energy Load Forecasting,Hao Xue,http://arxiv.org/pdf/2310.17788v1.pdf,2023-10-26,"['cs.ai', 'cs.cl']",2310.17788v1.pdf," Energy load forecasting plays a crucial role in optimizing resource +allocation and managing energy consumption in buildings and cities. In this +paper, we propose a novel approach that leverages language models for energy +load forecasting. We employ prompting techniques to convert energy consumption +data into descriptive sentences, enabling fine-tuning of language models. By +adopting an autoregressive generating approach, our proposed method enables +predictions of various horizons of future energy load consumption. Through +extensive experiments on real-world datasets, we demonstrate the effectiveness +and accuracy of our proposed method. Our results indicate that utilizing +language models for energy load forecasting holds promise for enhancing energy +efficiency and facilitating intelligent decision-making in energy systems. +" +Eliciting Topic Hierarchies from Large Language Models,Grace Li,http://arxiv.org/pdf/2310.19275v1.pdf,2023-10-30,['cs.hc'],2310.19275v1.pdf," Finding topics to write about can be a mentally demanding process. However, +topic hierarchies can help writers explore topics of varying levels of +specificity. In this paper, we use large language models (LLMs) to help +construct topic hierarchies. Although LLMs have access to such knowledge, it +can be difficult to elicit due to issues of specificity, scope, and repetition. +We designed and tested three different prompting techniques to find one that +maximized accuracy. We found that prepending the general topic area to a prompt +yielded the most accurate results with 85% accuracy. We discuss applications of +this research including STEM writing, education, and content creation. +" +Structured Chain-of-Thought Prompting for Code Generation,Jia Li,http://arxiv.org/pdf/2305.06599v3.pdf,2023-05-11,"['cs.se', 'cs.cl']",2305.06599v3.pdf," Large Language Models (LLMs) (e.g., ChatGPT) have shown impressive +performance in code generation. LLMs take prompts as inputs, and +Chain-of-Thought (CoT) prompting is the state-of-the-art prompting technique. +CoT prompting asks LLMs first to generate CoTs (i.e., intermediate natural +language reasoning steps) and then output the code. However, CoT prompting is +designed for natural language generation and has low accuracy in code +generation. + In this paper, we propose Structured CoTs (SCoTs) and present a novel +prompting technique for code generation, named SCoT prompting. Our motivation +is source code contains rich structural information and any code can be +composed of three program structures (i.e., sequence, branch, and loop +structures). Intuitively, structured intermediate reasoning steps make for +structured source code. Thus, we ask LLMs to use program structures to build +CoTs, obtaining SCoTs. Then, LLMs generate the final code based on SCoTs. +Compared to CoT prompting, SCoT prompting explicitly constrains LLMs to think +about how to solve requirements from the view of source code and further the +performance of LLMs in code generation. We apply SCoT prompting to two LLMs +(i.e., ChatGPT and Codex) and evaluate it on three benchmarks (i.e., HumanEval, +MBPP, and MBCPP). (1) SCoT prompting outperforms the state-of-the-art baseline +- CoT prompting by up to 13.79% in Pass@1. (2) Human evaluation shows human +developers prefer programs from SCoT prompting. (3) SCoT prompting is robust to +examples and achieves substantial improvements. +" +The Impact of AI in Physics Education: A Comprehensive Review from GCSE to University Levels,Will Yeadon,http://arxiv.org/pdf/2309.05163v1.pdf,2023-09-10,['physics.ed-ph'],2309.05163v1.pdf," With the rapid evolution of Artificial Intelligence (AI), its potential +implications for higher education have become a focal point of interest. This +study delves into the capabilities of AI in Physics Education and offers +actionable AI policy recommendations. Using a Large Language Model (LLM), we +assessed its ability to answer 1337 Physics exam questions spanning GCSE, +A-Level, and Introductory University curricula. We employed various AI +prompting techniques: Zero Shot, In Context Learning, and Confirmatory +Checking, which merges Chain of Thought reasoning with Reflection. The AI's +proficiency varied across academic levels: it scored an average of 83.4% on +GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an +overall average of 59.9% using the most effective prompting technique. In a +separate test, the LLM's accuracy on 5000 mathematical operations was found to +decrease as the number of digits increased. Furthermore, when evaluated as a +marking tool, the LLM's concordance with human markers averaged at 50.8%, with +notable inaccuracies in marking straightforward questions, like +multiple-choice. Given these results, our recommendations underscore caution: +while current LLMs can consistently perform well on Physics questions at +earlier educational stages, their efficacy diminishes with advanced content and +complex calculations. LLM outputs often showcase novel methods not in the +syllabus, excessive verbosity, and miscalculations in basic arithmetic. This +suggests that at university, there's no substantial threat from LLMs for +non-invigilated Physics questions. However, given the LLMs' considerable +proficiency in writing Physics essays and coding abilities, non-invigilated +examinations of these skills in Physics are highly vulnerable to automated +completion by LLMs. This vulnerability also extends to Physics questions +pitched at lower academic levels. +" +HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models,Swaroop Mishra,http://arxiv.org/pdf/2208.08232v2.pdf,2022-08-17,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.hc', 'cs.lg']",2208.08232v2.pdf," Controlling the text generated by language models and customizing the content +has been a long-standing challenge. Existing prompting techniques proposed in +pursuit of providing control are task-specific and lack generality; this +provides overwhelming choices for non-expert users to find a suitable method +for their task. The effort associated with those techniques, such as in writing +examples, explanations, instructions, etc. further limits their adoption among +non-expert users. In this paper, we propose a simple prompting strategy HELP ME +THINK where we encourage GPT3 to help non-expert users by asking a set of +relevant questions and leveraging user answers to execute the task. We +demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. +Specifically, we focus on tasks that are hard for average humans and require +significant thinking to perform. We hope our work will encourage the +development of unconventional ways to harness the power of large language +models. +" +Enabling Conversational Interaction with Mobile UI using Large Language Models,Bryan Wang,http://arxiv.org/pdf/2209.08655v2.pdf,2022-09-18,"['cs.hc', 'cs.ai']",2209.08655v2.pdf," Conversational agents show the promise to allow users to interact with mobile +devices using language. However, to perform diverse UI tasks with natural +language, developers typically need to create separate datasets and models for +each specific task, which is expensive and effort-consuming. Recently, +pre-trained large language models (LLMs) have been shown capable of +generalizing to various downstream tasks when prompted with a handful of +examples from the target task. This paper investigates the feasibility of +enabling versatile conversational interactions with mobile UIs using a single +LLM. We designed prompting techniques to adapt an LLM to mobile UIs. We +experimented with four important modeling tasks that address various scenarios +in conversational interaction. Our method achieved competitive performance on +these challenging tasks without requiring dedicated datasets and training, +offering a lightweight and generalizable approach to enable language-based +mobile interaction. +" +Teaching Algorithmic Reasoning via In-context Learning,Hattie Zhou,http://arxiv.org/pdf/2211.09066v1.pdf,2022-11-15,"['cs.lg', 'cs.ai', 'cs.cl']",2211.09066v1.pdf," Large language models (LLMs) have shown increasing in-context learning +capabilities through scaling up model and data size. Despite this progress, +LLMs are still unable to solve algorithmic reasoning problems. While providing +a rationale with the final answer has led to further improvements in multi-step +reasoning problems, Anil et al. 2022 showed that even simple algorithmic +reasoning tasks such as parity are far from solved. In this work, we identify +and study four key stages for successfully teaching algorithmic reasoning to +LLMs: (1) formulating algorithms as skills, (2) teaching multiple skills +simultaneously (skill accumulation), (3) teaching how to combine skills (skill +composition) and (4) teaching how to use skills as tools. We show that it is +possible to teach algorithmic reasoning to LLMs via in-context learning, which +we refer to as algorithmic prompting. We evaluate our approach on a variety of +arithmetic and quantitative reasoning tasks, and demonstrate significant boosts +in performance over existing prompting techniques. In particular, for long +parity, addition, multiplication and subtraction, we achieve an error reduction +of approximately 10x, 9x, 5x and 2x respectively compared to the best available +baselines. +" +Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing,Justus Mattern,http://arxiv.org/pdf/2212.10678v1.pdf,2022-12-20,"['cs.cl', 'cs.lg']",2212.10678v1.pdf," Generated texts from large pretrained language models have been shown to +exhibit a variety of harmful, human-like biases about various demographics. +These findings prompted large efforts aiming to understand and measure such +effects, with the goal of providing benchmarks that can guide the development +of techniques mitigating these stereotypical associations. However, as recent +research has pointed out, the current benchmarks lack a robust experimental +setup, consequently hindering the inference of meaningful conclusions from +their evaluation metrics. In this paper, we extend these arguments and +demonstrate that existing techniques and benchmarks aiming to measure +stereotypes tend to be inaccurate and consist of a high degree of experimental +noise that severely limits the knowledge we can gain from benchmarking language +models based on them. Accordingly, we propose a new framework for robustly +measuring and quantifying biases exhibited by generative language models. +Finally, we use this framework to investigate GPT-3's occupational gender bias +and propose prompting techniques for mitigating these biases without the need +for fine-tuning. +" +Image To Tree with Recursive Prompting,James Batten,http://arxiv.org/pdf/2301.00447v1.pdf,2023-01-01,"['cs.cv', 'cs.lg']",2301.00447v1.pdf," Extracting complex structures from grid-based data is a common key step in +automated medical image analysis. The conventional solution to recovering +tree-structured geometries typically involves computing the minimal cost path +through intermediate representations derived from segmentation masks. However, +this methodology has significant limitations in the context of projective +imaging of tree-structured 3D anatomical data such as coronary arteries, since +there are often overlapping branches in the 2D projection. In this work, we +propose a novel approach to predicting tree connectivity structure which +reformulates the task as an optimization problem over individual steps of a +recursive process. We design and train a two-stage model which leverages the +UNet and Transformer architectures and introduces an image-based prompting +technique. Our proposed method achieves compelling results on a pair of +synthetic datasets, and outperforms a shortest-path baseline. +" +Large Language Models Can Be Easily Distracted by Irrelevant Context,Freda Shi,http://arxiv.org/pdf/2302.00093v3.pdf,2023-01-31,"['cs.cl', 'cs.ai']",2302.00093v3.pdf," Large language models have achieved impressive performance on various natural +language processing tasks. However, so far they have been evaluated primarily +on benchmarks where all information in the input context is relevant for +solving the task. In this work, we investigate the distractibility of large +language models, i.e., how the model problem-solving accuracy can be influenced +by irrelevant context. In particular, we introduce Grade-School Math with +Irrelevant Context (GSM-IC), an arithmetic reasoning dataset with irrelevant +information in the problem description. We use this benchmark to measure the +distractibility of cutting-edge prompting techniques for large language models, +and find that the model performance is dramatically decreased when irrelevant +information is included. We also identify several approaches for mitigating +this deficiency, such as decoding with self-consistency and adding to the +prompt an instruction that tells the language model to ignore the irrelevant +information. +" +Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models,Zhihong Shao,http://arxiv.org/pdf/2302.00618v1.pdf,2023-02-01,['cs.cl'],2302.00618v1.pdf," Large language models can perform various reasoning tasks by using +chain-of-thought prompting, which guides them to find answers through +step-by-step demonstrations. However, the quality of the prompts depends on the +demonstrations given to the models, and creating many of them by hand is +costly. We introduce Synthetic prompting, a method that leverages a few +handcrafted examples to prompt the model to generate more examples by itself, +and selects effective demonstrations to elicit better reasoning. Our method +alternates between a backward and forward process to generate new examples. The +backward process generates a question that match a sampled reasoning chain, so +that the question is solvable and clear. The forward process produces a more +detailed reasoning chain for the question, improving the quality of the +example. We evaluate our method on numerical, symbolic, and algorithmic +reasoning tasks, and show that it outperforms existing prompting techniques. +" +Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference,Ming Li,http://arxiv.org/pdf/2302.09582v4.pdf,2023-02-19,"['cs.ai', 'cs.cl']",2302.09582v4.pdf," Understanding how language supports emotion inference remains a topic of +debate in emotion science. The present study investigated whether +language-derived emotion-concept knowledge would causally support emotion +inference by manipulating the language-specific knowledge representations in +large language models. Using the prompt technique, 14 attributes of emotion +concepts were found to be represented by distinct artificial neuron +populations. By manipulating these attribute-related neurons, the majority of +the emotion inference tasks showed performance deterioration compared to random +manipulations. The attribute-specific performance deterioration was related to +the importance of different attributes in human mental space. Our findings +provide causal evidence in support of a language-based mechanism for emotion +inference and highlight the contributions of emotion-concept knowledge. +" +MathPrompter: Mathematical Reasoning using Large Language Models,Shima Imani,http://arxiv.org/pdf/2303.05398v1.pdf,2023-03-04,"['cs.cl', 'cs.ai']",2303.05398v1.pdf," Large Language Models (LLMs) have limited performance when solving arithmetic +reasoning tasks and often provide incorrect answers. Unlike natural language +understanding, math problems typically have a single correct answer, making the +task of generating accurate solutions more challenging for LLMs. To the best of +our knowledge, we are not aware of any LLMs that indicate their level of +confidence in their responses which fuels a trust deficit in these models +impeding their adoption. To address this deficiency, we propose `MathPrompter', +a technique that improves performance of LLMs on arithmetic problems along with +increased reliance in the predictions. MathPrompter uses the Zero-shot +chain-of-thought prompting technique to generate multiple Algebraic expressions +or Python functions to solve the same math problem in different ways and +thereby raise the confidence level in the output results. This is in contrast +to other prompt based CoT methods, where there is no check on the validity of +the intermediate steps followed. Our technique improves over state-of-the-art +on the MultiArith dataset ($78.7\%\rightarrow92.5\%$) evaluated using 175B +parameter GPT-based LLM. +" +Zero-shot Temporal Relation Extraction with ChatGPT,Chenhan Yuan,http://arxiv.org/pdf/2304.05454v1.pdf,2023-04-11,"['cs.cl', 'cs.ai']",2304.05454v1.pdf," The goal of temporal relation extraction is to infer the temporal relation +between two events in the document. Supervised models are dominant in this +task. In this work, we investigate ChatGPT's ability on zero-shot temporal +relation extraction. We designed three different prompt techniques to break +down the task and evaluate ChatGPT. Our experiments show that ChatGPT's +performance has a large gap with that of supervised methods and can heavily +rely on the design of prompts. We further demonstrate that ChatGPT can infer +more small relation classes correctly than supervised methods. The current +shortcomings of ChatGPT on temporal relation extraction are also discussed in +this paper. We found that ChatGPT cannot keep consistency during temporal +inference and it fails in actively long-dependency temporal inference. +" +An Empirical Study on the Robustness of the Segment Anything Model (SAM),Yuqing Wang,http://arxiv.org/pdf/2305.06422v2.pdf,2023-05-10,['cs.cv'],2305.06422v2.pdf," The Segment Anything Model (SAM) is a foundation model for general image +segmentation. Although it exhibits impressive performance predominantly on +natural images, understanding its robustness against various image +perturbations and domains is critical for real-world applications where such +challenges frequently arise. In this study we conduct a comprehensive +robustness investigation of SAM under diverse real-world conditions. Our +experiments encompass a wide range of image perturbations. Our experimental +results demonstrate that SAM's performance generally declines under perturbed +images, with varying degrees of vulnerability across different perturbations. +By customizing prompting techniques and leveraging domain knowledge based on +the unique characteristics of each dataset, the model's resilience to these +perturbations can be enhanced, addressing dataset-specific challenges. This +work sheds light on the limitations and strengths of SAM in real-world +applications, promoting the development of more robust and versatile image +segmentation solutions. +" +SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables,Xinyuan Lu,http://arxiv.org/pdf/2305.13186v3.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.13186v3.pdf," Current scientific fact-checking benchmarks exhibit several shortcomings, +such as biases arising from crowd-sourced claims and an over-reliance on +text-based evidence. We present SCITAB, a challenging evaluation dataset +consisting of 1.2K expert-verified scientific claims that 1) originate from +authentic scientific publications and 2) require compositional reasoning for +verification. The claims are paired with evidence-containing scientific tables +annotated with labels. Through extensive evaluations, we demonstrate that +SCITAB poses a significant challenge to state-of-the-art models, including +table-based pretraining models and large language models. All models except +GPT-4 achieved performance barely above random guessing. Popular prompting +techniques, such as Chain-of-Thought, do not achieve much performance gains on +SCITAB. Our analysis uncovers several unique challenges posed by SCITAB, +including table grounding, claim ambiguity, and compositional reasoning. Our +codes and data are publicly available at https://github.com/XinyuanLu00/SciTab. +" +Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations,Tiziano Labruna,http://arxiv.org/pdf/2305.14556v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14556v1.pdf," Large pre-trained language models have exhibited unprecedented capabilities +in producing high-quality text via prompting techniques. This fact introduces +new possibilities for data collection and annotation, particularly in +situations where such data is scarce, complex to gather, expensive, or even +sensitive. In this paper, we explore the potential of these models to generate +and annotate goal-oriented dialogues, and conduct an in-depth analysis to +evaluate their quality. Our experiments employ ChatGPT, and encompass three +categories of goal-oriented dialogues (task-oriented, collaborative, and +explanatory), two generation modes (interactive and one-shot), and two +languages (English and Italian). Based on extensive human-based evaluations, we +demonstrate that the quality of generated dialogues and annotations is on par +with those generated by humans. +" +StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code,Hannah McLean Babe,http://arxiv.org/pdf/2306.04556v1.pdf,2023-06-07,"['cs.lg', 'cs.hc', 'cs.se']",2306.04556v1.pdf," Code LLMs are being rapidly deployed and there is evidence that they can make +professional programmers more productive. Current benchmarks for code +generation measure whether models generate correct programs given an expert +prompt. In this paper, we present a new benchmark containing multiple prompts +per problem, written by a specific population of non-expert prompters: +beginning programmers. StudentEval contains 1,749 prompts for 48 problems, +written by 80 students who have only completed one semester of Python +programming. Our students wrote these prompts while working interactively with +a Code LLM, and we observed very mixed success rates. We use StudentEval to +evaluate 5 Code LLMs and find that StudentEval is a better discriminator of +model performance than existing benchmarks. We analyze the prompts and find +significant variation in students' prompting techniques. We also find that +nondeterministic LLM sampling could mislead students into thinking that their +prompts are more (or less) effective than they actually are, which has +implications for how to teach with Code LLMs. +" +Knowledge-Prompted Estimator: A Novel Approach to Explainable Machine Translation Assessment,Hao Yang,http://arxiv.org/pdf/2306.07486v1.pdf,2023-06-13,['cs.cl'],2306.07486v1.pdf," Cross-lingual Machine Translation (MT) quality estimation plays a crucial +role in evaluating translation performance. GEMBA, the first MT quality +assessment metric based on Large Language Models (LLMs), employs one-step +prompting to achieve state-of-the-art (SOTA) in system-level MT quality +estimation; however, it lacks segment-level analysis. In contrast, +Chain-of-Thought (CoT) prompting outperforms one-step prompting by offering +improved reasoning and explainability. In this paper, we introduce +Knowledge-Prompted Estimator (KPE), a CoT prompting method that combines three +one-step prompting techniques, including perplexity, token-level similarity, +and sentence-level similarity. This method attains enhanced performance for +segment-level estimation compared with previous deep learning models and +one-step prompting approaches. Furthermore, supplementary experiments on +word-level visualized alignment demonstrate that our KPE method significantly +improves token alignment compared with earlier models and provides better +interpretability for MT quality estimation. Code will be released upon +publication. +" +Questioning the Survey Responses of Large Language Models,Ricardo Dominguez-Olmedo,http://arxiv.org/pdf/2306.07951v2.pdf,2023-06-13,['cs.cl'],2306.07951v2.pdf," As large language models increase in capability, researchers have started to +conduct surveys of all kinds on these models with varying scientific +motivations. In this work, we examine what we can learn from language models' +survey responses on the basis of the well-established American Community Survey +(ACS) by the U.S. Census Bureau. Using a de-facto standard multiple-choice +prompting technique and evaluating 40 different language models, hundreds of +thousands of times each on questions from the ACS, we systematically establish +two dominant patterns. First, models have significant position and labeling +biases, for example, towards survey responses labeled with the letter ""A"". +Second, when adjusting for labeling biases through randomized answer ordering, +models across the board trend towards uniformly random survey responses. In +fact, binary classifiers can almost perfectly differentiate between models' +responses to the ACS and the responses of the US census. Taken together, our +findings suggest caution in treating survey responses from language models as +equivalent to those of human populations at present time. +" +Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering,Rabiul Awal,http://arxiv.org/pdf/2306.09996v1.pdf,2023-06-16,"['cs.cv', 'cs.cl']",2306.09996v1.pdf," Visual question answering (VQA) is a challenging task that requires the +ability to comprehend and reason with visual information. While recent +vision-language models have made strides, they continue to struggle with +zero-shot VQA, particularly in handling complex compositional questions and +adapting to new domains i.e. knowledge-based reasoning. This paper explores the +use of various prompting strategies, focusing on the BLIP2 model, to enhance +zero-shot VQA performance. We conduct a comprehensive investigation across +several VQA datasets, examining the effectiveness of different question +templates, the role of few-shot exemplars, the impact of chain-of-thought (CoT) +reasoning, and the benefits of incorporating image captions as additional +visual cues. Despite the varied outcomes, our findings demonstrate that +carefully designed question templates and the integration of additional visual +cues, like image captions, can contribute to improved VQA performance, +especially when used in conjunction with few-shot examples. However, we also +identify a limitation in the use of chain-of-thought rationalization, which +negatively affects VQA accuracy. Our study thus provides critical insights into +the potential of prompting for improving zero-shot VQA performance. +" +Extracting Multi-valued Relations from Language Models,Sneha Singhania,http://arxiv.org/pdf/2307.03122v2.pdf,2023-07-06,['cs.cl'],2307.03122v2.pdf," The widespread usage of latent language representations via pre-trained +language models (LMs) suggests that they are a promising source of structured +knowledge. However, existing methods focus only on a single object per +subject-relation pair, even though often multiple objects are correct. To +overcome this limitation, we analyze these representations for their potential +to yield materialized multi-object relational knowledge. We formulate the +problem as a rank-then-select task. For ranking candidate objects, we evaluate +existing prompting techniques and propose new ones incorporating domain +knowledge. Among the selection methods, we find that choosing objects with a +likelihood above a learned relation-specific threshold gives a 49.5% F1 score. +Our results highlight the difficulty of employing LMs for the multi-valued +slot-filling task and pave the way for further research on extracting +relational knowledge from latent language representations. +" +Prompts Should not be Seen as Secrets: Systematically Measuring Prompt Extraction Attack Success,Yiming Zhang,http://arxiv.org/pdf/2307.06865v1.pdf,2023-07-13,"['cs.cl', 'cs.ai']",2307.06865v1.pdf," The generations of large language models are commonly controlled through +prompting techniques, where a user's query to the model is prefixed with a +prompt that aims to guide the model's behaviour on the query. The prompts used +by companies to guide their models are often treated as secrets, to be hidden +from the user making the query. They have even been treated as commodities to +be bought and sold. However, there has been anecdotal evidence showing that the +prompts can be extracted by a user even when they are kept secret. In this +paper, we present a framework for systematically measuring the success of +prompt extraction attacks. In experiments with multiple sources of prompts and +multiple underlying language models, we find that simple text-based attacks can +in fact reveal prompts with high probability. +" +Leveraging Large Language Models to Generate Answer Set Programs,Adam Ishay,http://arxiv.org/pdf/2307.07699v1.pdf,2023-07-15,"['cs.ai', 'cs.cl', 'cs.sc']",2307.07699v1.pdf," Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated +exceptional performance in various natural language processing tasks and have +shown the ability to solve certain reasoning problems. However, their reasoning +capabilities are limited and relatively shallow, despite the application of +various prompting techniques. In contrast, formal logic is adept at handling +complex reasoning, but translating natural language descriptions into formal +logic is a challenging task that non-experts struggle with. This paper proposes +a neuro-symbolic method that combines the strengths of large language models +and answer set programming. Specifically, we employ an LLM to transform natural +language descriptions of logic puzzles into answer set programs. We carefully +design prompts for an LLM to convert natural language descriptions into answer +set programs in a step by step manner. Surprisingly, with just a few in-context +learning examples, LLMs can generate reasonably complex answer set programs. +The majority of errors made are relatively simple and can be easily corrected +by humans, thus enabling LLMs to effectively assist in the creation of answer +set programs. +" +Fixing Rust Compilation Errors using LLMs,Pantazis Deligiannis,http://arxiv.org/pdf/2308.05177v1.pdf,2023-08-09,"['cs.se', 'cs.pl']",2308.05177v1.pdf," The Rust programming language, with its safety guarantees, has established +itself as a viable choice for low-level systems programming language over the +traditional, unsafe alternatives like C/C++. These guarantees come from a +strong ownership-based type system, as well as primitive support for features +like closures, pattern matching, etc., that make the code more concise and +amenable to reasoning. These unique Rust features also pose a steep learning +curve for programmers. + This paper presents a tool called RustAssistant that leverages the emergent +capabilities of Large Language Models (LLMs) to automatically suggest fixes for +Rust compilation errors. RustAssistant uses a careful combination of prompting +techniques as well as iteration with an LLM to deliver high accuracy of fixes. +RustAssistant is able to achieve an impressive peak accuracy of roughly 74% on +real-world compilation errors in popular open-source Rust repositories. We plan +to release our dataset of Rust compilation errors to enable further research. +" +The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation,Patrick Fernandes,http://arxiv.org/pdf/2308.07286v1.pdf,2023-08-14,"['cs.cl', 'cs.lg']",2308.07286v1.pdf," Automatic evaluation of machine translation (MT) is a critical tool driving +the rapid iterative development of MT systems. While considerable progress has +been made on estimating a single scalar quality score, current metrics lack the +informativeness of more detailed schemes that annotate individual errors, such +as Multidimensional Quality Metrics (MQM). In this paper, we help fill this gap +by proposing AutoMQM, a prompting technique which leverages the reasoning and +in-context learning capabilities of large language models (LLMs) and asks them +to identify and categorize errors in translations. We start by evaluating +recent LLMs, such as PaLM and PaLM-2, through simple score prediction +prompting, and we study the impact of labeled data through in-context learning +and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that +it improves performance compared to just prompting for scores (with +particularly large gains for larger models) while providing interpretability +through error spans that align with human annotations. +" +Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought,Bin Lei,http://arxiv.org/pdf/2308.08614v1.pdf,2023-08-16,"['cs.lg', 'cs.ai', 'cs.cl']",2308.08614v1.pdf," Recent advancements in large-scale models, such as GPT-4, have showcased +remarkable capabilities in addressing standard queries. However, when facing +complex problems that require multi-step logical reasoning, their accuracy +dramatically decreases. Current research has explored the realm of +\textit{prompting engineering} to bolster the inferential capacities of these +models. Our paper unveils a pioneering prompting technique, dubbed +\textit{Graph of Thoughts (GoT)}. Through testing on a trio of escalating +challenges: the 24-point game, resolution of high-degree polynomial equations, +and derivation of formulas for recursive sequences, our method outperformed +GPT-4, achieving accuracy improvements of $89.7\%$, $86\%$, and $56\%$ for each +respective task. Moreover, when juxtaposed with the state-of-the-art (SOTA) +prompting method, \textit{Tree of Thought (ToT)}, our approach registered an +average accuracy boost of $23\%$, $24\%$, and $15\%$. +" +DevGPT: Studying Developer-ChatGPT Conversations,Tao Xiao,http://arxiv.org/pdf/2309.03914v1.pdf,2023-08-31,['cs.se'],2309.03914v1.pdf," The emergence of large language models (LLMs) such as ChatGPT has disrupted +the landscape of software development. Many studies are investigating the +quality of responses generated by ChatGPT, the efficacy of various prompting +techniques, and its comparative performance in programming contests, to name a +few examples. Yet, we know very little about how ChatGPT is actually used by +software developers. What questions do developers present to ChatGPT? What are +the dynamics of these interactions? What is the backdrop against which these +conversations are held, and how do the conversations feedback into the +artifacts of their work? To close this gap, we introduce DevGPT, a curated +dataset which encompasses 17,913 prompts and ChatGPT's responses including +11,751 code snippets, coupled with the corresponding software development +artifacts -- ranging from source code, commits, issues, pull requests, to +discussions and Hacker News threads -- to enable the analysis of the context +and implications of these developer interactions with ChatGPT. +" +Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting,Chao-Han Huck Yang,http://arxiv.org/pdf/2309.15649v2.pdf,2023-09-27,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as']",2309.15649v2.pdf," We explore the ability of large language models (LLMs) to act as speech +recognition post-processors that perform rescoring and error correction. Our +first focus is on instruction prompting to let LLMs perform these task without +fine-tuning, for which we evaluate different prompting schemes, both zero- and +few-shot in-context learning, and a novel task activation prompting method that +combines causal instructions and demonstration to increase its context windows. +Next, we show that rescoring only by in-context learning with frozen LLMs +achieves results that are competitive with rescoring by domain-tuned LMs, using +a pretrained first-pass recognition system and rescoring output on two +out-of-domain tasks (ATIS and WSJ). By combining prompting techniques with +fine-tuning we achieve error rates below the N-best oracle level, showcasing +the generalization power of the LLMs. +" +UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities,Hejia Geng,http://arxiv.org/pdf/2310.01441v1.pdf,2023-09-30,"['cs.cl', 'cs.ai']",2310.01441v1.pdf," Large Language Models (LLMs) have demonstrated impressive inferential +capabilities, with numerous research endeavors devoted to enhancing this +capacity through prompting. Despite these efforts, a unified epistemological +foundation is still conspicuously absent. Drawing inspiration from Kant's a +priori philosophy, we propose the UPAR prompting framework, designed to emulate +the structure of human cognition within LLMs. The UPAR framework is delineated +into four phases: ""Understand"", ""Plan"", ""Act"", and ""Reflect"", enabling the +extraction of structured information from complex contexts, prior planning of +solutions, execution according to plan, and self-reflection. This structure +significantly augments the explainability and accuracy of LLM inference, +producing a human-understandable and inspectable inferential trajectory. +Furthermore, our work offers an epistemological foundation for existing +prompting techniques, allowing for a possible systematic integration of these +methods. With GPT-4, our approach elevates the accuracy from COT baseline of +22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in +the causal judgment task. +" +Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models,Huaixiu Steven Zheng,http://arxiv.org/pdf/2310.06117v1.pdf,2023-10-09,"['cs.lg', 'cs.ai', 'cs.cl']",2310.06117v1.pdf," We present Step-Back Prompting, a simple prompting technique that enables +LLMs to do abstractions to derive high-level concepts and first principles from +instances containing specific details. Using the concepts and principles to +guide the reasoning steps, LLMs significantly improve their abilities in +following a correct reasoning path towards the solution. We conduct experiments +of Step-Back Prompting with PaLM-2L models and observe substantial performance +gains on a wide range of challenging reasoning-intensive tasks including STEM, +Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back Prompting +improves PaLM-2L performance on MMLU Physics and Chemistry by 7% and 11%, +TimeQA by 27%, and MuSiQue by 7%. +" +POSQA: Probe the World Models of LLMs with Size Comparisons,Chang Shu,http://arxiv.org/pdf/2310.13394v1.pdf,2023-10-20,"['cs.cl', 'cs.ai', 'cs.cy']",2310.13394v1.pdf," Embodied language comprehension emphasizes that language understanding is not +solely a matter of mental processing in the brain but also involves +interactions with the physical and social environment. With the explosive +growth of Large Language Models (LLMs) and their already ubiquitous presence in +our daily lives, it is becoming increasingly necessary to verify their +real-world understanding. Inspired by cognitive theories, we propose POSQA: a +Physical Object Size Question Answering dataset with simple size comparison +questions to examine the extremity and analyze the potential mechanisms of the +embodied comprehension of the latest LLMs. + We show that even the largest LLMs today perform poorly under the zero-shot +setting. We then push their limits with advanced prompting techniques and +external knowledge augmentation. Furthermore, we investigate whether their +real-world comprehension primarily derives from contextual information or +internal weights and analyse the impact of prompt formats and report bias of +different objects. Our results show that real-world understanding that LLMs +shaped from textual data can be vulnerable to deception and confusion by the +surface form of prompts, which makes it less aligned with human behaviours. +" +MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning,Zayne Sprague,http://arxiv.org/pdf/2310.16049v1.pdf,2023-10-24,['cs.cl'],2310.16049v1.pdf," While large language models (LLMs) equipped with techniques like +chain-of-thought prompting have demonstrated impressive capabilities, they +still fall short in their ability to reason robustly in complex settings. +However, evaluating LLM reasoning is challenging because system capabilities +continue to grow while benchmark datasets for tasks like logical deduction have +remained static. We introduce MuSR, a dataset for evaluating language models on +multistep soft reasoning tasks specified in a natural language narrative. This +dataset has two crucial features. First, it is created through a novel +neurosymbolic synthetic-to-natural generation algorithm, enabling the +construction of complex reasoning instances that challenge GPT-4 (e.g., murder +mysteries roughly 1000 words in length) and which can be scaled further as more +capable LLMs are released. Second, our dataset instances are free text +narratives corresponding to real-world domains of reasoning; this makes it +simultaneously much more challenging than other synthetically-crafted +benchmarks while remaining realistic and tractable for human annotators to +solve with high accuracy. We evaluate a range of LLMs and prompting techniques +on this dataset and characterize the gaps that remain for techniques like +chain-of-thought to perform robust reasoning. +" +"Supercharging academic writing with generative AI: framework, techniques, and caveats",Zhicheng Lin,http://arxiv.org/pdf/2310.17143v1.pdf,2023-10-26,"['cs.cy', 'cs.cl']",2310.17143v1.pdf," Academic writing is an indispensable yet laborious part of the research +enterprise. This Perspective maps out principles and methods for using +generative artificial intelligence (AI), specifically large language models +(LLMs), to elevate the quality and efficiency of academic writing. We introduce +a human-AI collaborative framework that delineates the rationale (why), process +(how), and nature (what) of AI engagement in writing. The framework pinpoints +both short-term and long-term reasons for engagement and their underlying +mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals +the role of AI throughout the writing process, conceptualized through a +two-stage model for human-AI collaborative writing, and the nature of AI +assistance in writing, represented through a model of writing-assistance types +and levels. Building on this framework, we describe effective prompting +techniques for incorporating AI into the writing routine (outlining, drafting, +and editing) as well as strategies for maintaining rigorous scholarship, +adhering to varied journal policies, and avoiding overreliance on AI. +Ultimately, the prudent integration of AI into academic writing can ease the +communication burden, empower authors, accelerate discovery, and promote +diversity in science. +" +Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task,Neema Kotonya,http://arxiv.org/pdf/2311.00686v1.pdf,2023-11-01,['cs.cl'],2311.00686v1.pdf," This paper describes and analyzes our participation in the 2023 Eval4NLP +shared task, which focuses on assessing the effectiveness of prompt-based +techniques to empower Large Language Models to handle the task of quality +estimation, particularly in the context of evaluating machine translations and +summaries. We conducted systematic experiments with various prompting +techniques, including standard prompting, prompts informed by annotator +instructions, and innovative chain-of-thought prompting. In addition, we +integrated these approaches with zero-shot and one-shot learning methods to +maximize the efficacy of our evaluation procedures. Our work reveals that +combining these approaches using a ""small"", open source model (orca_mini_v3_7B) +yields competitive results. +" +Can Large Language Models Design Accurate Label Functions?,Naiqing Guan,http://arxiv.org/pdf/2311.00739v1.pdf,2023-11-01,"['cs.cl', 'cs.db', 'cs.lg', 'h.2.8; i.5.4']",2311.00739v1.pdf," Programmatic weak supervision methodologies facilitate the expedited labeling +of extensive datasets through the use of label functions (LFs) that encapsulate +heuristic data sources. Nonetheless, the creation of precise LFs necessitates +domain expertise and substantial endeavors. Recent advances in pre-trained +language models (PLMs) have exhibited substantial potential across diverse +tasks. However, the capacity of PLMs to autonomously formulate accurate LFs +remains an underexplored domain. In this research, we address this gap by +introducing DataSculpt, an interactive framework that harnesses PLMs for the +automated generation of LFs. Within DataSculpt, we incorporate an array of +prompting techniques, instance selection strategies, and LF filtration methods +to explore the expansive design landscape. Ultimately, we conduct a thorough +assessment of DataSculpt's performance on 12 real-world datasets, encompassing +a range of tasks. This evaluation unveils both the strengths and limitations of +contemporary PLMs in LF design. +" +Prompting as Probing: Using Language Models for Knowledge Base Construction,Dimitrios Alivanistos,http://arxiv.org/pdf/2208.11057v3.pdf,2022-08-23,"['cs.cl', 'cs.ai']",2208.11057v3.pdf," Language Models (LMs) have proven to be useful in various downstream +applications, such as summarisation, translation, question answering and text +classification. LMs are becoming increasingly important tools in Artificial +Intelligence, because of the vast quantity of information they can store. In +this work, we present ProP (Prompting as Probing), which utilizes GPT-3, a +large Language Model originally proposed by OpenAI in 2020, to perform the task +of Knowledge Base Construction (KBC). ProP implements a multi-step approach +that combines a variety of prompting techniques to achieve this. Our results +show that manual prompt curation is essential, that the LM must be encouraged +to give answer sets of variable lengths, in particular including empty answer +sets, that true/false questions are a useful device to increase precision on +suggestions generated by the LM, that the size of the LM is a crucial factor, +and that a dictionary of entity aliases improves the LM score. Our evaluation +study indicates that these proposed techniques can substantially enhance the +quality of the final predictions: ProP won track 2 of the LM-KBC competition, +outperforming the baseline by 36.4 percentage points. Our implementation is +available on https://github.com/HEmile/iswc-challenge. +" +Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors,Mohammad Reza Taesiri,http://arxiv.org/pdf/2210.02506v1.pdf,2022-10-05,"['cs.cl', 'cs.se']",2210.02506v1.pdf," Video game testing requires game-specific knowledge as well as common sense +reasoning about the events in the game. While AI-driven agents can satisfy the +first requirement, it is not yet possible to meet the second requirement +automatically. Therefore, video game testing often still relies on manual +testing, and human testers are required to play the game thoroughly to detect +bugs. As a result, it is challenging to fully automate game testing. In this +study, we explore the possibility of leveraging the zero-shot capabilities of +large language models for video game bug detection. By formulating the bug +detection problem as a question-answering task, we show that large language +models can identify which event is buggy in a sequence of textual descriptions +of events from a game. To this end, we introduce the GameBugDescriptions +benchmark dataset, which consists of 167 buggy gameplay videos and a total of +334 question-answer pairs across 8 games. We extensively evaluate the +performance of six models across the OPT and InstructGPT large language model +families on our benchmark dataset. Our results show promising results for +employing language models to detect video game bugs. With the proper prompting +technique, we could achieve an accuracy of 70.66%, and on some video games, up +to 78.94%. Our code, evaluation data and the benchmark can be found on +https://asgaardlab.github.io/LLMxBugs +" +Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt,Hao Li,http://arxiv.org/pdf/2302.01171v1.pdf,2023-02-02,"['cs.cv', 'cs.ai']",2302.01171v1.pdf," Recently, inspired by DETR variants, query-based end-to-end instance +segmentation (QEIS) methods have outperformed CNN-based models on large-scale +datasets. Yet they would lose efficacy when only a small amount of training +data is available since it's hard for the crucial queries/kernels to learn +localization and shape priors. To this end, this work offers a novel +unsupervised pre-training solution for low-data regimes. Inspired by the recent +success of the Prompting technique, we introduce a new pre-training method that +boosts QEIS models by giving Saliency Prompt for queries/kernels. Our method +contains three parts: 1) Saliency Masks Proposal is responsible for generating +pseudo masks from unlabeled images based on the saliency mechanism. 2) +Prompt-Kernel Matching transfers pseudo masks into prompts and injects the +corresponding localization and shape priors to the best-matched kernels. 3) +Kernel Supervision is applied to supply supervision at the kernel level for +robust learning. From a practical perspective, our pre-training method helps +QEIS models achieve a similar convergence speed and comparable performance with +CNN-based models in low-data regimes. Experimental results show that our method +significantly boosts several QEIS models on three datasets. Code will be made +available. +" +One-Shot Labeling for Automatic Relevance Estimation,Sean MacAvaney,http://arxiv.org/pdf/2302.11266v2.pdf,2023-02-22,['cs.ir'],2302.11266v2.pdf," Dealing with unjudged documents (""holes"") in relevance assessments is a +perennial problem when evaluating search systems with offline experiments. +Holes can reduce the apparent effectiveness of retrieval systems during +evaluation and introduce biases in models trained with incomplete data. In this +work, we explore whether large language models can help us fill such holes to +improve offline evaluations. We examine an extreme, albeit common, evaluation +setting wherein only a single known relevant document per query is available +for evaluation. We then explore various approaches for predicting the relevance +of unjudged documents with respect to a query and the known relevant document, +including nearest neighbor, supervised, and prompting techniques. We find that +although the predictions of these One-Shot Labelers (1SL) frequently disagree +with human assessments, the labels they produce yield a far more reliable +ranking of systems than the single labels do alone. Specifically, the strongest +approaches can consistently reach system ranking correlations of over 0.86 with +the full rankings over a variety of measures. Meanwhile, the approach +substantially increases the reliability of t-tests due to filling holes in +relevance assessments, giving researchers more confidence in results they find +to be significant. Alongside this work, we release an easy-to-use software +package to enable the use of 1SL for evaluation of other ad-hoc collections or +systems. +" +Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding,Yuqing Wang,http://arxiv.org/pdf/2304.05368v3.pdf,2023-04-09,"['cs.cl', 'cs.ai']",2304.05368v3.pdf," Large language models (LLMs) have made significant progress in various +domains, including healthcare. However, the specialized nature of clinical +language understanding tasks presents unique challenges and limitations that +warrant further investigation. In this study, we conduct a comprehensive +evaluation of state-of-the-art LLMs, namely GPT-3.5, GPT-4, and Bard, within +the realm of clinical language understanding tasks. These tasks span a diverse +range, including named entity recognition, relation extraction, natural +language inference, semantic textual similarity, document classification, and +question-answering. We also introduce a novel prompting strategy, +self-questioning prompting (SQP), tailored to enhance LLMs' performance by +eliciting informative questions and answers pertinent to the clinical scenarios +at hand. Our evaluation underscores the significance of task-specific learning +strategies and prompting techniques for improving LLMs' effectiveness in +healthcare-related tasks. Additionally, our in-depth error analysis on the +challenging relation extraction task offers valuable insights into error +distribution and potential avenues for improvement using SQP. Our study sheds +light on the practical implications of employing LLMs in the specialized domain +of healthcare, serving as a foundation for future research and the development +of potential applications in healthcare settings. +" +Multi-Prompt with Depth Partitioned Cross-Modal Learning,Yingjie Tian,http://arxiv.org/pdf/2305.06221v3.pdf,2023-05-10,"['cs.cv', 'cs.ai']",2305.06221v3.pdf," In recent years, soft prompt learning methods have been proposed to fine-tune +large-scale vision-language pre-trained models for various downstream tasks. +These methods typically combine learnable textual tokens with class tokens as +input for models with frozen parameters. However, they often employ a single +prompt to describe class contexts, failing to capture categories' diverse +attributes adequately. This study introduces the Partitioned Multi-modal Prompt +(PMPO), a multi-modal prompting technique that extends the soft prompt from a +single learnable prompt to multiple prompts. Our method divides the visual +encoder depths and connects learnable prompts to the separated visual depths, +enabling different prompts to capture the hierarchical contextual depths of +visual representations. Furthermore, to maximize the advantages of multi-prompt +learning, we incorporate prior information from manually designed templates and +learnable multi-prompts, thus improving the generalization capabilities of our +approach. We evaluate the effectiveness of our approach on three challenging +tasks: new class generalization, cross-dataset evaluation, and domain +generalization. For instance, our method achieves a $79.28$ harmonic mean, +averaged over 11 diverse image recognition datasets ($+7.62$ compared to CoOp), +demonstrating significant competitiveness compared to state-of-the-art +prompting methods. +" +ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models,Qijiong Liu,http://arxiv.org/pdf/2305.06566v4.pdf,2023-05-11,"['cs.ir', 'cs.cl']",2305.06566v4.pdf," Personalized content-based recommender systems have become indispensable +tools for users to navigate through the vast amount of content available on +platforms like daily news websites and book recommendation services. However, +existing recommenders face significant challenges in understanding the content +of items. Large language models (LLMs), which possess deep semantic +comprehension and extensive knowledge from pretraining, have proven to be +effective in various natural language processing tasks. In this study, we +explore the potential of leveraging both open- and closed-source LLMs to +enhance content-based recommendation. With open-source LLMs, we utilize their +deep layers as content encoders, enriching the representation of content at the +embedding level. For closed-source LLMs, we employ prompting techniques to +enrich the training data at the token level. Through comprehensive experiments, +we demonstrate the high effectiveness of both types of LLMs and show the +synergistic relationship between them. Notably, we observed a significant +relative improvement of up to 19.32% compared to existing state-of-the-art +recommendation models. These findings highlight the immense potential of both +open- and closed-source of LLMs in enhancing content-based recommendation +systems. We will make our code and LLM-generated data available for other +researchers to reproduce our results. +" +OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models,Badr AlKhamissi,http://arxiv.org/pdf/2305.12001v2.pdf,2023-05-19,['cs.cl'],2305.12001v2.pdf," In this paper, we conduct a thorough investigation into the reasoning +capabilities of Large Language Models (LLMs), focusing specifically on the Open +Pretrained Transformers (OPT) models as a representative of such models. Our +study entails finetuning three different sizes of OPT on a carefully curated +reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned +without explanations, and OPT-RE, finetuned with explanations. We then evaluate +all models on 57 out-of-domain tasks drawn from the SUPER-NATURALINSTRUCTIONS +benchmark, covering 26 distinct reasoning skills, utilizing three prompting +techniques. Through a comprehensive grid of 27 configurations and 6,156 test +evaluations, we investigate the dimensions of finetuning, prompting, and scale +to understand the role of explanations on different reasoning skills. Our +findings reveal that having explanations in the fewshot exemplar has no +significant impact on the model's performance when the model is finetuned, +while positively affecting the non-finetuned counterpart. Moreover, we observe +a slight yet consistent increase in classification accuracy as we incorporate +explanations during prompting and finetuning, respectively. Finally, we offer +insights on which skills benefit the most from incorporating explanations +during finetuning and prompting, such as Numerical (+20.4%) and Analogical +(+13.9%) reasoning, as well as skills that exhibit negligible or negative +effects. +" +The Utility of Large Language Models and Generative AI for Education Research,Andrew Katz,http://arxiv.org/pdf/2305.18125v1.pdf,2023-05-29,['cs.hc'],2305.18125v1.pdf," The use of natural language processing (NLP) techniques in engineering +education can provide valuable insights into the underlying processes involved +in generating text. While accessing these insights can be labor-intensive if +done manually, recent advances in NLP and large language models have made it a +realistic option for individuals. This study explores and evaluates a +combination of clustering, summarization, and prompting techniques to analyze +over 1,000 student essays in which students discussed their career interests. +The specific assignment prompted students to define and explain their career +goals as engineers. Using text embedding representations of student responses, +we clustered the responses together to identify thematically similar statements +from students. The clustered responses were then summarized to quickly identify +career interest themes. We also used a set of a priori codes about career +satisfaction and sectors to demonstrate an alternative approach to using these +generative text models to analyze student writing. The results of this study +demonstrate the feasibility and usefulness of NLP techniques in engineering +education research. By automating the initial analysis of student essays, +researchers and educators can more efficiently and accurately identify key +themes and patterns in student writing. The methods presented in this paper +have broader applications for engineering education and research purposes +beyond analyzing student essays. By explaining these methods to the engineering +education community, readers can utilize them in their own contexts. +" +Fine-Grained Visual Prompting,Lingfeng Yang,http://arxiv.org/pdf/2306.04356v1.pdf,2023-06-07,['cs.cv'],2306.04356v1.pdf," Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive +zero-shot transfer capabilities in image-level visual perception. However, +these models have shown limited performance in instance-level tasks that demand +precise localization and recognition. Previous works have suggested that +incorporating visual prompts, such as colorful boxes or circles, can improve +the ability of models to recognize objects of interest. Nonetheless, compared +to language prompting, visual prompting designs are rarely explored. Existing +approaches, which employ coarse visual cues such as colorful boxes or circles, +often result in sub-optimal performance due to the inclusion of irrelevant and +noisy pixels. In this paper, we carefully study the visual prompting designs by +exploring more fine-grained markings, such as segmentation masks and their +variations. In addition, we introduce a new zero-shot framework that leverages +pixel-level annotations acquired from a generalist segmentation model for +fine-grained visual prompting. Consequently, our investigation reveals that a +straightforward application of blur outside the target mask, referred to as the +Blur Reverse Mask, exhibits exceptional effectiveness. This proposed prompting +strategy leverages the precise mask annotations to reduce focus on weakly +related regions while retaining spatial coherence between the target and the +surrounding background. Our Fine-Grained Visual Prompting (FGVP) demonstrates +superior performance in zero-shot comprehension of referring expressions on the +RefCOCO, RefCOCO+, and RefCOCOg benchmarks. It outperforms prior methods by an +average margin of 3.0% to 4.6%, with a maximum improvement of 12.5% on the +RefCOCO+ testA subset. The part detection experiments conducted on the PACO +dataset further validate the preponderance of FGVP over existing visual +prompting techniques. Code and models will be made available. +" +The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification,Norbert Tihanyi,http://arxiv.org/pdf/2307.02192v2.pdf,2023-07-05,"['cs.db', 'cs.ai']",2307.02192v2.pdf," This paper presents the FormAI dataset, a large collection of 112, 000 +AI-generated compilable and independent C programs with vulnerability +classification. We introduce a dynamic zero-shot prompting technique +constructed to spawn diverse programs utilizing Large Language Models (LLMs). +The dataset is generated by GPT-3.5-turbo and comprises programs with varying +levels of complexity. Some programs handle complicated tasks like network +management, table games, or encryption, while others deal with simpler tasks +like string manipulation. Every program is labeled with the vulnerabilities +found within the source code, indicating the type, line number, and vulnerable +function name. This is accomplished by employing a formal verification method +using the Efficient SMT-based Bounded Model Checker (ESBMC), which uses model +checking, abstract interpretation, constraint programming, and satisfiability +modulo theories to reason over safety/security properties in programs. This +approach definitively detects vulnerabilities and offers a formal model known +as a counterexample, thus eliminating the possibility of generating false +positive reports. We have associated the identified vulnerabilities with Common +Weakness Enumeration (CWE) numbers. We make the source code available for the +112, 000 programs, accompanied by a separate file containing the +vulnerabilities detected in each program, making the dataset ideal for training +LLMs and machine learning algorithms. Our study unveiled that according to +ESBMC, 51.24% of the programs generated by GPT-3.5 contained vulnerabilities, +thereby presenting considerable risks to software safety and security. +" +SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs,Shengzhi Li,http://arxiv.org/pdf/2308.03349v1.pdf,2023-08-07,"['cs.cl', 'cs.ai', 'cs.cv']",2308.03349v1.pdf," In this work, we present SciGraphQA, a synthetic multi-turn question-answer +dataset related to academic graphs. SciGraphQA is 13 times larger than +ChartVQA, the previously largest chart-visual question-answering dataset. It is +also the largest open-sourced chart VQA dataset with non-synthetic charts. To +build our dataset, we selected 290,000 Computer Science or Machine Learning +ArXiv papers published between 2010 and 2020, and then used Palm-2 to generate +295K samples of open-vocabulary multi-turn question-answering dialogues about +the graphs. As context, we provided the text-only Palm-2 with paper title, +abstract, paragraph mentioning the graph, and rich text contextual data from +the graph itself, obtaining dialogues with an average 2.23 question-answer +turns for each graph. We asked GPT-4 to assess the matching quality of our +question-answer turns given the paper's context, obtaining an average rating of +8.7/10 on our 3K test set. We evaluated the 0-shot capability of the most +popular MLLM models such as LLaVa, mPLUGowl, BLIP-2, and openFlamingo's on our +dataset, finding LLaVA-13B being the most performant with a CIDEr score of +0.08. We further enriched the question prompts for LLAVA by including the +serialized data tables extracted from the graphs using the DePlot model, +boosting LLaVA's 0-shot CIDEr to 0.15. To verify the validity of our dataset, +we also fine-tuned LLaVa using our dataset, reaching a substantially higher +CIDEr score of 0.26. We anticipate further accuracy improvement by including +segmentation mask tokens and leveraging larger LLM backbones coupled with +emergent prompting techniques. Our code and data are open-sourced. +" +GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised Learning,Mainak Singha,http://arxiv.org/pdf/2308.11605v1.pdf,2023-08-22,['cs.cv'],2308.11605v1.pdf," Large-scale foundation models, such as CLIP, have demonstrated remarkable +success in visual recognition tasks by embedding images in a semantically rich +space. Self-supervised learning (SSL) has also shown promise in improving +visual recognition by learning invariant features. However, the combination of +CLIP with SSL is found to face challenges due to the multi-task framework that +blends CLIP's contrastive loss and SSL's loss, including difficulties with loss +weighting and inconsistency among different views of images in CLIP's output +space. To overcome these challenges, we propose a prompt learning-based model +called GOPro, which is a unified framework that ensures similarity between +various augmented views of input images in a shared image-text embedding space, +using a pair of learnable image and text projectors atop CLIP, to promote +invariance and generalizability. To automatically learn such prompts, we +leverage the visual content and style primitives extracted from pre-trained +CLIP and adapt them to the target task. In addition to CLIP's cross-domain +contrastive loss, we introduce a visual contrastive loss and a novel prompt +consistency loss, considering the different views of the images. GOPro is +trained end-to-end on all three loss objectives, combining the strengths of +CLIP and SSL in a principled manner. Empirical evaluations demonstrate that +GOPro outperforms the state-of-the-art prompting techniques on three +challenging domain generalization tasks across multiple benchmarks by a +significant margin. Our code is available at +https://github.com/mainaksingha01/GOPro. +" +Spoken Language Intelligence of Large Language Models for Language Learning,Linkai Peng,http://arxiv.org/pdf/2308.14536v1.pdf,2023-08-28,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as']",2308.14536v1.pdf," People have long hoped for a conversational system that can assist in +real-life situations, and recent progress on large language models (LLMs) is +bringing this idea closer to reality. While LLMs are often impressive in +performance, their efficacy in real-world scenarios that demand expert +knowledge remains unclear. LLMs are believed to hold the most potential and +value in education, especially in the development of Artificial intelligence +(AI) based virtual teachers capable of facilitating language learning. Our +focus is centered on evaluating the efficacy of LLMs in the realm of education, +specifically in the areas of spoken language learning which encompass +phonetics, phonology, and second language acquisition. We introduce a new +multiple-choice question dataset to evaluate the effectiveness of LLMs in the +aforementioned scenarios, including understanding and application of spoken +language knowledge. In addition, we investigate the influence of various +prompting techniques such as zero- and few-shot method (prepending the question +with question-answer exemplars), chain-of-thought (CoT, think step-by-step), +in-domain exampler and external tools (Google, Wikipedia). We conducted +large-scale evaluation on popular LLMs (20 distinct models) using these +methods. We achieved significant performance improvements compared to the +zero-shot baseline in the practical questions reasoning (GPT-3.5, 49.1% -> +63.1%; LLaMA2-70B-Chat, 42.2% -> 48.6%). We found that models of different +sizes have good understanding of concepts in phonetics, phonology, and second +language acquisition, but show limitations in reasoning for real-world +problems. Additionally, we also explore preliminary findings on conversational +communication. +" +Are Emergent Abilities in Large Language Models just In-Context Learning?,Sheng Lu,http://arxiv.org/pdf/2309.01809v1.pdf,2023-09-04,['cs.cl'],2309.01809v1.pdf," Large language models have exhibited emergent abilities, demonstrating +exceptional performance across diverse tasks for which they were not explicitly +trained, including those that require complex reasoning abilities. The +emergence of such abilities carries profound implications for the future +direction of research in NLP, especially as the deployment of such models +becomes more prevalent. However, one key challenge is that the evaluation of +these abilities is often confounded by competencies that arise in models +through alternative prompting techniques, such as in-context learning and +instruction following, which also emerge as the models are scaled up. In this +study, we provide the first comprehensive examination of these emergent +abilities while accounting for various potentially biasing factors that can +influence the evaluation of models. We conduct rigorous tests on a set of 18 +models, encompassing a parameter range from 60 million to 175 billion +parameters, across a comprehensive set of 22 tasks. Through an extensive series +of over 1,000 experiments, we provide compelling evidence that emergent +abilities can primarily be ascribed to in-context learning. We find no evidence +for the emergence of reasoning abilities, thus providing valuable insights into +the underlying mechanisms driving the observed abilities and thus alleviating +safety concerns regarding their use. +" +Unsupervised Contrast-Consistent Ranking with Language Models,Niklas Stoehr,http://arxiv.org/pdf/2309.06991v1.pdf,2023-09-13,"['cs.lg', 'cs.cl', 'stat.ml']",2309.06991v1.pdf," Language models contain ranking-based knowledge and are powerful solvers of +in-context ranking tasks. For instance, they may have parametric knowledge +about the ordering of countries by size or may be able to rank reviews by +sentiment. Recent work focuses on pairwise, pointwise, and listwise prompting +techniques to elicit a language model's ranking knowledge. However, we find +that even with careful calibration and constrained decoding, prompting-based +techniques may not always be self-consistent in the rankings they produce. This +motivates us to explore an alternative approach that is inspired by an +unsupervised probing method called Contrast-Consistent Search (CCS). The idea +is to train a probing model guided by a logical constraint: a model's +representation of a statement and its negation must be mapped to contrastive +true-false poles consistently across multiple statements. We hypothesize that +similar constraints apply to ranking tasks where all items are related via +consistent pairwise or listwise comparisons. To this end, we extend the binary +CCS method to Contrast-Consistent Ranking (CCR) by adapting existing ranking +methods such as the Max-Margin Loss, Triplet Loss, and Ordinal Regression +objective. Our results confirm that, for the same language model, CCR probing +outperforms prompting and even performs on a par with prompting much larger +language models. +" +S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs,Sarkar Snigdha Sarathi Das,http://arxiv.org/pdf/2309.08827v1.pdf,2023-09-16,"['cs.cl', 'cs.ai']",2309.08827v1.pdf," The traditional Dialogue State Tracking (DST) problem aims to track user +preferences and intents in user-agent conversations. While sufficient for +task-oriented dialogue systems supporting narrow domain applications, the +advent of Large Language Model (LLM)-based chat systems has introduced many +real-world intricacies in open-domain dialogues. These intricacies manifest in +the form of increased complexity in contextual interactions, extended dialogue +sessions encompassing a diverse array of topics, and more frequent contextual +shifts. To handle these intricacies arising from evolving LLM-based chat +systems, we propose joint dialogue segmentation and state tracking per segment +in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a +true open-domain dialogue system, we propose S3-DST, a structured prompting +technique that harnesses Pre-Analytical Recollection, a novel grounding +mechanism we designed for improving long context tracking. To demonstrate the +efficacy of our proposed approach in joint segmentation and state tracking, we +evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as +well as publicly available DST and segmentation datasets. Across all datasets +and settings, S3-DST consistently outperforms the state-of-the-art, +demonstrating its potency and robustness the next generation of LLM-based chat +systems. +" +Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?,Yongchao Chen,http://arxiv.org/pdf/2309.15943v1.pdf,2023-09-27,['cs.ro'],2309.15943v1.pdf," A flurry of recent work has demonstrated that pre-trained large language +models (LLMs) can be effective task planners for a variety of single-robot +tasks. The planning performance of LLMs is significantly improved via prompting +techniques, such as in-context learning or re-prompting with state feedback, +placing new importance on the token budget for the context window. An +under-explored but natural next direction is to investigate LLMs as multi-robot +task planners. However, long-horizon, heterogeneous multi-robot planning +introduces new challenges of coordination while also pushing up against the +limits of context window length. It is therefore critical to find +token-efficient LLM planning frameworks that are also able to reason about the +complexities of multi-robot coordination. In this work, we compare the task +success rate and token efficiency of four multi-agent communication frameworks +(centralized, decentralized, and two hybrid) as applied to four +coordination-dependent multi-agent 2D task scenarios for increasing numbers of +agents. We find that a hybrid framework achieves better task success rates +across all four tasks and scales better to more agents. We further demonstrate +the hybrid frameworks in 3D simulations where the vision-to-text problem and +dynamical errors are considered. See our project website +https://yongchao98.github.io/MIT-REALM-Multi-Robot/ for prompts, videos, and +code. +" +Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model Reasoning,Jianpeng Zhou,http://arxiv.org/pdf/2310.01446v1.pdf,2023-10-01,"['cs.cl', 'cs.ai']",2310.01446v1.pdf," Large Language Models (LLMs) are showcasing impressive ability in handling +complex reasoning tasks. In real-world situations, problems often span a +spectrum of complexities. Humans inherently adjust their problem-solving +approaches based on task complexity. However, most methodologies that leverage +LLMs tend to adopt a uniform approach: utilizing consistent models, prompting +methods, and degrees of problem decomposition, regardless of the problem +complexity. Inflexibility of them can bring unnecessary computational overhead +or sub-optimal performance. To address this problem, we introduce an +Adaptive-Solver framework. It strategically modulates solving strategies based +on the difficulties of the problems. Given an initial solution, the framework +functions with two primary modules. The initial evaluation module assesses the +adequacy of the current solution. If improvements are needed, the subsequent +adaptation module comes into play. Within this module, three key adaptation +strategies are employed: (1) Model Adaptation: Switching to a stronger LLM when +a weaker variant is inadequate. (2) Prompting Method Adaptation: Alternating +between different prompting techniques to suit the problem's nuances. (3) +Decomposition Granularity Adaptation: Breaking down a complex problem into more +fine-grained sub-questions to enhance solvability. Through such dynamic +adaptations, our framework not only enhances computational efficiency but also +elevates the overall performance. This dual-benefit ensures both the efficiency +of the system for simpler tasks and the precision required for more complex +questions. Experimental results from complex reasoning tasks reveal that the +prompting method adaptation and decomposition granularity adaptation enhance +performance across all tasks. Furthermore, the model adaptation approach +significantly reduces API costs (up to 50%) while maintaining superior +performance. +" +Revisiting Large Language Models as Zero-shot Relation Extractors,Guozheng Li,http://arxiv.org/pdf/2310.05028v3.pdf,2023-10-08,"['cs.ai', 'cs.cl']",2310.05028v3.pdf," Relation extraction (RE) consistently involves a certain degree of labeled or +unlabeled data even if under zero-shot setting. Recent studies have shown that +large language models (LLMs) transfer well to new tasks out-of-the-box simply +given a natural language prompt, which provides the possibility of extracting +relations from text without any data and parameter tuning. This work focuses on +the study of exploring LLMs, such as ChatGPT, as zero-shot relation extractors. +On the one hand, we analyze the drawbacks of existing RE prompts and attempt to +incorporate recent prompt techniques such as chain-of-thought (CoT) to improve +zero-shot RE. We propose the summarize-and-ask (\textsc{SumAsk}) prompting, a +simple prompt recursively using LLMs to transform RE inputs to the effective +question answering (QA) format. On the other hand, we conduct comprehensive +experiments on various benchmarks and settings to investigate the capabilities +of LLMs on zero-shot RE. Specifically, we have the following findings: (i) +\textsc{SumAsk} consistently and significantly improves LLMs performance on +different model sizes, benchmarks and settings; (ii) Zero-shot prompting with +ChatGPT achieves competitive or superior results compared with zero-shot and +fully supervised methods; (iii) LLMs deliver promising performance in +extracting overlapping relations; (iv) The performance varies greatly regarding +different relations. Different from small language models, LLMs are effective +in handling challenge none-of-the-above (NoTA) relation. +" +Towards Training-free Open-world Segmentation via Image Prompting Foundation Models,Lv Tang,http://arxiv.org/pdf/2310.10912v1.pdf,2023-10-17,['cs.cv'],2310.10912v1.pdf," The realm of computer vision has witnessed a paradigm shift with the advent +of foundational models, mirroring the transformative influence of large +language models in the domain of natural language processing. This paper delves +into the exploration of open-world segmentation, presenting a novel approach +called Image Prompt Segmentation (IPSeg) that harnesses the power of vision +foundational models. At the heart of IPSeg lies the principle of a +training-free paradigm, which capitalizes on image prompting techniques. IPSeg +utilizes a single image containing a subjective visual concept as a flexible +prompt to query vision foundation models like DINOv2 and Stable Diffusion. Our +approach extracts robust features for the prompt image and input image, then +matches the input representations to the prompt representations via a novel +feature interaction module to generate point prompts highlighting target +objects in the input image. The generated point prompts are further utilized to +guide the Segment Anything Model to segment the target object in the input +image. The proposed method stands out by eliminating the need for exhaustive +training sessions, thereby offering a more efficient and scalable solution. +Experiments on COCO, PASCAL VOC, and other datasets demonstrate IPSeg's +efficacy for flexible open-world segmentation using intuitive image prompts. +This work pioneers tapping foundation models for open-world understanding +through visual concepts conveyed in images. +" +Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages,Libo Qin,http://arxiv.org/pdf/2310.14799v1.pdf,2023-10-23,"['cs.cl', 'cs.ai']",2310.14799v1.pdf," Chain-of-thought (CoT) is capable of eliciting models to explicitly generate +reasoning paths, thus promoting reasoning accuracy and attracting increasing +attention. Specifically, zero-shot CoT achieves remarkable improvements in a +wide range of reasoning tasks by simply instructing the LLM with the prompt +""Let's think step by step!"". Despite the success of zero-shot CoT, the existing +zero-shot prompting techniques remain limited to a single language, making it +challenging to generalize to other languages and hindering global development. +In this work, we introduce cross-lingual prompting (CLP), aiming to improve +zero-shot CoT reasoning across languages. Specifically, CLP consists of two +main components: (1) cross-lingual alignment prompting and (2) task-specific +solver prompting. The cross-lingual alignment prompting is responsible for +aligning representations across different languages, whereas the task-specific +solver prompting is used to generate the final chain of thoughts and results +for the reasoning task. In addition, we further introduce cross-lingual +self-consistent prompting (CLSP) to ensemble different reasoning paths across +languages. Our experimental evaluations on several benchmarks demonstrate that +CLP and CLSP significantly outperform the existing prompting methods and +achieve state-of-the-art performance. We hope this work will inspire further +breakthroughs in cross-lingual CoT. +" +HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks,Yihong Ma,http://arxiv.org/pdf/2310.15318v1.pdf,2023-10-23,"['cs.lg', 'cs.ai']",2310.15318v1.pdf," Graphs have emerged as a natural choice to represent and analyze the +intricate patterns and rich information of the Web, enabling applications such +as online page classification and social recommendation. The prevailing +""pre-train, fine-tune"" paradigm has been widely adopted in graph machine +learning tasks, particularly in scenarios with limited labeled nodes. However, +this approach often exhibits a misalignment between the training objectives of +pretext tasks and those of downstream tasks. This gap can result in the +""negative transfer"" problem, wherein the knowledge gained from pre-training +adversely affects performance in the downstream tasks. The surge in +prompt-based learning within Natural Language Processing (NLP) suggests the +potential of adapting a ""pre-train, prompt"" paradigm to graphs as an +alternative. However, existing graph prompting techniques are tailored to +homogeneous graphs, neglecting the inherent heterogeneity of Web graphs. To +bridge this gap, we propose HetGPT, a general post-training prompting framework +to improve the predictive performance of pre-trained heterogeneous graph neural +networks (HGNNs). The key is the design of a novel prompting function that +integrates a virtual class prompt and a heterogeneous feature prompt, with the +aim to reformulate downstream tasks to mirror pretext tasks. Moreover, HetGPT +introduces a multi-view neighborhood aggregation mechanism, capturing the +complex neighborhood structure in heterogeneous graphs. Extensive experiments +on three benchmark datasets demonstrate HetGPT's capability to enhance the +performance of state-of-the-art HGNNs on semi-supervised node classification. +" +Videoprompter: an ensemble of foundational models for zero-shot video understanding,Adeel Yousaf,http://arxiv.org/pdf/2310.15324v1.pdf,2023-10-23,['cs.cv'],2310.15324v1.pdf," Vision-language models (VLMs) classify the query video by calculating a +similarity score between the visual features and text-based class label +representations. Recently, large language models (LLMs) have been used to +enrich the text-based class labels by enhancing the descriptiveness of the +class names. However, these improvements are restricted to the text-based +classifier only, and the query visual features are not considered. In this +paper, we propose a framework which combines pre-trained discriminative VLMs +with pre-trained generative video-to-text and text-to-text models. We introduce +two key modifications to the standard zero-shot setting. First, we propose +language-guided visual feature enhancement and employ a video-to-text model to +convert the query video to its descriptive form. The resulting descriptions +contain vital visual cues of the query video, such as what objects are present +and their spatio-temporal interactions. These descriptive cues provide +additional semantic knowledge to VLMs to enhance their zeroshot performance. +Second, we propose video-specific prompts to LLMs to generate more meaningful +descriptions to enrich class label representations. Specifically, we introduce +prompt techniques to create a Tree Hierarchy of Categories for class names, +offering a higher-level action context for additional visual cues, We +demonstrate the effectiveness of our approach in video understanding across +three different zero-shot settings: 1) video action recognition, 2) +video-to-text and textto-video retrieval, and 3) time-sensitive video tasks. +Consistent improvements across multiple benchmarks and with various VLMs +demonstrate the effectiveness of our proposed framework. Our code will be made +publicly available. +" +Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting,Preethi Lahoti,http://arxiv.org/pdf/2310.16523v1.pdf,2023-10-25,"['cs.cl', 'cs.ai']",2310.16523v1.pdf," A crucial challenge for generative large language models (LLMs) is diversity: +when a user's prompt is under-specified, models may follow implicit assumptions +while generating a response, which may result in homogenization of the +responses, as well as certain demographic groups being under-represented or +even erased from the generated responses. In this paper, we formalize diversity +of representation in generative LLMs. We present evaluation datasets and +propose metrics to measure diversity in generated responses along people and +culture axes. We find that LLMs understand the notion of diversity, and that +they can reason and critique their own responses for that goal. This finding +motivated a new prompting technique called collective-critique and self-voting +(CCSV) to self-improve people diversity of LLMs by tapping into its diversity +reasoning capabilities, without relying on handcrafted examples or prompt +tuning. Extensive empirical experiments with both human and automated +evaluations show that our proposed approach is effective at improving people +and culture diversity, and outperforms all baseline methods by a large margin. +" +LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?,Zeyang Zhang,http://arxiv.org/pdf/2310.17110v1.pdf,2023-10-26,['cs.lg'],2310.17110v1.pdf," In an era marked by the increasing adoption of Large Language Models (LLMs) +for various tasks, there is a growing focus on exploring LLMs' capabilities in +handling web data, particularly graph data. Dynamic graphs, which capture +temporal network evolution patterns, are ubiquitous in real-world web data. +Evaluating LLMs' competence in understanding spatial-temporal information on +dynamic graphs is essential for their adoption in web applications, which +remains unexplored in the literature. In this paper, we bridge the gap via +proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic +graphs, to the best of our knowledge, for the first time. Specifically, we +propose the LLM4DyG benchmark, which includes nine specially designed tasks +considering the capability evaluation of LLMs from both temporal and spatial +dimensions. Then, we conduct extensive experiments to analyze the impacts of +different data generators, data statistics, prompting techniques, and LLMs on +the model performance. Finally, we propose Disentangled Spatial-Temporal +Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal +understanding abilities. Our main observations are: 1) LLMs have preliminary +spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph +tasks show increasing difficulties for LLMs as the graph size and density +increase, while not sensitive to the time span and data generation mechanism, +3) the proposed DST2 prompting method can help to improve LLMs' +spatial-temporal understanding abilities on dynamic graphs for most tasks. The +data and codes will be open-sourced at publication time. +" +Which is better? Exploring Prompting Strategy For LLM-based Metrics,Joonghoon Kim,http://arxiv.org/pdf/2311.03754v1.pdf,2023-11-07,['cs.cl'],2311.03754v1.pdf," This paper describes the DSBA submissions to the Prompting Large Language +Models as Explainable Metrics shared task, where systems were submitted to two +tracks: small and large summarization tracks. With advanced Large Language +Models (LLMs) such as GPT-4, evaluating the quality of Natural Language +Generation (NLG) has become increasingly paramount. Traditional +similarity-based metrics such as BLEU and ROUGE have shown to misalign with +human evaluation and are ill-suited for open-ended generation tasks. To address +this issue, we explore the potential capability of LLM-based metrics, +especially leveraging open-source LLMs. In this study, wide range of prompts +and prompting techniques are systematically analyzed with three approaches: +prompting strategy, score aggregation, and explainability. Our research focuses +on formulating effective prompt templates, determining the granularity of NLG +quality scores and assessing the impact of in-context examples on LLM-based +evaluation. Furthermore, three aggregation strategies are compared to identify +the most reliable method for aggregating NLG quality scores. To examine +explainability, we devise a strategy that generates rationales for the scores +and analyzes the characteristics of the explanation produced by the open-source +LLMs. Extensive experiments provide insights regarding evaluation capabilities +of open-source LLMs and suggest effective prompting strategies. +" +Understanding and Improving Visual Prompting: A Label-Mapping Perspective,Aochuan Chen,http://arxiv.org/pdf/2211.11635v5.pdf,2022-11-21,['cs.cv'],2211.11635v5.pdf," We revisit and advance visual prompting (VP), an input prompting technique +for vision tasks. VP can reprogram a fixed, pre-trained source model to +accomplish downstream tasks in the target domain by simply incorporating +universal prompts (in terms of input perturbation patterns) into downstream +data points. Yet, it remains elusive why VP stays effective even given a +ruleless label mapping (LM) between the source classes and the target classes. +Inspired by the above, we ask: How is LM interrelated with VP? And how to +exploit such a relationship to improve its accuracy on target tasks? We peer +into the influence of LM on VP and provide an affirmative answer that a better +'quality' of LM (assessed by mapping precision and explanation) can +consistently improve the effectiveness of VP. This is in contrast to the prior +art where the factor of LM was missing. To optimize LM, we propose a new VP +framework, termed ILM-VP (iterative label mapping-based visual prompting), +which automatically re-maps the source labels to the target labels and +progressively improves the target task accuracy of VP. Further, when using a +contrastive language-image pretrained (CLIP) model, we propose to integrate an +LM process to assist the text prompt selection of CLIP and to improve the +target task accuracy. Extensive experiments demonstrate that our proposal +significantly outperforms state-of-the-art VP methods. As highlighted below, we +show that when reprogramming an ImageNet-pretrained ResNet-18 to 13 target +tasks, our method outperforms baselines by a substantial margin, e.g., 7.9% and +6.7% accuracy improvements in transfer learning to the target Flowers102 and +CIFAR100 datasets. Besides, our proposal on CLIP-based VP provides 13.7% and +7.1% accuracy improvements on Flowers102 and DTD respectively. Our code is +available at https://github.com/OPTML-Group/ILM-VP. +" +The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms,Yuyang Du,http://arxiv.org/pdf/2307.07319v4.pdf,2023-07-14,['eess.sp'],2307.07319v4.pdf," Large language models (LLMs) have garnered significant attention across +various research disciplines, including the wireless communication community. +There have been several heated discussions on the intersection of LLMs and +wireless technologies. While recent studies have demonstrated the ability of +LLMs to generate hardware description language (HDL) code for simple +computation tasks, developing wireless prototypes and products via HDL poses +far greater challenges because of the more complex computation tasks involved. +In this paper, we aim to address this challenge by investigating the role of +LLMs in FPGA-based hardware development for advanced wireless signal +processing. We begin by exploring LLM-assisted code refactoring, reuse, and +validation, using an open-source software-defined radio (SDR) project as a case +study. Through the case study, we find that an LLM assistant can potentially +yield substantial productivity gains for researchers and developers. We then +examine the feasibility of using LLMs to generate HDL code for advanced +wireless signal processing, using the Fast Fourier Transform (FFT) algorithm as +an example. This task presents two unique challenges: the scheduling of +subtasks within the overall task and the multi-step thinking required to solve +certain arithmetic problem within the task. To address these challenges, we +employ in-context learning (ICL) and Chain-of-Thought (CoT) prompting +techniques, culminating in the successful generation of a 64-point Verilog FFT +module. Our results demonstrate the potential of LLMs for generalization and +imitation, affirming their usefulness in writing HDL code for wireless +communication systems. Overall, this work contributes to understanding the role +of LLMs in wireless communication and motivates further exploration of their +capabilities. +" +Foundation Metrics: Quantifying Effectiveness of Healthcare Conversations powered by Generative AI,Mahyar Abbasian,http://arxiv.org/pdf/2309.12444v2.pdf,2023-09-21,['cs.cl'],2309.12444v2.pdf," Generative Artificial Intelligence is set to revolutionize healthcare +delivery by transforming traditional patient care into a more personalized, +efficient, and proactive process. Chatbots, serving as interactive +conversational models, will probably drive this patient-centered transformation +in healthcare. Through the provision of various services, including diagnosis, +personalized lifestyle recommendations, and mental health support, the +objective is to substantially augment patient health outcomes, all the while +mitigating the workload burden on healthcare providers. The life-critical +nature of healthcare applications necessitates establishing a unified and +comprehensive set of evaluation metrics for conversational models. Existing +evaluation metrics proposed for various generic large language models (LLMs) +demonstrate a lack of comprehension regarding medical and health concepts and +their significance in promoting patients' well-being. Moreover, these metrics +neglect pivotal user-centered aspects, including trust-building, ethics, +personalization, empathy, user comprehension, and emotional support. The +purpose of this paper is to explore state-of-the-art LLM-based evaluation +metrics that are specifically applicable to the assessment of interactive +conversational models in healthcare. Subsequently, we present an comprehensive +set of evaluation metrics designed to thoroughly assess the performance of +healthcare chatbots from an end-user perspective. These metrics encompass an +evaluation of language processing abilities, impact on real-world clinical +tasks, and effectiveness in user-interactive conversations. Finally, we engage +in a discussion concerning the challenges associated with defining and +implementing these metrics, with particular emphasis on confounding factors +such as the target audience, evaluation methods, and prompt techniques involved +in the evaluation process. +" +Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems,Aniruddha Deb,http://arxiv.org/pdf/2310.01991v1.pdf,2023-10-03,"['cs.cl', 'cs.ai', 'cs.lg', 'i.2.3']",2310.01991v1.pdf," While forward reasoning (i.e. find the answer given the question) has been +explored extensively in the recent literature, backward reasoning is relatively +unexplored. We examine the backward reasoning capabilities of LLMs on Math Word +Problems (MWPs): given a mathematical question and its answer, with some +details omitted from the question, can LLMs effectively retrieve the missing +information? + In this paper, we formally define the backward reasoning task on math word +problems and modify three datasets to evaluate this task: GSM8k, SVAMP and +MultiArith. Our findings show a significant drop in the accuracy of models on +backward reasoning compared to forward reasoning across four SOTA LLMs (GPT4, +GPT3.5, PaLM-2, and LLaMa-2). Utilizing the specific format of this task, we +propose three novel techniques that improve performance: Rephrase reformulates +the given problem into a forward reasoning problem, PAL-Tools combines the idea +of Program-Aided LLMs to produce a set of equations that can be solved by an +external solver, and Check your Work exploits the availability of natural +verifier of high accuracy in the forward direction, interleaving solving and +verification steps. Finally, realizing that each of our base methods correctly +solves a different set of problems, we propose a novel Bayesian formulation for +creating an ensemble over these base methods aided by a verifier to further +boost the accuracy by a significant margin. Extensive experimentation +demonstrates that our techniques successively improve the performance of LLMs +on the backward reasoning task, with the final ensemble-based method resulting +in a substantial performance gain compared to the raw LLMs with standard +prompting techniques such as chain-of-thought. +" +Autonomous Tree-search Ability of Large Language Models,Zheyu Zhang,http://arxiv.org/pdf/2310.10686v1.pdf,2023-10-14,"['cs.cl', 'cs.ai']",2310.10686v1.pdf," Large Language Models have excelled in remarkable reasoning capabilities with +advanced prompting techniques, but they fall short on tasks that require +exploration, strategic foresight, and sequential decision-making. Recent works +propose to utilize external programs to define search logic, such that LLMs can +perform passive tree search to solve more challenging reasoning tasks. Though +impressive results have been achieved, there are several fundamental +limitations of these approaches. First, passive tree searches are not efficient +as they usually require multiple rounds of LLM API calls to solve one single +problem. Moreover, passive search methods are not flexible since they need +task-specific program designs. Then a natural question arises: can we maintain +the tree-search capability of LLMs without the aid of external programs, and +can still generate responses that clearly demonstrate the process of a +tree-structure search? To this end, we propose a new concept called autonomous +tree-search ability of LLM, which can automatically generate a response +containing search trajectories for the correct answer. Concretely, we perform +search trajectories using capable LLM API via a fixed system prompt, allowing +them to perform autonomous tree-search (ATS) right out of the box. Experiments +on 4 puzzle games demonstrate our method can achieve huge improvements. The +ATS-BFS method outperforms the Chain of Thought approach by achieving an +average accuracy improvement of 33%. Compared to Tree of Thoughts, it requires +65.6% or 47.7% less GPT-api cost to attain a comparable level of accuracy. +Moreover, we have collected data using the ATS prompt method and fine-tuned +LLaMA. This approach yield a greater improvement compared to the ones +fine-tuned on CoT data. Specifically, it outperforms CoT-tuned LLaMAs by an +average of 40.6% and 38.5% for LLaMA2-7B and LLaMA2-13B, respectively. +" +In-Context Impersonation Reveals Large Language Models' Strengths and Biases,Leonard Salewski,http://arxiv.org/pdf/2305.14930v1.pdf,2023-05-24,"['cs.ai', 'cs.cl', 'cs.lg']",2305.14930v1.pdf," In everyday conversations, humans can take on different roles and adapt their +vocabulary to their chosen roles. We explore whether LLMs can take on, that is +impersonate, different roles when they generate text in-context. We ask LLMs to +assume different personas before solving vision and language tasks. We do this +by prefixing the prompt with a persona that is associated either with a social +identity or domain expertise. In a multi-armed bandit task, we find that LLMs +pretending to be children of different ages recover human-like developmental +stages of exploration. In a language-based reasoning task, we find that LLMs +impersonating domain experts perform better than LLMs impersonating non-domain +experts. Finally, we test whether LLMs' impersonations are complementary to +visual information when describing different categories. We find that +impersonation can improve performance: an LLM prompted to be a bird expert +describes birds better than one prompted to be a car expert. However, +impersonation can also uncover LLMs' biases: an LLM prompted to be a man +describes cars better than one prompted to be a woman. These findings +demonstrate that LLMs are capable of taking on diverse roles and that this +in-context impersonation can be used to uncover their hidden strengths and +biases. +" +ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts,Bilel Benjdira,http://arxiv.org/pdf/2308.11236v2.pdf,2023-08-22,"['cs.ro', 'cs.ai']",2308.11236v2.pdf," In this paper, we argue that the next generation of robots can be commanded +using only Language Models' prompts. Every prompt interrogates separately a +specific Robotic Modality via its Modality Language Model (MLM). A central Task +Modality mediates the whole communication to execute the robotic mission via a +Large Language Model (LLM). This paper gives this new robotic design pattern +the name of: Prompting Robotic Modalities (PRM). Moreover, this paper applies +this PRM design pattern in building a new robotic framework named +ROSGPT_Vision. ROSGPT_Vision allows the execution of a robotic task using only +two prompts: a Visual and an LLM prompt. The Visual Prompt extracts, in natural +language, the visual semantic features related to the task under consideration +(Visual Robotic Modality). Meanwhile, the LLM Prompt regulates the robotic +reaction to the visual description (Task Modality). The framework automates all +the mechanisms behind these two prompts. The framework enables the robot to +address complex real-world scenarios by processing visual data, making informed +decisions, and carrying out actions automatically. The framework comprises one +generic vision module and two independent ROS nodes. As a test application, we +used ROSGPT_Vision to develop CarMate, which monitors the driver's distraction +on the roads and makes real-time vocal notifications to the driver. We showed +how ROSGPT_Vision significantly reduced the development cost compared to +traditional methods. We demonstrated how to improve the quality of the +application by optimizing the prompting strategies, without delving into +technical details. ROSGPT_Vision is shared with the community (link: +https://github.com/bilel-bj/ROSGPT_Vision) to advance robotic research in this +direction and to build more robotic frameworks that implement the PRM design +pattern and enables controlling robots using only prompts. +" +ProgPrompt: Generating Situated Robot Task Plans using Large Language Models,Ishika Singh,http://arxiv.org/pdf/2209.11302v1.pdf,2022-09-22,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.lg']",2209.11302v1.pdf," Task planning can require defining myriad domain knowledge about the world in +which a robot needs to act. To ameliorate that effort, large language models +(LLMs) can be used to score potential next actions during task planning, and +even generate action sequences directly, given an instruction in natural +language with no additional domain information. However, such methods either +require enumerating all possible next steps for scoring, or generate free-form +text that may contain actions not possible on a given robot in its current +context. We present a programmatic LLM prompt structure that enables plan +generation functional across situated environments, robot capabilities, and +tasks. Our key insight is to prompt the LLM with program-like specifications of +the available actions and objects in an environment, as well as with example +programs that can be executed. We make concrete recommendations about prompt +structure and generation constraints through ablation experiments, demonstrate +state of the art success rates in VirtualHome household tasks, and deploy our +method on a physical robot arm for tabletop tasks. Website at +progprompt.github.io +" +Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models,Renat Aksitov,http://arxiv.org/pdf/2302.05578v2.pdf,2023-02-11,"['cs.cl', 'cs.ai']",2302.05578v2.pdf," Despite recent progress, it has been difficult to prevent semantic +hallucinations in generative Large Language Models. One common solution to this +is augmenting LLMs with a retrieval system and making sure that the generated +output is attributable to the retrieved information. Given this new added +constraint, it is plausible to expect that the overall quality of the output +will be affected, for example, in terms of fluency. Can scaling language models +help? + Here we examine the relationship between fluency and attribution in LLMs +prompted with retrieved evidence in knowledge-heavy dialog settings. Our +experiments were implemented with a set of auto-metrics that are aligned with +human preferences. They were used to evaluate a large set of generations, +produced under varying parameters of LLMs and supplied context. + We show that larger models tend to do much better in both fluency and +attribution, and that (naively) using top-k retrieval versus top-1 retrieval +improves attribution but hurts fluency. We next propose a recipe that could +allow smaller models to both close the gap with larger models and preserve the +benefits of top-k retrieval while avoiding its drawbacks. +" +Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation,Marjan Ghazvininejad,http://arxiv.org/pdf/2302.07856v1.pdf,2023-02-15,"['cs.cl', 'cs.lg']",2302.07856v1.pdf," Large language models (LLMs) demonstrate remarkable machine translation (MT) +abilities via prompting, even though they were not explicitly trained for this +task. However, even given the incredible quantities of data they are trained +on, LLMs can struggle to translate inputs with rare words, which are common in +low resource or domain transfer scenarios. We show that LLM prompting can +provide an effective solution for rare words as well, by using prior knowledge +from bilingual dictionaries to provide control hints in the prompts. We propose +a novel method, DiPMT, that provides a set of possible translations for a +subset of the input words, thereby enabling fine-grained phrase-level prompted +control of the LLM. Extensive experiments show that DiPMT outperforms the +baseline both in low-resource MT, as well as for out-of-domain MT. We further +provide a qualitative analysis of the benefits and limitations of this +approach, including the overall level of controllability that is achieved. +" +UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers,Jon Saad-Falcon,http://arxiv.org/pdf/2303.00807v3.pdf,2023-03-01,"['cs.ir', 'cs.cl']",2303.00807v3.pdf," Many information retrieval tasks require large labeled datasets for +fine-tuning. However, such datasets are often unavailable, and their utility +for real-world applications can diminish quickly due to domain shifts. To +address this challenge, we develop and motivate a method for using large +language models (LLMs) to generate large numbers of synthetic queries cheaply. +The method begins by generating a small number of synthetic queries using an +expensive LLM. After that, a much less expensive one is used to create large +numbers of synthetic queries, which are used to fine-tune a family of reranker +models. These rerankers are then distilled into a single efficient retriever +for use in the target domain. We show that this technique boosts zero-shot +accuracy in long-tail domains and achieves substantially lower latency than +standard reranking methods. +" +LMCanvas: Object-Oriented Interaction to Personalize Large Language Model-Powered Writing Environments,Tae Soo Kim,http://arxiv.org/pdf/2303.15125v1.pdf,2023-03-27,"['cs.hc', 'cs.cl']",2303.15125v1.pdf," Large language models (LLMs) can enhance writing by automating or supporting +specific tasks in writers' workflows (e.g., paraphrasing, creating analogies). +Leveraging this capability, a collection of interfaces have been developed that +provide LLM-powered tools for specific writing tasks. However, these interfaces +provide limited support for writers to create personal tools for their own +unique tasks, and may not comprehensively fulfill a writer's needs -- requiring +them to continuously switch between interfaces during writing. In this work, we +envision LMCanvas, an interface that enables writers to create their own +LLM-powered writing tools and arrange their personal writing environment by +interacting with ""blocks"" in a canvas. In this interface, users can create text +blocks to encapsulate writing and LLM prompts, model blocks for model parameter +configurations, and connect these to create pipeline blocks that output +generations. In this workshop paper, we discuss the design for LMCanvas and our +plans to develop this concept. +" +SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting,Xiaoying Zhang,http://arxiv.org/pdf/2305.09067v1.pdf,2023-05-15,['cs.cl'],2305.09067v1.pdf," Building end-to-end task bots and maintaining their integration with new +functionalities using minimal human efforts is a long-standing challenge in +dialog research. Recently large language models (LLMs) have demonstrated +exceptional proficiency in conversational engagement and adherence to +instructions across various downstream tasks. In this work, we introduce +SGP-TOD, Schema-Guided Prompting for building Task-Oriented Dialog systems +effortlessly based on LLMs. Utilizing the symbolic knowledge -- task schema, we +instruct fixed LLMs to generate appropriate responses on novel tasks, +circumventing the need for training data. Specifically, SGP-TOD comprises three +components: a LLM for engaging with users, a DST Prompter to aid the LLM with +dialog state tracking, which is then used to retrieve database items, and a +Policy Prompter to elicit proper responses adhering to the provided dialog +policy. Experimental results on Multiwoz, RADDLE and STAR datasets show that +our training-free strategy SGP-TOD, without any task-specific data, yields +state-of-the-art (SOTA) zero-shot performance, greatly surpasses the few-shot +approaches. In a domain-extension setting, SGP-TOD aptly adapts to new +functionalities by merely adding supplementary schema rules. We make our code +and data publicly available. +" +TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks,Shubhra Kanti Karmaker Santu,http://arxiv.org/pdf/2305.11430v2.pdf,2023-05-19,"['cs.ai', 'cs.cl', 'cs.ir', 'cs.lg', 'i.2.7']",2305.11430v2.pdf," While LLMs have shown great success in understanding and generating text in +traditional conversational settings, their potential for performing ill-defined +complex tasks is largely under-studied. Indeed, we are yet to conduct +comprehensive benchmarking studies with multiple LLMs that are exclusively +focused on a complex task. However, conducting such benchmarking studies is +challenging because of the large variations in LLMs' performance when different +prompt types/styles are used and different degrees of detail are provided in +the prompts. To address this issue, the paper proposes a general taxonomy that +can be used to design prompts with specific properties in order to perform a +wide range of complex tasks. This taxonomy will allow future benchmarking +studies to report the specific categories of prompts used as part of the study, +enabling meaningful comparisons across different studies. Also, by establishing +a common standard through this taxonomy, researchers will be able to draw more +accurate conclusions about LLMs' performance on a specific complex task. +" +S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering,Fangyu Lei,http://arxiv.org/pdf/2305.11725v1.pdf,2023-05-19,['cs.cl'],2305.11725v1.pdf," Answering multi-hop questions over hybrid factual knowledge from the given +text and table (TextTableQA) is a challenging task. Existing models mainly +adopt a retriever-reader framework, which have several deficiencies, such as +noisy labeling in training retriever, insufficient utilization of heterogeneous +information over text and table, and deficient ability for different reasoning +operations. In this paper, we propose a three-stage TextTableQA framework +S3HQA, which comprises of retriever, selector, and reasoner. We use a retriever +with refinement training to solve the noisy labeling problem. Then, a hybrid +selector considers the linked relationships between heterogeneous data to +select the most relevant factual knowledge. For the final stage, instead of +adapting a reading comprehension module like in previous methods, we employ a +generation-based reasoner to obtain answers. This includes two approaches: a +row-wise generator and an LLM prompting generator~(first time used in this +task). The experimental results demonstrate that our method achieves +competitive results in the few-shot setting. When trained on the full dataset, +our approach outperforms all baseline methods, ranking first on the HybridQA +leaderboard. +" +LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models,Yen-Ting Lin,http://arxiv.org/pdf/2305.13711v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.13711v1.pdf," We propose LLM-Eval, a unified multi-dimensional automatic evaluation method +for open-domain conversations with large language models (LLMs). Existing +evaluation methods often rely on human annotations, ground-truth responses, or +multiple LLM prompts, which can be expensive and time-consuming. To address +these issues, we design a single prompt-based evaluation method that leverages +a unified evaluation schema to cover multiple dimensions of conversation +quality in a single model call. We extensively evaluate the performance of +LLM-Eval on various benchmark datasets, demonstrating its effectiveness, +efficiency, and adaptability compared to state-of-the-art evaluation methods. +Our analysis also highlights the importance of choosing suitable LLMs and +decoding strategies for accurate evaluation results. LLM-Eval offers a +versatile and robust solution for evaluating open-domain conversation systems, +streamlining the evaluation process and providing consistent performance across +diverse scenarios. +" +AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models,Siqi Ouyang,http://arxiv.org/pdf/2305.15064v3.pdf,2023-05-24,['cs.cl'],2305.15064v3.pdf," Recent large language models (LLMs) are promising for making decisions in +grounded environments. However, LLMs frequently fail in complex decision-making +tasks due to the misalignment between the pre-trained knowledge in LLMs and the +actual rules in the environment. Existing methods require either costly +gradient computation or lengthy in-context demonstrations. In this paper, we +propose AutoPlan, an approach to guide LLM-based agents to accomplish +interactive decision-making tasks. AutoPlan augments the LLM prompt with a +task-solving plan and optimizes it through iterative experience collection and +reflection. Our experiments show that AutoPlan, though using no in-context +demonstrations, achieves success rates on par with the baselines using +human-written demonstrations on ALFWorld and even outperforms them by 8% on +HotpotQA. The code is available at https://github.com/owaski/AutoPlan. +" +ChatGPT for PLC/DCS Control Logic Generation,Heiko Koziolek,http://arxiv.org/pdf/2305.15809v1.pdf,2023-05-25,"['cs.se', 'cs.ai', 'd.2.2']",2305.15809v1.pdf," Large language models (LLMs) providing generative AI have become popular to +support software engineers in creating, summarizing, optimizing, and +documenting source code. It is still unknown how LLMs can support control +engineers using typical control programming languages in programming tasks. +Researchers have explored GitHub CoPilot or DeepMind AlphaCode for source code +generation but did not yet tackle control logic programming. The contribution +of this paper is an exploratory study, for which we created 100 LLM prompts in +10 representative categories to analyze control logic generation for of PLCs +and DCS from natural language. We tested the prompts by generating answers with +ChatGPT using the GPT-4 LLM. It generated syntactically correct IEC 61131-3 +Structured Text code in many cases and demonstrated useful reasoning skills +that could boost control engineer productivity. Our prompt collection is the +basis for a more formal LLM benchmark to test and compare such models for +control logic generation. +" +AdaPlanner: Adaptive Planning from Feedback with Language Models,Haotian Sun,http://arxiv.org/pdf/2305.16653v1.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.lg']",2305.16653v1.pdf," Large language models (LLMs) have recently demonstrated the potential in +acting as autonomous agents for sequential decision-making tasks. However, most +existing methods either take actions greedily without planning or rely on +static plans that are not adaptable to environmental feedback. Consequently, +the sequential decision-making performance of LLM agents degenerates with +problem complexity and plan horizons increase. We propose a closed-loop +approach, AdaPlanner, which allows the LLM agent to refine its self-generated +plan adaptively in response to environmental feedback. In AdaPlanner, the LLM +agent adaptively refines its plan from feedback with both in-plan and +out-of-plan refinement strategies. To mitigate hallucination, we develop a +code-style LLM prompt structure that facilitates plan generation across a +variety of tasks, environments, and agent capabilities. Furthermore, we propose +a skill discovery mechanism that leverages successful plans as few-shot +exemplars, enabling the agent to plan and refine with fewer task +demonstrations. Our experiments in the ALFWorld and MiniWoB++ environments +demonstrate that AdaPlanner outperforms state-of-the-art baselines by 3.73% and +4.11% while utilizing 2x and 600x fewer samples, respectively. +" +Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures,Yue Zhen,http://arxiv.org/pdf/2306.05171v1.pdf,2023-06-08,"['cs.ro', 'cs.ai']",2306.05171v1.pdf," Traditional robot task planning methods face challenges when dealing with +highly unstructured environments and complex tasks. We propose a task planning +method that combines human expertise with an LLM and have designed an LLM +prompt template, Think_Net_Prompt, with stronger expressive power to represent +structured professional knowledge. We further propose a method to progressively +decompose tasks and generate a task tree to reduce the planning volume for each +task, and we have designed a strategy to decouple robot task planning. By +dividing different planning entities and separating the task from the actual +machine binding process, the task planning process becomes more flexible. +Research results show that our method performs well in handling specified code +formats, understanding the relationship between tasks and subtasks, and +extracting parameters from text descriptions. However, there are also problems +such as limited complexity of task logic handling, ambiguity in the quantity of +parts and the precise location of assembly. Improving the precision of task +description and cognitive structure can bring certain improvements. +https://github.com/NOMIzy/Think_Net_Prompt +" +SayTap: Language to Quadrupedal Locomotion,Yujin Tang,http://arxiv.org/pdf/2306.07580v3.pdf,2023-06-13,['cs.ro'],2306.07580v3.pdf," Large language models (LLMs) have demonstrated the potential to perform +high-level planning. Yet, it remains a challenge for LLMs to comprehend +low-level commands, such as joint angle targets or motor torques. This paper +proposes an approach to use foot contact patterns as an interface that bridges +human commands in natural language and a locomotion controller that outputs +these low-level commands. This results in an interactive system for quadrupedal +robots that allows the users to craft diverse locomotion behaviors flexibly. We +contribute an LLM prompt design, a reward function, and a method to expose the +controller to the feasible distribution of contact patterns. The results are a +controller capable of achieving diverse locomotion patterns that can be +transferred to real robot hardware. Compared with other design choices, the +proposed approach enjoys more than 50% success rate in predicting the correct +contact patterns and can solve 10 more tasks out of a total of 30 tasks. Our +project site is: https://saytap.github.io. +" +Large Language Models Enable Few-Shot Clustering,Vijay Viswanathan,http://arxiv.org/pdf/2307.00524v1.pdf,2023-07-02,['cs.cl'],2307.00524v1.pdf," Unlike traditional unsupervised clustering, semi-supervised clustering allows +users to provide meaningful structure to the data, which helps the clustering +algorithm to match the user's intent. Existing approaches to semi-supervised +clustering require a significant amount of feedback from an expert to improve +the clusters. In this paper, we ask whether a large language model can amplify +an expert's guidance to enable query-efficient, few-shot semi-supervised text +clustering. We show that LLMs are surprisingly effective at improving +clustering. We explore three stages where LLMs can be incorporated into +clustering: before clustering (improving input features), during clustering (by +providing constraints to the clusterer), and after clustering (using LLMs +post-correction). We find incorporating LLMs in the first two stages can +routinely provide significant improvements in cluster quality, and that LLMs +enable a user to make trade-offs between cost and accuracy to produce desired +clusters. We release our code and LLM prompts for the public to use. +" +GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution,Yining Lu,http://arxiv.org/pdf/2307.08775v1.pdf,2023-07-17,['cs.ai'],2307.08775v1.pdf," Augmenting large language models (LLM) to use external tools enhances their +performance across a variety of tasks. However, prior works over-rely on +task-specific demonstration of tool use that limits their generalizability and +computational cost due to making many calls to large-scale LLMs. We introduce +GEAR, a computationally efficient query-tool grounding algorithm that is +generalizable to various tasks that require tool use while not relying on +task-specific demonstrations. GEAR achieves better efficiency by delegating +tool grounding and execution to small language models (SLM) and LLM, +respectively; while leveraging semantic and pattern-based evaluation at both +question and answer levels for generalizable tool grounding. We evaluate GEAR +on 14 datasets across 6 downstream tasks, demonstrating its strong +generalizability to novel tasks, tools and different SLMs. Despite offering +more efficiency, GEAR achieves higher precision in tool grounding compared to +prior strategies using LLM prompting, thus improving downstream accuracy at a +reduced computational cost. For example, we demonstrate that GEAR-augmented +GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of +better tool use. +" +Simple LLM Prompting is State-of-the-Art for Robust and Multilingual Dialogue Evaluation,John Mendonça,http://arxiv.org/pdf/2308.16797v2.pdf,2023-08-31,['cs.cl'],2308.16797v2.pdf," Despite significant research effort in the development of automatic dialogue +evaluation metrics, little thought is given to evaluating dialogues other than +in English. At the same time, ensuring metrics are invariant to semantically +similar responses is also an overlooked topic. In order to achieve the desired +properties of robustness and multilinguality for dialogue evaluation metrics, +we propose a novel framework that takes advantage of the strengths of current +evaluation models with the newly-established paradigm of prompting Large +Language Models (LLMs). Empirical results show our framework achieves state of +the art results in terms of mean Spearman correlation scores across several +benchmarks and ranks first place on both the Robust and Multilingual tasks of +the DSTC11 Track 4 ""Automatic Evaluation Metrics for Open-Domain Dialogue +Systems"", proving the evaluation capabilities of prompted LLMs. +" +"MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images",Weihao Liu,http://arxiv.org/pdf/2309.04790v1.pdf,2023-09-09,['cs.cl'],2309.04790v1.pdf," In the real world, knowledge often exists in a multimodal and heterogeneous +form. Addressing the task of question answering with hybrid data types, +including text, tables, and images, is a challenging task (MMHQA). Recently, +with the rise of large language models (LLM), in-context learning (ICL) has +become the most popular way to solve QA problems. We propose MMHQA-ICL +framework for addressing this problems, which includes stronger heterogeneous +data retriever and an image caption module. Most importantly, we propose a +Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage +their powerful performance in this task. We are the first to use end-to-end LLM +prompting method for this task. Experimental results demonstrate that our +framework outperforms all baselines and methods trained on the full dataset, +achieving state-of-the-art results under the few-shot setting on the +MultimodalQA dataset. +" +Empowering Private Tutoring by Chaining Large Language Models,Yulin Chen,http://arxiv.org/pdf/2309.08112v1.pdf,2023-09-15,['cs.hc'],2309.08112v1.pdf," Artificial intelligence has been applied in various aspects of online +education to facilitate teaching and learning. However, few approaches has been +made toward a complete AI-powered tutoring system. In this work, we explore the +development of a full-fledged intelligent tutoring system powered by +state-of-the-art large language models (LLMs), covering automatic course +planning and adjusting, tailored instruction, and flexible quiz evaluation. To +make the system robust to prolonged interaction and cater to individualized +education, the system is decomposed into three inter-connected core +processes-interaction, reflection, and reaction. Each process is implemented by +chaining LLM-powered tools along with dynamically updated memory modules. Tools +are LLMs prompted to execute one specific task at a time, while memories are +data storage that gets updated during education process. Statistical results +from learning logs demonstrate the effectiveness and mechanism of each tool +usage. Subjective feedback from human users reveal the usability of each +function, and comparison with ablation systems further testify the benefits of +the designed processes in long-term interaction. +" +Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering,Yike Wu,http://arxiv.org/pdf/2309.11206v2.pdf,2023-09-20,"['cs.cl', 'cs.ai']",2309.11206v2.pdf," Despite their competitive performance on knowledge-intensive tasks, large +language models (LLMs) still have limitations in memorizing all world knowledge +especially long tail knowledge. In this paper, we study the KG-augmented +language model approach for solving the knowledge graph question answering +(KGQA) task that requires rich world knowledge. Existing work has shown that +retrieving KG knowledge to enhance LLMs prompting can significantly improve +LLMs performance in KGQA. However, their approaches lack a well-formed +verbalization of KG knowledge, i.e., they ignore the gap between KG +representations and textual representations. To this end, we propose an +answer-sensitive KG-to-Text approach that can transform KG knowledge into +well-textualized statements most informative for KGQA. Based on this approach, +we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task. +Experiments on several KGQA benchmarks show that the proposed KG-to-Text +augmented LLMs approach outperforms previous KG-augmented LLMs approaches +regarding answer accuracy and usefulness of knowledge statements. +" +LPML: LLM-Prompting Markup Language for Mathematical Reasoning,Ryutaro Yamauchi,http://arxiv.org/pdf/2309.13078v2.pdf,2023-09-21,"['cs.ai', 'cs.lg', 'cs.pl']",2309.13078v2.pdf," In utilizing large language models (LLMs) for mathematical reasoning, +addressing the errors in the reasoning and calculation present in the generated +text by LLMs is a crucial challenge. In this paper, we propose a novel +framework that integrates the Chain-of-Thought (CoT) method with an external +tool (Python REPL). We discovered that by prompting LLMs to generate structured +text in XML-like markup language, we could seamlessly integrate CoT and the +external tool and control the undesired behaviors of LLMs. With our approach, +LLMs can utilize Python computation to rectify errors within CoT. We applied +our method to ChatGPT (GPT-3.5) to solve challenging mathematical problems and +demonstrated that combining CoT and Python REPL through the markup language +enhances the reasoning capability of LLMs. Our approach enables LLMs to write +the markup language and perform advanced mathematical reasoning using only +zero-shot prompting. +" +HeaP: Hierarchical Policies for Web Actions using LLMs,Paloma Sodhi,http://arxiv.org/pdf/2310.03720v1.pdf,2023-10-05,['cs.lg'],2310.03720v1.pdf," Large language models (LLMs) have demonstrated remarkable capabilities in +performing a range of instruction following tasks in few and zero-shot +settings. However, teaching LLMs to perform tasks on the web presents +fundamental challenges -- combinatorially large open-world tasks and variations +across web interfaces. We tackle these challenges by leveraging LLMs to +decompose web tasks into a collection of sub-tasks, each of which can be solved +by a low-level, closed-loop policy. These policies constitute a shared grammar +across tasks, i.e., new web tasks can be expressed as a composition of these +policies. We propose a novel framework, Hierarchical Policies for Web Actions +using LLMs (HeaP), that learns a set of hierarchical LLM prompts from +demonstrations for planning high-level tasks and executing them via a sequence +of low-level policies. We evaluate HeaP against a range of baselines on a suite +of web tasks, including MiniWoB++, WebArena, a mock airline CRM, as well as +live website interactions, and show that it is able to outperform prior works +using orders of magnitude less data. +" +OptiMUS: Optimization Modeling Using MIP Solvers and large language models,Ali AhmadiTeshnizi,http://arxiv.org/pdf/2310.06116v2.pdf,2023-10-09,['cs.ai'],2310.06116v2.pdf," Optimization problems are pervasive across various sectors, from +manufacturing and distribution to healthcare. However, most such problems are +still solved heuristically by hand rather than optimally by state-of-the-art +solvers, as the expertise required to formulate and solve these problems limits +the widespread adoption of optimization tools and techniques. We introduce +OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and +solve MILP problems from their natural language descriptions. OptiMUS is +capable of developing mathematical models, writing and debugging solver code, +developing tests, and checking the validity of generated solutions. To +benchmark our agent, we present NLP4LP, a novel dataset of linear programming +(LP) and mixed integer linear programming (MILP) problems. Our experiments +demonstrate that OptiMUS solves nearly twice as many problems as a basic LLM +prompting strategy. OptiMUS code and NLP4LP dataset are available at +\href{https://github.com/teshnizi/OptiMUS}{https://github.com/teshnizi/OptiMUS} +" +A ML-LLM pairing for better code comment classification,Hanna Abi Akl,http://arxiv.org/pdf/2310.10275v1.pdf,2023-10-13,"['cs.se', 'cs.ai']",2310.10275v1.pdf," The ""Information Retrieval in Software Engineering (IRSE)"" at FIRE 2023 +shared task introduces code comment classification, a challenging task that +pairs a code snippet with a comment that should be evaluated as either useful +or not useful to the understanding of the relevant code. We answer the code +comment classification shared task challenge by providing a two-fold +evaluation: from an algorithmic perspective, we compare the performance of +classical machine learning systems and complement our evaluations from a +data-driven perspective by generating additional data with the help of large +language model (LLM) prompting to measure the potential increase in +performance. Our best model, which took second place in the shared task, is a +Neural Network with a Macro-F1 score of 88.401% on the provided seed data and a +1.5% overall increase in performance on the data generated by the LLM. +" +Multi-stage Large Language Model Correction for Speech Recognition,Jie Pu,http://arxiv.org/pdf/2310.11532v1.pdf,2023-10-17,"['cs.cl', 'eess.as']",2310.11532v1.pdf," In this paper, we investigate the usage of large language models (LLMs) to +improve the performance of competitive speech recognition systems. Different +from traditional language models that focus on one single data domain, the rise +of LLMs brings us the opportunity to push the limit of state-of-the-art ASR +performance, and at the same time to achieve higher robustness and generalize +effectively across multiple domains. Motivated by this, we propose a novel +multi-stage approach to combine traditional language model re-scoring and LLM +prompting. Specifically, the proposed method has two stages: the first stage +uses a language model to re-score an N-best list of ASR hypotheses and run a +confidence check; The second stage uses prompts to a LLM to perform ASR error +correction on less confident results from the first stage. Our experimental +results demonstrate the effectiveness of the proposed method by showing a 10% ~ +20% relative improvement in WER over a competitive ASR system -- across +multiple test domains. +" +PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows,Savvas Petridis,http://arxiv.org/pdf/2310.15435v1.pdf,2023-10-24,"['cs.hc', 'cs.ai']",2310.15435v1.pdf," Prototyping AI applications is notoriously difficult. While large language +model (LLM) prompting has dramatically lowered the barriers to AI prototyping, +designers are still prototyping AI functionality and UI separately. We +investigate how coupling prompt and UI design affects designers' workflows. +Grounding this research, we developed PromptInfuser, a Figma plugin that +enables users to create semi-functional mockups, by connecting UI elements to +the inputs and outputs of prompts. In a study with 14 designers, we compare +PromptInfuser to designers' current AI-prototyping workflow. PromptInfuser was +perceived to be significantly more useful for communicating product ideas, more +capable of producing prototypes that realistically represent the envisioned +artifact, more efficient for prototyping, and more helpful for anticipating UI +issues and technical constraints. PromptInfuser encouraged iteration over +prompt and UI together, which helped designers identify UI and prompt +incompatibilities and reflect upon their total solution. Together, these +findings inform future systems for prototyping AI applications. +" +OmniFill: Domain-Agnostic Form Filling Suggestions Using Multi-Faceted Context,Timothy J. Aveni,http://arxiv.org/pdf/2310.17826v1.pdf,2023-10-27,['cs.hc'],2310.17826v1.pdf," Predictive suggestion systems offer contextually-relevant text entry +completions. Existing approaches, like autofill, often excel in +narrowly-defined domains but fail to generalize to arbitrary workflows. We +introduce a conceptual framework to analyze the compound demands of a +particular suggestion context, yielding unique opportunities for large language +models (LLMs) to infer suggestions for a wide range of domain-agnostic +form-filling tasks that were out of reach with prior approaches. We explore +these opportunities in OmniFill, a prototype that collects multi-faceted +context including browsing and text entry activity to construct an LLM prompt +that offers suggestions in situ for arbitrary structured text entry interfaces. +Through a user study with 18 participants, we found that OmniFill offered +valuable suggestions and we identified four themes that characterize users' +behavior and attitudes: an ""opportunistic scrapbooking"" approach; a trust +placed in the system; value in partial success; and a need for visibility into +prompt context. +" +Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models,Ran Xu,http://arxiv.org/pdf/2311.00287v1.pdf,2023-11-01,"['cs.cl', 'cs.ai', 'cs.lg', 'q-bio.qm']",2311.00287v1.pdf," Clinical natural language processing requires methods that can address +domain-specific challenges, such as complex medical terminology and clinical +contexts. Recently, large language models (LLMs) have shown promise in this +domain. Yet, their direct deployment can lead to privacy issues and are +constrained by resources. To address this challenge, we delve into synthetic +clinical text generation using LLMs for clinical NLP tasks. We propose an +innovative, resource-efficient approach, ClinGen, which infuses knowledge into +the process. Our model involves clinical knowledge extraction and +context-informed LLM prompting. Both clinical topics and writing styles are +drawn from external domain-specific knowledge graphs and LLMs to guide data +generation. Our extensive empirical study across 7 clinical NLP tasks and 16 +datasets reveals that ClinGen consistently enhances performance across various +tasks, effectively aligning the distribution of real datasets and significantly +enriching the diversity of generated training instances. We will publish our +code and all the generated data in \url{https://github.com/ritaranx/ClinGen}. +" +Promptagator: Few-shot Dense Retrieval From 8 Examples,Zhuyun Dai,http://arxiv.org/pdf/2209.11755v1.pdf,2022-09-23,"['cs.cl', 'cs.ir']",2209.11755v1.pdf," Much recent research on information retrieval has focused on how to transfer +from one task (typically with abundant supervised data) to various other tasks +where supervision is limited, with the implicit assumption that it is possible +to generalize from one task to all the rest. However, this overlooks the fact +that there are many diverse and unique retrieval tasks, each targeting +different search intents, queries, and search domains. In this paper, we +suggest to work on Few-shot Dense Retrieval, a setting where each task comes +with a short description and a few examples. To amplify the power of a few +examples, we propose Prompt-base Query Generation for Retriever (Promptagator), +which leverages large language models (LLM) as a few-shot query generator, and +creates task-specific retrievers based on the generated data. Powered by LLM's +generalization ability, Promptagator makes it possible to create task-specific +end-to-end retrievers solely based on a few examples {without} using Natural +Questions or MS MARCO to train %question generators or dual encoders. +Surprisingly, LLM prompting with no more than 8 examples allows dual encoders +to outperform heavily engineered models trained on MS MARCO like ColBERT v2 by +more than 1.2 nDCG on average on 11 retrieval sets. Further training +standard-size re-rankers using the same generated data yields another 5.0 point +nDCG improvement. Our studies determine that query generation can be far more +effective than previously observed, especially when a small amount of +task-specific knowledge is given. +" +Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback,Baolin Peng,http://arxiv.org/pdf/2302.12813v3.pdf,2023-02-24,"['cs.cl', 'cs.ai']",2302.12813v3.pdf," Large language models (LLMs), such as ChatGPT, are able to generate +human-like, fluent responses for many downstream tasks, e.g., task-oriented +dialog and question answering. However, applying LLMs to real-world, +mission-critical applications remains challenging mainly due to their tendency +to generate hallucinations and their inability to use external knowledge. This +paper proposes a LLM-Augmenter system, which augments a black-box LLM with a +set of plug-and-play modules. Our system makes the LLM generate responses +grounded in external knowledge, e.g., stored in task-specific databases. It +also iteratively revises LLM prompts to improve model responses using feedback +generated by utility functions, e.g., the factuality score of a LLM-generated +response. The effectiveness of LLM-Augmenter is empirically validated on two +types of scenarios, task-oriented dialog and open-domain question answering. +LLM-Augmenter significantly reduces ChatGPT's hallucinations without +sacrificing the fluency and informativeness of its responses. We make the +source code and models publicly available. +" +AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback,Yann Dubois,http://arxiv.org/pdf/2305.14387v2.pdf,2023-05-22,"['cs.lg', 'cs.ai', 'cs.cl']",2305.14387v2.pdf," Large language models (LLMs) such as ChatGPT have seen widespread adoption +due to their ability to follow user instructions well. Developing these LLMs +involves a complex yet poorly understood workflow requiring training with human +feedback. Replicating and understanding this instruction-following process +faces three major challenges: the high cost of data collection, the lack of +trustworthy evaluation, and the absence of reference method implementations. We +address these challenges with AlpacaFarm, a simulator that enables research and +development for learning from feedback at a low cost. First, we design LLM +prompts to simulate human feedback that are 45x cheaper than crowdworkers and +display high agreement with humans. Second, we propose an automatic evaluation +and validate it against human instructions obtained on real-world interactions. +Third, we contribute reference implementations for several methods (PPO, +best-of-n, expert iteration, and more) that learn from pairwise feedback. +Finally, as an end-to-end validation of AlpacaFarm, we train and evaluate +eleven models on 10k pairs of real human feedback and show that rankings of +models trained in AlpacaFarm match rankings of models trained on human data. As +a demonstration of the research possible in AlpacaFarm, we find that methods +that use a reward model can substantially improve over supervised fine-tuning +and that our reference PPO implementation leads to a +10% improvement in +win-rate against Davinci003. We release all components of AlpacaFarm at +https://github.com/tatsu-lab/alpaca_farm. +" +MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems,Jakub Macina,http://arxiv.org/pdf/2305.14536v2.pdf,2023-05-23,['cs.cl'],2305.14536v2.pdf," While automatic dialogue tutors hold great potential in making education +personalized and more accessible, research on such systems has been hampered by +a lack of sufficiently large and high-quality datasets. Collecting such +datasets remains challenging, as recording tutoring sessions raises privacy +concerns and crowdsourcing leads to insufficient data quality. To address this, +we propose a framework to generate such dialogues by pairing human teachers +with a Large Language Model (LLM) prompted to represent common student errors. +We describe how we use this framework to collect MathDial, a dataset of 3k +one-to-one teacher-student tutoring dialogues grounded in multi-step math +reasoning problems. While models like GPT-3 are good problem solvers, they fail +at tutoring because they generate factually incorrect feedback or are prone to +revealing solutions to students too early. To overcome this, we let teachers +provide learning opportunities to students by guiding them using various +scaffolding questions according to a taxonomy of teacher moves. We demonstrate +MathDial and its extensive annotations can be used to finetune models to be +more effective tutors (and not just solvers). We confirm this by automatic and +human evaluation, notably in an interactive setting that measures the trade-off +between student solving success and telling solutions. The dataset is released +publicly. +" +SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and Reasoning,Yue Wu,http://arxiv.org/pdf/2305.15486v2.pdf,2023-05-24,"['cs.ai', 'cs.lg']",2305.15486v2.pdf," Open-world survival games pose significant challenges for AI algorithms due +to their multi-tasking, deep exploration, and goal prioritization requirements. +Despite reinforcement learning (RL) being popular for solving games, its high +sample complexity limits its effectiveness in complex open-world games like +Crafter or Minecraft. We propose a novel approach, SPRING, to read the game's +original academic paper and use the knowledge learned to reason and play the +game through a large language model (LLM). Prompted with the LaTeX source as +game context and a description of the agent's current observation, our SPRING +framework employs a directed acyclic graph (DAG) with game-related questions as +nodes and dependencies as edges. We identify the optimal action to take in the +environment by traversing the DAG and calculating LLM responses for each node +in topological order, with the LLM's answer to final node directly translating +to environment actions. In our experiments, we study the quality of in-context +""reasoning"" induced by different forms of prompts under the setting of the +Crafter open-world environment. Our experiments suggest that LLMs, when +prompted with consistent chain-of-thought, have great potential in completing +sophisticated high-level trajectories. Quantitatively, SPRING with GPT-4 +outperforms all state-of-the-art RL baselines, trained for 1M steps, without +any training. Finally, we show the potential of games as a test bed for LLMs. +" +Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models,Haonan Duan,http://arxiv.org/pdf/2305.15594v1.pdf,2023-05-24,"['cs.lg', 'cs.cl', 'cs.cr']",2305.15594v1.pdf," Large language models (LLMs) are excellent in-context learners. However, the +sensitivity of data contained in prompts raises privacy concerns. Our work +first shows that these concerns are valid: we instantiate a simple but highly +effective membership inference attack against the data used to prompt LLMs. To +address this vulnerability, one could forego prompting and resort to +fine-tuning LLMs with known algorithms for private gradient descent. However, +this comes at the expense of the practicality and efficiency offered by +prompting. Therefore, we propose to privately learn to prompt. We first show +that soft prompts can be obtained privately through gradient descent on +downstream data. However, this is not the case for discrete prompts. Thus, we +orchestrate a noisy vote among an ensemble of LLMs presented with different +prompts, i.e., a flock of stochastic parrots. The vote privately transfers the +flock's knowledge into a single public prompt. We show that LLMs prompted with +our private algorithms closely match the non-private baselines. For example, +using GPT3 as the base model, we achieve a downstream accuracy of 92.7% on the +sst2 dataset with ($\epsilon=0.147, \delta=10^{-6}$)-differential privacy vs. +95.2% for the non-private baseline. Through our experiments, we also show that +our prompt-based approach is easily deployed with existing commercial APIs. +" +Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction,Salvatore Carta,http://arxiv.org/pdf/2307.01128v1.pdf,2023-07-03,"['cs.cl', 'cs.ai']",2307.01128v1.pdf," In the current digitalization era, capturing and effectively representing +knowledge is crucial in most real-world scenarios. In this context, knowledge +graphs represent a potent tool for retrieving and organizing a vast amount of +information in a properly interconnected and interpretable structure. However, +their generation is still challenging and often requires considerable human +effort and domain expertise, hampering the scalability and flexibility across +different application fields. This paper proposes an innovative knowledge graph +generation approach that leverages the potential of the latest generative large +language models, such as GPT-3.5, that can address all the main critical issues +in knowledge graph building. The approach is conveyed in a pipeline that +comprises novel iterative zero-shot and external knowledge-agnostic strategies +in the main stages of the generation process. Our unique manifold approach may +encompass significant benefits to the scientific community. In particular, the +main contribution can be summarized by: (i) an innovative strategy for +iteratively prompting large language models to extract relevant components of +the final graph; (ii) a zero-shot strategy for each prompt, meaning that there +is no need for providing examples for ""guiding"" the prompt result; (iii) a +scalable solution, as the adoption of LLMs avoids the need for any external +resources or human expertise. To assess the effectiveness of our proposed +model, we performed experiments on a dataset that covered a specific domain. We +claim that our proposal is a suitable solution for scalable and versatile +knowledge graph construction and may be applied to different and novel +contexts. +" +PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine,Chenrui Zhang,http://arxiv.org/pdf/2308.12033v1.pdf,2023-08-23,"['cs.cl', 'cs.ai']",2308.12033v1.pdf," As an effective tool for eliciting the power of Large Language Models (LLMs), +prompting has recently demonstrated unprecedented abilities across a variety of +complex tasks. To further improve the performance, prompt ensemble has +attracted substantial interest for tackling the hallucination and instability +of LLMs. However, existing methods usually adopt a two-stage paradigm, which +requires a pre-prepared set of prompts with substantial manual effort, and is +unable to perform directed optimization for different weak learners. In this +paper, we propose a simple, universal, and automatic method named PREFER (Pompt +Ensemble learning via Feedback-Reflect-Refine) to address the stated +limitations. Specifically, given the fact that weak learners are supposed to +focus on hard examples during boosting, PREFER builds a feedback mechanism for +reflecting on the inadequacies of existing weak learners. Based on this, the +LLM is required to automatically synthesize new prompts for iterative +refinement. Moreover, to enhance stability of the prompt effect evaluation, we +propose a novel prompt bagging method involving forward and backward thinking, +which is superior to majority voting and is beneficial for both feedback and +weight calculation in boosting. Extensive experiments demonstrate that our +PREFER achieves state-of-the-art performance in multiple types of tasks by a +significant margin. We have made our code publicly available. +" +ABScribe: Rapid Exploration of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models,Mohi Reza,http://arxiv.org/pdf/2310.00117v2.pdf,2023-09-29,"['cs.hc', 'cs.ai', 'cs.lg']",2310.00117v2.pdf," Exploring alternative ideas by rewriting text is integral to the writing +process. State-of-the-art large language models (LLMs) can simplify writing +variation generation. However, current interfaces pose challenges for +simultaneous consideration of multiple variations: creating new versions +without overwriting text can be difficult, and pasting them sequentially can +clutter documents, increasing workload and disrupting writers' flow. To tackle +this, we present ABScribe, an interface that supports rapid, yet visually +structured, exploration of writing variations in human-AI co-writing tasks. +With ABScribe, users can swiftly produce multiple variations using LLM prompts, +which are auto-converted into reusable buttons. Variations are stored +adjacently within text segments for rapid in-place comparisons using mouse-over +interactions on a context toolbar. Our user study with 12 writers shows that +ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances +user perceptions of the revision process (d = 2.41, p < 0.001) compared to a +popular baseline workflow, and provides insights into how writers explore +variations using LLMs. +" +Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models,Wenxuan Ding,http://arxiv.org/pdf/2310.01290v1.pdf,2023-10-02,"['cs.cl', 'cs.ai']",2310.01290v1.pdf," Large language models (LLMs) are widely adopted in knowledge-intensive tasks +and have achieved impressive performance thanks to their knowledge abilities. +While LLMs have demonstrated outstanding performance on atomic or linear +(multi-hop) QA tasks, whether they can reason in knowledge-rich scenarios with +interweaving constraints remains an underexplored problem. In this work, we +propose geometric reasoning over structured knowledge, where pieces of +knowledge are connected in a graph structure and models need to fill in the +missing information. Such geometric knowledge reasoning would require the +ability to handle structured knowledge, reason with uncertainty, verify facts, +and backtrack when an error occurs. We propose Knowledge Crosswords, a +multi-blank QA dataset where each problem consists of a natural language +question representing the geometric constraints of an incomplete entity +network, where LLMs are tasked with working out the missing entities while +meeting all factual constraints. Knowledge Crosswords contains 2,101 individual +problems, covering various knowledge domains and further divided into three +difficulty levels. We conduct extensive experiments to evaluate existing LLM +prompting approaches on the Knowledge Crosswords benchmark. We additionally +propose two new approaches, Staged Prompting and Verify-All, to augment LLMs' +ability to backtrack and verify structured constraints. Our results demonstrate +that while baseline approaches perform well on easier problems but struggle +with hard ones, our proposed Verify-All outperforms other methods by a large +margin and is more robust with hard problems. Further analysis reveals that +LLMs' ability of geometric reasoning over structured knowledge is still far +from robust or perfect, susceptible to confounders such as the order of +options, certain structural patterns, assumption of existence of correct +answer, and more. +" +Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference,Zachary Levonian,http://arxiv.org/pdf/2310.03184v1.pdf,2023-10-04,"['cs.cl', 'cs.hc']",2310.03184v1.pdf," For middle-school math students, interactive question-answering (QA) with +tutors is an effective way to learn. The flexibility and emergent capabilities +of generative large language models (LLMs) has led to a surge of interest in +automating portions of the tutoring process - including interactive QA to +support conceptual discussion of mathematical concepts. However, LLM responses +to math questions can be incorrect or mismatched to the educational context - +such as being misaligned with a school's curriculum. One potential solution is +retrieval-augmented generation (RAG), which involves incorporating a vetted +external knowledge source in the LLM prompt to increase response quality. In +this paper, we designed prompts that retrieve and use content from a +high-quality open-source math textbook to generate responses to real student +questions. We evaluate the efficacy of this RAG system for middle-school +algebra and geometry QA by administering a multi-condition survey, finding that +humans prefer responses generated using RAG, but not when responses are too +grounded in the textbook content. We argue that while RAG is able to improve +response quality, designers of math QA systems must consider trade-offs between +generating responses preferred by students and responses closely matched to +specific educational resources. +" +Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning,Gurusha Juneja,http://arxiv.org/pdf/2310.18338v1.pdf,2023-10-21,"['cs.cl', 'cs.ai']",2310.18338v1.pdf," Large Language Models (LLMs) prompted to generate chain-of-thought (CoT) +exhibit impressive reasoning capabilities. Recent attempts at prompt +decomposition toward solving complex, multi-step reasoning problems depend on +the ability of the LLM to simultaneously decompose and solve the problem. A +significant disadvantage is that foundational LLMs are typically not available +for fine-tuning, making adaptation computationally prohibitive. We believe (and +demonstrate) that problem decomposition and solution generation are distinct +capabilites, better addressed in separate modules, than by one monolithic LLM. +We introduce DaSLaM, which uses a decomposition generator to decompose complex +problems into subproblems that require fewer reasoning steps. These subproblems +are answered by a solver. We use a relatively small (13B parameters) LM as the +decomposition generator, which we train using policy gradient optimization to +interact with a solver LM (regarded as black-box) and guide it through +subproblems, thereby rendering our method solver-agnostic. Evaluation on +multiple different reasoning datasets reveal that with our method, a 175 +billion parameter LM (text-davinci-003) can produce competitive or even better +performance, compared to its orders-of-magnitude larger successor, GPT-4. +Additionally, we show that DaSLaM is not limited by the solver's capabilities +as a function of scale; e.g., solver LMs with diverse sizes give significant +performance improvement with our solver-agnostic decomposition technique. +Exhaustive ablation studies evince the superiority of our modular finetuning +technique over exorbitantly large decomposer LLMs, based on prompting alone. +" +Universal Fuzzing via Large Language Models,Chunqiu Steven Xia,http://arxiv.org/pdf/2308.04748v1.pdf,2023-08-09,"['cs.se', 'cs.lg']",2308.04748v1.pdf," Fuzzing has achieved tremendous success in discovering bugs and +vulnerabilities in various software systems. Systems under test (SUTs) that +take in programming or formal language as inputs, e.g., compilers, runtime +engines, constraint solvers, and software libraries with accessible APIs, are +especially important as they are fundamental building blocks of software +development. However, existing fuzzers for such systems often target a specific +language, and thus cannot be easily applied to other languages or even other +versions of the same language. Moreover, the inputs generated by existing +fuzzers are often limited to specific features of the input language, and thus +can hardly reveal bugs related to other or new features. This paper presents +Fuzz4All, the first fuzzer that is universal in the sense that it can target +many different input languages and many different features of these languages. +The key idea behind Fuzz4All is to leverage large language models (LLMs) as an +input generation and mutation engine, which enables the approach to produce +diverse and realistic inputs for any practically relevant language. To realize +this potential, we present a novel autoprompting technique, which creates LLM +prompts that are wellsuited for fuzzing, and a novel LLM-powered fuzzing loop, +which iteratively updates the prompt to create new fuzzing inputs. We evaluate +Fuzz4All on nine systems under test that take in six different languages (C, +C++, Go, SMT2, Java and Python) as inputs. The evaluation shows, across all six +languages, that universal fuzzing achieves higher coverage than existing, +language-specific fuzzers. Furthermore, Fuzz4All has identified 76 bugs in +widely used systems, such as GCC, Clang, Z3, CVC5, OpenJDK, and the Qiskit +quantum computing platform, with 47 bugs already confirmed by developers as +previously unknown. +" +AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts,Tongshuang Wu,http://arxiv.org/pdf/2110.01691v3.pdf,2021-10-04,"['cs.hc', 'cs.cl']",2110.01691v3.pdf," Although large language models (LLMs) have demonstrated impressive potential +on simple tasks, their breadth of scope, lack of transparency, and insufficient +controllability can make them less effective when assisting humans on more +complex tasks. In response, we introduce the concept of Chaining LLM steps +together, where the output of one step becomes the input for the next, thus +aggregating the gains per step. We first define a set of LLM primitive +operations useful for Chain construction, then present an interactive system +where users can modify these Chains, along with their intermediate results, in +a modular way. In a 20-person user study, we found that Chaining not only +improved the quality of task outcomes, but also significantly enhanced system +transparency, controllability, and sense of collaboration. Additionally, we saw +that users developed new ways of interacting with LLMs through Chains: they +leveraged sub-tasks to calibrate model expectations, compared and contrasted +alternative strategies by observing parallel downstream effects, and debugged +unexpected model outputs by ""unit-testing"" sub-components of a Chain. In two +case studies, we further explore how LLM Chains may be used in future +applications +" +PromptChainer: Chaining Large Language Model Prompts through Visual Programming,Tongshuang Wu,http://arxiv.org/pdf/2203.06566v1.pdf,2022-03-13,['cs.hc'],2203.06566v1.pdf," While LLMs can effectively help prototype single ML functionalities, many +real-world applications involve complex tasks that cannot be easily handled via +a single run of an LLM. Recent work has found that chaining multiple LLM runs +together (with the output of one step being the input to the next) can help +users accomplish these more complex tasks, and in a way that is perceived to be +more transparent and controllable. However, it remains unknown what users need +when authoring their own LLM chains -- a key step for lowering the barriers for +non-AI-experts to prototype AI-infused applications. In this work, we explore +the LLM chain authoring process. We conclude from pilot studies find that +chaining requires careful scaffolding for transforming intermediate node +outputs, as well as debugging the chain at multiple granularities; to help with +these needs, we designed PromptChainer, an interactive interface for visually +programming chains. Through case studies with four people, we show that +PromptChainer supports building prototypes for a range of applications, and +conclude with open questions on scaling chains to complex tasks, and supporting +low-fi chain prototyping. +" +Few-shot Reranking for Multi-hop QA via Language Model Prompting,Muhammad Khalifa,http://arxiv.org/pdf/2205.12650v3.pdf,2022-05-25,"['cs.cl', 'cs.ir']",2205.12650v3.pdf," We study few-shot reranking for multi-hop QA with open-domain questions. To +alleviate the need for a large number of labeled question-document pairs for +retriever training, we propose PromptRank, which relies on large language +models prompting for multi-hop path reranking. PromptRank first constructs an +instruction-based prompt that includes a candidate document path and then +computes the relevance score between a given question and the path based on the +conditional likelihood of the question given the path prompt according to a +language model. PromptRank yields strong retrieval performance on HotpotQA with +only 128 training examples compared to state-of-the-art methods trained on +thousands of examples -- 73.6 recall@10 by PromptRank vs. 77.8 by PathRetriever +and 77.5 by multi-hop dense retrieval. Code available at +https://github.com/mukhal/PromptRank +" +Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency,Lingfeng Shen,http://arxiv.org/pdf/2305.10713v2.pdf,2023-05-18,"['cs.cl', 'cs.lg']",2305.10713v2.pdf," With growing capabilities of large language models, prompting them has become +the dominant way to access them. This has motivated the development of +strategies for automatically selecting effective language prompts. In this +paper, we introduce prompt flatness, a new metric to quantify the expected +utility of a language prompt. This metric is inspired by flatness +regularization in statistical learning that quantifies the robustness of the +model towards its parameter perturbations. We provide theoretical foundations +for this metric and its relationship with other prompt selection metrics, +providing a comprehensive understanding of existing methods. Empirically, we +show that combining prompt flatness with existing metrics improves both +performance and sample efficiency. Our metric outperforms the previous prompt +selection metrics with an average increase of 5% in accuracy and 10% in Pearson +correlation across 6 classification benchmarks. +" +A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction,Erica Cai,http://arxiv.org/pdf/2305.15051v1.pdf,2023-05-24,['cs.cl'],2305.15051v1.pdf," We consider dyadic zero-shot event extraction (EE) to identify actions +between pairs of actors. The \emph{zero-shot} setting allows social scientists +or other non-computational researchers to extract any customized, +user-specified set of events without training, resulting in a \emph{dyadic} +event database, allowing insight into sociopolitical relational dynamics among +actors and the higher level organizations or countries they represent. +Unfortunately, we find that current zero-shot EE methods perform poorly for the +task, with issues including word sense ambiguity, modality mismatch, and +efficiency. Straightforward application of large language model prompting +typically performs even worse. We address these challenges with a new +fine-grained, multi-stage generative question-answer method, using a Monte +Carlo approach to exploit and overcome the randomness of generative outputs. It +performs 90\% fewer queries than a previous approach, with strong performance +on the widely-used Automatic Content Extraction dataset. Finally, we extend our +method to extract affiliations of actor arguments and demonstrate our method +and findings on a dyadic international relations case study. +" +EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria,Tae Soo Kim,http://arxiv.org/pdf/2309.13633v1.pdf,2023-09-24,"['cs.hc', 'cs.ai', 'cs.cl']",2309.13633v1.pdf," By simply composing prompts, developers can prototype novel generative +applications with Large Language Models (LLMs). To refine prototypes into +products, however, developers must iteratively revise prompts by evaluating +outputs to diagnose weaknesses. Formative interviews (N=8) revealed that +developers invest significant effort in manually evaluating outputs as they +assess context-specific and subjective criteria. We present EvalLM, an +interactive system for iteratively refining prompts by evaluating multiple +outputs on user-defined criteria. By describing criteria in natural language, +users can employ the system's LLM-based evaluator to get an overview of where +prompts excel or fail, and improve these based on the evaluator's feedback. A +comparative study (N=12) showed that EvalLM, when compared to manual +evaluation, helped participants compose more diverse criteria, examine twice as +many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond +prompts, our work can be extended to augment model evaluation and alignment in +specific application contexts. +" +Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting,Nikolay Bogoychev,http://arxiv.org/pdf/2310.05824v1.pdf,2023-10-09,['cs.cl'],2310.05824v1.pdf," Terminology correctness is important in the downstream application of machine +translation, and a prevalent way to ensure this is to inject terminology +constraints into a translation system. In our submission to the WMT 2023 +terminology translation task, we adopt a translate-then-refine approach which +can be domain-independent and requires minimal manual efforts. We annotate +random source words with pseudo-terminology translations obtained from word +alignment to first train a terminology-aware model. Further, we explore two +post-processing methods. First, we use an alignment process to discover whether +a terminology constraint has been violated, and if so, we re-decode with the +violating word negatively constrained. Alternatively, we leverage a large +language model to refine a hypothesis by providing it with terminology +constraints. Results show that our terminology-aware model learns to +incorporate terminologies effectively, and the large language model refinement +process can further improve terminology recall. +" +Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following,Yuki Inoue,http://arxiv.org/pdf/2211.03267v1.pdf,2022-11-07,"['cs.ro', 'cs.cv']",2211.03267v1.pdf," Embodied Instruction Following (EIF) studies how mobile manipulator robots +should be controlled to accomplish long-horizon tasks specified by natural +language instructions. While most research on EIF are conducted in simulators, +the ultimate goal of the field is to deploy the agents in real life. As such, +it is important to minimize the data cost required for training an agent, to +help the transition from sim to real. However, many studies only focus on the +performance and overlook the data cost -- modules that require separate +training on extra data are often introduced without a consideration on +deployability. In this work, we propose FILM++ which extends the existing work +FILM with modifications that do not require extra data. While all data-driven +modules are kept constant, FILM++ more than doubles FILM's performance. +Furthermore, we propose Prompter, which replaces FILM++'s semantic search +module with language model prompting. Unlike FILM++'s implementation that +requires training on extra sets of data, no training is needed for our +prompting based implementation while achieving better or at least comparable +performance. Prompter achieves 42.64% and 45.72% on the ALFRED benchmark with +high-level instructions only and with step-by-step instructions, respectively, +outperforming the previous state of the art by 6.57% and 10.31%. +" +FIRE: Food Image to REcipe generation,Prateek Chhikara,http://arxiv.org/pdf/2308.14391v1.pdf,2023-08-28,"['cs.cv', 'cs.cl']",2308.14391v1.pdf," Food computing has emerged as a prominent multidisciplinary field of research +in recent years. An ambitious goal of food computing is to develop end-to-end +intelligent systems capable of autonomously producing recipe information for a +food image. Current image-to-recipe methods are retrieval-based and their +success depends heavily on the dataset size and diversity, as well as the +quality of learned embeddings. Meanwhile, the emergence of powerful +attention-based vision and language models presents a promising avenue for +accurate and generalizable recipe generation, which has yet to be extensively +explored. This paper proposes FIRE, a novel multimodal methodology tailored to +recipe generation in the food computing domain, which generates the food title, +ingredients, and cooking instructions based on input food images. FIRE +leverages the BLIP model to generate titles, utilizes a Vision Transformer with +a decoder for ingredient extraction, and employs the T5 model to generate +recipes incorporating titles and ingredients as inputs. We showcase two +practical applications that can benefit from integrating FIRE with large +language model prompting: recipe customization to fit recipes to user +preferences and recipe-to-code transformation to enable automated cooking +processes. Our experimental findings validate the efficacy of our proposed +approach, underscoring its potential for future advancements and widespread +adoption in food computing. +" +Large language models can accurately predict searcher preferences,Paul Thomas,http://arxiv.org/pdf/2309.10621v1.pdf,2023-09-19,"['cs.ir', 'cs.ai', 'cs.cl', 'cs.lg']",2309.10621v1.pdf," Relevance labels, which indicate whether a search result is valuable to a +searcher, are key to evaluating and optimising search systems. The best way to +capture the true preferences of users is to ask them for their careful feedback +on which results would be useful, but this approach does not scale to produce a +large number of labels. Getting relevance labels at scale is usually done with +third-party labellers, who judge on behalf of the user, but there is a risk of +low-quality data if the labeller doesn't understand user needs. To improve +quality, one standard approach is to study real users through interviews, user +studies and direct feedback, find areas where labels are systematically +disagreeing with users, then educate labellers about user needs through judging +guidelines, training and monitoring. This paper introduces an alternate +approach for improving label quality. It takes careful feedback from real +users, which by definition is the highest-quality first-party gold data that +can be derived, and develops an large language model prompt that agrees with +that data. + We present ideas and observations from deploying language models for +large-scale relevance labelling at Bing, and illustrate with data from TREC. We +have found large language models can be effective, with accuracy as good as +human labellers and similar capability to pick the hardest queries, best runs, +and best groups. Systematic changes to the prompts make a difference in +accuracy, but so too do simple paraphrases. To measure agreement with real +searchers needs high-quality ``gold'' labels, but with these we find that +models produce better labels than third-party workers, for a fraction of the +cost, and these labels let us train notably better rankers. +" +Meta-in-context learning in large language models,Julian Coda-Forno,http://arxiv.org/pdf/2305.12907v1.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.lg']",2305.12907v1.pdf," Large language models have shown tremendous performance in a variety of +tasks. In-context learning -- the ability to improve at a task after being +provided with a number of demonstrations -- is seen as one of the main +contributors to their success. In the present paper, we demonstrate that the +in-context learning abilities of large language models can be recursively +improved via in-context learning itself. We coin this phenomenon +meta-in-context learning. Looking at two idealized domains, a one-dimensional +regression task and a two-armed bandit task, we show that meta-in-context +learning adaptively reshapes a large language model's priors over expected +tasks. Furthermore, we find that meta-in-context learning modifies the +in-context learning strategies of such models. Finally, we extend our approach +to a benchmark of real-world regression problems where we observe competitive +performance to traditional learning algorithms. Taken together, our work +improves our understanding of in-context learning and paves the way toward +adapting large language models to the environment they are applied purely +through meta-in-context learning rather than traditional finetuning. +" +MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models,Masoud Monajatipoor,http://arxiv.org/pdf/2306.01311v1.pdf,2023-06-02,['cs.cl'],2306.01311v1.pdf," Large-scale language models have shown the ability to adapt to a new task via +conditioning on a few demonstrations (i.e., in-context learning). However, in +the vision-language domain, most large-scale pre-trained vision-language (VL) +models do not possess the ability to conduct in-context learning. How can we +enable in-context learning for VL models? In this paper, we study an +interesting hypothesis: can we transfer the in-context learning ability from +the language domain to VL domain? Specifically, we first meta-trains a language +model to perform in-context learning on NLP tasks (as in MetaICL); then we +transfer this model to perform VL tasks by attaching a visual encoder. Our +experiments suggest that indeed in-context learning ability can be transferred +cross modalities: our model considerably improves the in-context learning +capability on VL tasks and can even compensate for the size of the model +significantly. On VQA, OK-VQA, and GQA, our method could outperform the +baseline model while having 20 times fewer parameters. +" +A Theory of Emergent In-Context Learning as Implicit Structure Induction,Michael Hahn,http://arxiv.org/pdf/2303.07971v1.pdf,2023-03-14,"['cs.cl', 'cs.lg']",2303.07971v1.pdf," Scaling large language models (LLMs) leads to an emergent capacity to learn +in-context from example demonstrations. Despite progress, theoretical +understanding of this phenomenon remains limited. We argue that in-context +learning relies on recombination of compositional operations found in natural +language data. We derive an information-theoretic bound showing how in-context +learning abilities arise from generic next-token prediction when the +pretraining distribution has sufficient amounts of compositional structure, +under linguistically motivated assumptions. A second bound provides a +theoretical justification for the empirical success of prompting LLMs to output +intermediate steps towards an answer. To validate theoretical predictions, we +introduce a controlled setup for inducing in-context learning; unlike previous +approaches, it accounts for the compositional nature of language. Trained +transformers can perform in-context learning for a range of tasks, in a manner +consistent with the theoretical results. Mirroring real-world LLMs in a +miniature setup, in-context learning emerges when scaling parameters and data, +and models perform better when prompted to output intermediate steps. Probing +shows that in-context learning is supported by a representation of the input's +compositional structure. Taken together, these results provide a step towards +theoretical understanding of emergent behavior in large language models. +" +Fine-tune Language Models to Approximate Unbiased In-context Learning,Timothy Chu,http://arxiv.org/pdf/2310.03331v1.pdf,2023-10-05,['cs.lg'],2310.03331v1.pdf," In-context learning (ICL) is an astonishing emergent ability of large +language models (LLMs). By presenting a prompt that includes multiple +input-output pairs as examples and introducing a new query input, models can +generate the corresponding output. However, the performance of models heavily +relies on the quality of the input prompt when implementing in-context +learning. Biased or imbalanced input prompts can significantly degrade the +performance of language models. To address this issue, we introduce a +reweighted algorithm called RICL (Reweighted In-context Learning). This +algorithm fine-tunes language models using an unbiased validation set to +determine the optimal weight for each input-output example to approximate +unbiased in-context learning. Furthermore, we also introduce a low-cost +reweighted algorithm, a linear optimal weight approximation algorithm called +LARICL (Linear Approximation of Reweighted In-context Learning). This algorithm +requires minimal training cost while providing effective results. We prove the +convergence of our algorithm and validate its performance through experiments +conducted on a numerical dataset. The experimental findings reveal a +substantial improvement in comparison to benchmarks including the performance +of casual prompt-based in-context learning and the performance of a classic +fine-tuning method. +" +PRODIGY: Enabling In-context Learning Over Graphs,Qian Huang,http://arxiv.org/pdf/2305.12600v1.pdf,2023-05-21,"['cs.lg', 'cs.ai']",2305.12600v1.pdf," In-context learning is the ability of a pretrained model to adapt to novel +and diverse downstream tasks by conditioning on prompt examples, without +optimizing any parameters. While large language models have demonstrated this +ability, how in-context learning could be performed over graphs is unexplored. +In this paper, we develop \textbf{Pr}etraining \textbf{O}ver \textbf{D}iverse +\textbf{I}n-Context \textbf{G}raph S\textbf{y}stems (PRODIGY), the first +pretraining framework that enables in-context learning over graphs. The key +idea of our framework is to formulate in-context learning over graphs with a +novel \emph{prompt graph} representation, which connects prompt examples and +queries. We then propose a graph neural network architecture over the prompt +graph and a corresponding family of in-context pretraining objectives. With +PRODIGY, the pretrained model can directly perform novel downstream +classification tasks on unseen graphs via in-context learning. We provide +empirical evidence of the effectiveness of our framework by showcasing its +strong in-context learning performance on tasks involving citation networks and +knowledge graphs. Our approach outperforms the in-context learning accuracy of +contrastive pretraining baselines with hard-coded adaptation by 18\% on average +across all setups. Moreover, it also outperforms standard finetuning with +limited data by 33\% on average with in-context learning. +" +An Explanation of In-context Learning as Implicit Bayesian Inference,Sang Michael Xie,http://arxiv.org/pdf/2111.02080v6.pdf,2021-11-03,"['cs.cl', 'cs.lg']",2111.02080v6.pdf," Large language models (LMs) such as GPT-3 have the surprising ability to do +in-context learning, where the model learns to do a downstream task simply by +conditioning on a prompt consisting of input-output examples. The LM learns +from these examples without being explicitly pretrained to learn. Thus, it is +unclear what enables in-context learning. In this paper, we study how +in-context learning can emerge when pretraining documents have long-range +coherence. Here, the LM must infer a latent document-level concept to generate +coherent next tokens during pretraining. At test time, in-context learning +occurs when the LM also infers a shared latent concept between examples in a +prompt. We prove when this occurs despite a distribution mismatch between +prompts and pretraining data in a setting where the pretraining distribution is +a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs +capable of in-context learning, we generate a small-scale synthetic dataset +(GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond +the theory, experiments on GINC exhibit large-scale real-world phenomena +including improved in-context performance with model scaling (despite the same +pretraining loss), sensitivity to example order, and instances where zero-shot +is better than few-shot in-context learning. +" +Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale,Hritik Bansal,http://arxiv.org/pdf/2212.09095v2.pdf,2022-12-18,"['cs.cl', 'cs.ai']",2212.09095v2.pdf," Language models have been shown to perform better with an increase in scale +on a wide variety of tasks via the in-context learning paradigm. In this paper, +we investigate the hypothesis that the ability of a large language model to +in-context learn-perform a task is not uniformly spread across all of its +underlying components. Using a 66 billion parameter language model (OPT-66B) +across a diverse set of 14 downstream tasks, we find this is indeed the case: +$\sim$70% of attention heads and $\sim$20% of feed forward networks can be +removed with minimal decline in task performance. We find substantial overlap +in the set of attention heads (un)important for in-context learning across +tasks and number of in-context examples. We also address our hypothesis through +a task-agnostic lens, finding that a small set of attention heads in OPT-66B +score highly on their ability to perform primitive induction operations +associated with in-context learning, namely, prefix matching and copying. These +induction heads overlap with task-specific important heads, reinforcing +arguments by Olsson et al. (arXiv:2209.11895) regarding induction head +generality to more sophisticated behaviors associated with in-context learning. +Overall, our study provides several insights that indicate large language +models may be under-trained for in-context learning and opens up questions on +how to pre-train language models to more effectively perform in-context +learning. +" +A Closer Look at In-Context Learning under Distribution Shifts,Kartik Ahuja,http://arxiv.org/pdf/2305.16704v1.pdf,2023-05-26,"['cs.lg', 'stat.ml']",2305.16704v1.pdf," In-context learning, a capability that enables a model to learn from input +examples on the fly without necessitating weight updates, is a defining +characteristic of large language models. In this work, we follow the setting +proposed in (Garg et al., 2022) to better understand the generality and +limitations of in-context learning from the lens of the simple yet fundamental +task of linear regression. The key question we aim to address is: Are +transformers more adept than some natural and simpler architectures at +performing in-context learning under varying distribution shifts? To compare +transformers, we propose to use a simple architecture based on set-based +Multi-Layer Perceptrons (MLPs). We find that both transformers and set-based +MLPs exhibit in-context learning under in-distribution evaluations, but +transformers more closely emulate the performance of ordinary least squares +(OLS). Transformers also display better resilience to mild distribution shifts, +where set-based MLPs falter. However, under severe distribution shifts, both +models' in-context learning abilities diminish. +" +Exploring the Relationship Between Model Architecture and In-Context Learning Ability,Ivan Lee,http://arxiv.org/pdf/2310.08049v1.pdf,2023-10-12,['cs.lg'],2310.08049v1.pdf," What is the relationship between model architecture and the ability to +perform in-context learning? In this empirical study, we take the first steps +towards answering this question. In particular, we evaluate fifteen model +architectures across a suite of synthetic in-context learning tasks. The +selected architectures represent a broad range of paradigms, including +recurrent and convolution-based neural networks, transformers, and emerging +attention alternatives. We discover that all considered architectures can +perform in-context learning under certain conditions. However, contemporary +architectures are found to be the best performing, especially as task +complexity grows. Additionally, our follow-up experiments delve into various +factors that influence in-context learning. We observe varied sensitivities +among architectures with respect to hyperparameter settings. Our study of +training dynamics reveals that certain architectures exhibit a smooth, +progressive learning trajectory, while others demonstrate periods of stagnation +followed by abrupt mastery of the task. Finally, and somewhat surprisingly, we +find that several emerging attention alternatives are more robust in-context +learners than transformers; since such approaches have constant-sized memory +footprints at inference time, this result opens the future possibility of +scaling up in-context learning to vastly larger numbers of in-context examples. +" +What Can Transformers Learn In-Context? A Case Study of Simple Function Classes,Shivam Garg,http://arxiv.org/pdf/2208.01066v3.pdf,2022-08-01,"['cs.cl', 'cs.lg']",2208.01066v3.pdf," In-context learning refers to the ability of a model to condition on a prompt +sequence consisting of in-context examples (input-output pairs corresponding to +some task) along with a new query input, and generate the corresponding output. +Crucially, in-context learning happens only at inference time without any +parameter updates to the model. While large language models such as GPT-3 +exhibit some ability to perform in-context learning, it is unclear what the +relationship is between tasks on which this succeeds and what is present in the +training data. To make progress towards understanding in-context learning, we +consider the well-defined problem of training a model to in-context learn a +function class (e.g., linear functions): that is, given data derived from some +functions in the class, can we train a model to in-context learn ""most"" +functions from this class? We show empirically that standard Transformers can +be trained from scratch to perform in-context learning of linear functions -- +that is, the trained model is able to learn unseen linear functions from +in-context examples with performance comparable to the optimal least squares +estimator. In fact, in-context learning is possible even under two forms of +distribution shift: (i) between the training data of the model and +inference-time prompts, and (ii) between the in-context examples and the query +input during inference. We also show that we can train Transformers to +in-context learn more complex function classes -- namely sparse linear +functions, two-layer neural networks, and decision trees -- with performance +that matches or exceeds task-specific learning algorithms. Our code and models +are available at https://github.com/dtsip/in-context-learning . +" +"Structured Prompting: Scaling In-Context Learning to 1,000 Examples",Yaru Hao,http://arxiv.org/pdf/2212.06713v1.pdf,2022-12-13,['cs.cl'],2212.06713v1.pdf," Large language models have exhibited intriguing in-context learning +capability, achieving promising zero- and few-shot performance without updating +the parameters. However, conventional in-context learning is usually restricted +by length constraints, rendering it ineffective to absorb supervision from a +large number of examples. In order to go beyond few shots, we introduce +structured prompting that breaks the length limit and scales in-context +learning to thousands of examples. Specifically, demonstration examples are +separately encoded with well-designed position embeddings, and then they are +jointly attended by the test example using a rescaled attention mechanism. So +we can scale the number of exemplars with linear complexity instead of +quadratic complexity with respect to length. Experimental results on a diverse +set of tasks show that our approach improves end-task performance and reduces +evaluation variance over conventional in-context learning as the number of +demonstration examples increases. Code has been released at +https://aka.ms/structured-prompting. +" +Pre-Training to Learn in Context,Yuxian Gu,http://arxiv.org/pdf/2305.09137v1.pdf,2023-05-16,['cs.cl'],2305.09137v1.pdf," In-context learning, where pre-trained language models learn to perform tasks +from task examples and instructions in their contexts, has attracted much +attention in the NLP community. However, the ability of in-context learning is +not fully exploited because language models are not explicitly trained to learn +in context. To this end, we propose PICL (Pre-training for In-Context +Learning), a framework to enhance the language models' in-context learning +ability by pre-training the model on a large collection of ""intrinsic tasks"" in +the general plain-text corpus using the simple language modeling objective. +PICL encourages the model to infer and perform tasks by conditioning on the +contexts while maintaining task generalization of pre-trained models. We +evaluate the in-context learning performance of the model trained with PICL on +seven widely-used text classification datasets and the Super-NaturalInstrctions +benchmark, which contains 100+ NLP tasks formulated to text generation. Our +experiments show that PICL is more effective and task-generalizable than a +range of baselines, outperforming larger language models with nearly 4x +parameters. The code is publicly available at https://github.com/thu-coai/PICL. +" +EXnet: Efficient In-context Learning for Data-less Text classification,Debaditya Shome,http://arxiv.org/pdf/2305.14622v1.pdf,2023-05-24,"['cs.cl', 'cs.lg']",2305.14622v1.pdf," Large pre-trained language models (PLMs) have made significant progress in +encoding world knowledge and spawned a new set of learning paradigms including +zero-shot, few-shot, and in-context learning. Many language tasks can be +modeled as a set of prompts (for example, is this text about geography?) and +language models can provide binary answers, i.e., Yes or No. There is evidence +to suggest that the next-word prediction used by many PLMs does not align well +with zero-shot paradigms. Therefore, PLMs are fine-tuned as a +question-answering system. In-context learning extends zero-shot learning by +incorporating prompts and examples, resulting in increased task accuracy. Our +paper presents EXnet, a model specifically designed to perform in-context +learning without any limitations on the number of examples. We argue that +in-context learning is an effective method to increase task accuracy, and +providing examples facilitates cross-task generalization, especially when it +comes to text classification tasks. With extensive experiments, we show that +even our smallest model (15M parameters) generalizes to several unseen +classification tasks and domains. +" +RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models,Jie Huang,http://arxiv.org/pdf/2308.07922v1.pdf,2023-08-15,"['cs.cl', 'cs.ai', 'cs.lg']",2308.07922v1.pdf," In this paper, we investigate the in-context learning ability of +retrieval-augmented encoder-decoder language models. We first conduct a +comprehensive analysis of the state-of-the-art ATLAS model and identify its +limitations in in-context learning, primarily due to a mismatch between +pretraining and testing, as well as a restricted context length. To address +these issues, we propose RAVEN, a model that combines retrieval-augmented +masked language modeling and prefix language modeling. We further introduce +Fusion-in-Context Learning to enhance the few-shot performance by enabling the +model to leverage more in-context examples without requiring additional +training or model modifications. Through extensive experiments, we demonstrate +that RAVEN significantly outperforms ATLAS and achieves results comparable to +the most advanced language models in certain scenarios, despite having +substantially fewer parameters. Our work underscores the potential of +retrieval-augmented encoder-decoder language models for in-context learning and +encourages further research in this direction. +" +Understanding In-Context Learning from Repetitions,Jianhao Yan,http://arxiv.org/pdf/2310.00297v2.pdf,2023-09-30,['cs.cl'],2310.00297v2.pdf," This paper explores the elusive mechanism underpinning in-context learning in +Large Language Models (LLMs). Our work provides a novel perspective by +examining in-context learning via the lens of surface repetitions. We +quantitatively investigate the role of surface features in text generation, and +empirically establish the existence of \emph{token co-occurrence +reinforcement}, a principle that strengthens the relationship between two +tokens based on their contextual co-occurrences. By investigating the dual +impacts of these features, our research illuminates the internal workings of +in-context learning and expounds on the reasons for its failures. This paper +provides an essential contribution to the understanding of in-context learning +and its potential limitations, providing a fresh perspective on this exciting +capability. +" +In-Context Learning Dynamics with Random Binary Sequences,Eric J. Bigelow,http://arxiv.org/pdf/2310.17639v1.pdf,2023-10-26,"['cs.ai', 'cs.cl', 'cs.lg']",2310.17639v1.pdf," Large language models (LLMs) trained on huge corpora of text datasets +demonstrate complex, emergent capabilities, achieving state-of-the-art +performance on tasks they were not explicitly trained for. The precise nature +of LLM capabilities is often mysterious, and different prompts can elicit +different capabilities through in-context learning. We propose a Cognitive +Interpretability framework that enables us to analyze in-context learning +dynamics to understand latent concepts in LLMs underlying behavioral patterns. +This provides a more nuanced understanding than success-or-failure evaluation +benchmarks, but does not require observing internal activations as a +mechanistic interpretation of circuits would. Inspired by the cognitive science +of human randomness perception, we use random binary sequences as context and +study dynamics of in-context learning by manipulating properties of context +data, such as sequence length. In the latest GPT-3.5+ models, we find emergent +abilities to generate pseudo-random numbers and learn basic formal languages, +with striking in-context learning dynamics where model outputs transition +sharply from pseudo-random behaviors to deterministic repetition. +" +In-Context Learning with Many Demonstration Examples,Mukai Li,http://arxiv.org/pdf/2302.04931v1.pdf,2023-02-09,"['cs.cl', 'cs.ai']",2302.04931v1.pdf," Large pre-training language models (PLMs) have shown promising in-context +learning abilities. However, due to the backbone transformer architecture, +existing PLMs are bottlenecked by the memory and computational cost when +scaling up to a large context size, leaving instruction tuning and in-context +learning of many demonstration examples, as well as long-range language +modeling under-explored. In this study, we propose a long-range language model +EVALM based on an efficient transformer mechanism. EVALM is trained with 8k +tokens per batch line and can test up to 256k-lengthed contexts with +extrapolation, 128 times to the limit of existing PLMs (e.g. GPT3). Based on +EVALM, we scale up the size of examples efficiently in both instruction tuning +and in-context learning to explore the boundary of the benefits from more +annotated data. Experimental results on a diverse set of tasks show that EVALM +achieves 4.1% higher accuracy on average, and the average length of achieving +the best accuracy score over tasks is around 12k. We find that in-context +learning can achieve higher performance with more demonstrations under +many-shot instruction tuning (8k), and further extending the length of +instructions (16k) can further improve the upper bound of scaling in-context +learning. +" +The Learnability of In-Context Learning,Noam Wies,http://arxiv.org/pdf/2303.07895v1.pdf,2023-03-14,['cs.cl'],2303.07895v1.pdf," In-context learning is a surprising and important phenomenon that emerged +when modern language models were scaled to billions of learned parameters. +Without modifying a large language model's weights, it can be tuned to perform +various downstream natural language tasks simply by including concatenated +training examples of these tasks in its input. Though disruptive for many +practical applications of large language models, this emergent learning +paradigm is not well understood from a theoretical perspective. In this paper, +we propose a first-of-its-kind PAC based framework for in-context learnability, +and use it to provide the first finite sample complexity results for the +in-context learning setup. Our framework includes an initial pretraining phase, +which fits a function to the pretraining distribution, and then a second +in-context learning phase, which keeps this function constant and concatenates +training examples of the downstream task in its input. We use our framework in +order to prove that, under mild assumptions, when the pretraining distribution +is a mixture of latent tasks (a model often considered for natural language +pretraining), these tasks can be efficiently learned via in-context learning, +even though the model's weights are unchanged and the input significantly +diverges from the pretraining distribution. Our theoretical analysis reveals +that in this setting, in-context learning is more about identifying the task +than about learning it, a result which is in line with a series of recent +empirical findings. We hope that the in-context learnability framework +presented in this paper will facilitate future progress towards a deeper +understanding of this important new learning paradigm. +" +SINC: Self-Supervised In-Context Learning for Vision-Language Tasks,Yi-Syuan Chen,http://arxiv.org/pdf/2307.07742v2.pdf,2023-07-15,"['cs.cv', 'cs.ai']",2307.07742v2.pdf," Large Pre-trained Transformers exhibit an intriguing capacity for in-context +learning. Without gradient updates, these models can rapidly construct new +predictors from demonstrations presented in the inputs. Recent works promote +this ability in the vision-language domain by incorporating visual information +into large language models that can already make in-context predictions. +However, these methods could inherit issues in the language domain, such as +template sensitivity and hallucination. Also, the scale of these language +models raises a significant demand for computations, making learning and +operating these models resource-intensive. To this end, we raise a question: +``How can we enable in-context learning without relying on the intrinsic +in-context ability of large language models?"". To answer it, we propose a +succinct and general framework, Self-supervised IN-Context learning (SINC), +that introduces a meta-model to learn on self-supervised prompts consisting of +tailored demonstrations. The learned models can be transferred to downstream +tasks for making in-context predictions on-the-fly. Extensive experiments show +that SINC outperforms gradient-based methods in various vision-language tasks +under few-shot settings. Furthermore, the designs of SINC help us investigate +the benefits of in-context learning across different tasks, and the analysis +further reveals the essential components for the emergence of in-context +learning in the vision-language domain. +" +Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator,Hyuhng Joon Kim,http://arxiv.org/pdf/2206.08082v1.pdf,2022-06-16,['cs.cl'],2206.08082v1.pdf," Large-scale pre-trained language models (PLMs) are well-known for being +capable of solving a task simply by conditioning a few input-label pairs dubbed +demonstrations on a prompt without being explicitly tuned for the desired +downstream task. Such a process (i.e., in-context learning), however, naturally +leads to high reliance on the demonstrations which are usually selected from +external datasets. In this paper, we propose self-generated in-context learning +(SG-ICL), which generates demonstrations for in-context learning from PLM +itself to minimize the reliance on the external demonstration. We conduct +experiments on four different text classification tasks and show SG-ICL +significantly outperforms zero-shot learning and is generally worth +approximately 0.6 gold training samples. Moreover, our generated demonstrations +show more consistent performance with low variance compared to randomly +selected demonstrations from the training dataset. +" +Active Example Selection for In-Context Learning,Yiming Zhang,http://arxiv.org/pdf/2211.04486v1.pdf,2022-11-08,"['cs.cl', 'cs.ai']",2211.04486v1.pdf," With a handful of demonstration examples, large-scale language models show +strong capability to perform various tasks by in-context learning from these +examples, without any fine-tuning. We demonstrate that in-context learning +performance can be highly unstable across samples of examples, indicating the +idiosyncrasies of how language models acquire information. We formulate example +selection for in-context learning as a sequential decision problem, and propose +a reinforcement learning algorithm for identifying generalizable policies to +select demonstration examples. For GPT-2, our learned policies demonstrate +strong abilities of generalizing to unseen tasks in training, with a $5.8\%$ +improvement on average. Examples selected from our learned policies can even +achieve a small improvement on GPT-3 Ada. However, the improvement diminishes +on larger GPT-3 models, suggesting emerging capabilities of large language +models. +" +On the Compositional Generalization Gap of In-Context Learning,Arian Hosseini,http://arxiv.org/pdf/2211.08473v1.pdf,2022-11-15,"['cs.cl', 'cs.lg']",2211.08473v1.pdf," Pretrained large generative language models have shown great performance on +many tasks, but exhibit low compositional generalization abilities. Scaling +such models has been shown to improve their performance on various NLP tasks +even just by conditioning them on a few examples to solve the task without any +fine-tuning (also known as in-context learning). In this work, we look at the +gap between the in-distribution (ID) and out-of-distribution (OOD) performance +of such models in semantic parsing tasks with in-context learning. In the ID +settings, the demonstrations are from the same split (test or train) that the +model is being evaluated on, and in the OOD settings, they are from the other +split. We look at how the relative generalization gap of in-context learning +evolves as models are scaled up. We evaluate four model families, OPT, BLOOM, +CodeGen and Codex on three semantic parsing datasets, CFQ, SCAN and GeoQuery +with different number of exemplars, and observe a trend of decreasing relative +generalization gap as models are scaled up. +" +Bayesian Optimization of Catalysts With In-context Learning,Mayk Caldas Ramos,http://arxiv.org/pdf/2304.05341v1.pdf,2023-04-11,"['physics.chem-ph', 'cs.lg']",2304.05341v1.pdf," Large language models (LLMs) are able to do accurate classification with zero +or only a few examples (in-context learning). We show a prompting system that +enables regression with uncertainty for in-context learning with frozen LLM +(GPT-3, GPT-3.5, and GPT-4) models, allowing predictions without features or +architecture tuning. By incorporating uncertainty, our approach enables +Bayesian optimization for catalyst or molecule optimization using natural +language, eliminating the need for training or simulation. Here, we performed +the optimization using the synthesis procedure of catalysts to predict +properties. Working with natural language mitigates difficulty synthesizability +since the literal synthesis procedure is the model's input. We showed that +in-context learning could improve past a model context window (maximum number +of tokens the model can process at once) as data is gathered via example +selection, allowing the model to scale better. Although our method does not +outperform all baselines, it requires zero training, feature selection, and +minimal computing while maintaining satisfactory performance. We also find +Gaussian Process Regression on text embeddings is strong at Bayesian +optimization. The code is available in our GitHub repository: +https://github.com/ur-whitelab/BO-LIFT +" +In-Context Learning Unlocked for Diffusion Models,Zhendong Wang,http://arxiv.org/pdf/2305.01115v2.pdf,2023-05-01,['cs.cv'],2305.01115v2.pdf," We present Prompt Diffusion, a framework for enabling in-context learning in +diffusion-based generative models. Given a pair of task-specific example +images, such as depth from/to image and scribble from/to image, and a text +guidance, our model automatically understands the underlying task and performs +the same task on a new query image following the text guidance. To achieve +this, we propose a vision-language prompt that can model a wide range of +vision-language tasks and a diffusion model that takes it as input. The +diffusion model is trained jointly over six different tasks using these +prompts. The resulting Prompt Diffusion model is the first diffusion-based +vision-language foundation model capable of in-context learning. It +demonstrates high-quality in-context generation on the trained tasks and +generalizes effectively to new, unseen vision tasks with their respective +prompts. Our model also shows compelling text-guided image editing results. Our +framework aims to facilitate research into in-context learning for computer +vision. We share our code and pre-trained models at +https://github.com/Zhendong-Wang/Prompt-Diffusion. +" +Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation,Marius Mosbach,http://arxiv.org/pdf/2305.16938v2.pdf,2023-05-26,['cs.cl'],2305.16938v2.pdf," Few-shot fine-tuning and in-context learning are two alternative strategies +for task adaptation of pre-trained language models. Recently, in-context +learning has gained popularity over fine-tuning due to its simplicity and +improved out-of-domain generalization, and because extensive evidence shows +that fine-tuned models pick up on spurious correlations. Unfortunately, +previous comparisons of the two approaches were done using models of different +sizes. This raises the question of whether the observed weaker out-of-domain +generalization of fine-tuned models is an inherent property of fine-tuning or a +limitation of the experimental setup. In this paper, we compare the +generalization of few-shot fine-tuning and in-context learning to challenge +datasets, while controlling for the models used, the number of examples, and +the number of parameters, ranging from 125M to 30B. Our results show that +fine-tuned language models can in fact generalize well out-of-domain. We find +that both approaches generalize similarly; they exhibit large variation and +depend on properties such as model size and the number of examples, +highlighting that robust task adaptation remains a challenge. +" +Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning,Ruixiang Tang,http://arxiv.org/pdf/2305.17256v2.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.lg']",2305.17256v2.pdf," Large language models (LLMs) have recently shown great potential for +in-context learning, where LLMs learn a new task simply by conditioning on a +few input-label pairs (prompts). Despite their potential, our understanding of +the factors influencing end-task performance and the robustness of in-context +learning remains limited. This paper aims to bridge this knowledge gap by +investigating the reliance of LLMs on shortcuts or spurious correlations within +prompts. Through comprehensive experiments on classification and extraction +tasks, we reveal that LLMs are ""lazy learners"" that tend to exploit shortcuts +in prompts for downstream tasks. Additionally, we uncover a surprising finding +that larger models are more likely to utilize shortcuts in prompts during +inference. Our findings provide a new perspective on evaluating robustness in +in-context learning and pose new challenges for detecting and mitigating the +use of shortcuts in prompts. +" +Multi-Dimensional Evaluation of Text Summarization with In-Context Learning,Sameer Jain,http://arxiv.org/pdf/2306.01200v1.pdf,2023-06-01,['cs.cl'],2306.01200v1.pdf," Evaluation of natural language generation (NLG) is complex and +multi-dimensional. Generated text can be evaluated for fluency, coherence, +factuality, or any other dimensions of interest. Most frameworks that perform +such multi-dimensional evaluation require training on large manually or +synthetically generated datasets. In this paper, we study the efficacy of large +language models as multi-dimensional evaluators using in-context learning, +obviating the need for large training datasets. Our experiments show that +in-context learning-based evaluators are competitive with learned evaluation +frameworks for the task of text summarization, establishing state-of-the-art on +dimensions such as relevance and factual consistency. We then analyze the +effects of factors such as the selection and number of in-context examples on +performance. Finally, we study the efficacy of in-context learning based +evaluators in evaluating zero-shot summaries written by large language models +such as GPT-3. +" +Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Empirical Study,Zeping Min,http://arxiv.org/pdf/2307.06530v1.pdf,2023-07-13,"['cs.cl', 'cs.sd', 'eess.as']",2307.06530v1.pdf," This paper explores the integration of Large Language Models (LLMs) into +Automatic Speech Recognition (ASR) systems to improve transcription accuracy. +The increasing sophistication of LLMs, with their in-context learning +capabilities and instruction-following behavior, has drawn significant +attention in the field of Natural Language Processing (NLP). Our primary focus +is to investigate the potential of using an LLM's in-context learning +capabilities to enhance the performance of ASR systems, which currently face +challenges such as ambient noise, speaker accents, and complex linguistic +contexts. We designed a study using the Aishell-1 and LibriSpeech datasets, +with ChatGPT and GPT-4 serving as benchmarks for LLM capabilities. +Unfortunately, our initial experiments did not yield promising results, +indicating the complexity of leveraging LLM's in-context learning for ASR +applications. Despite further exploration with varied settings and models, the +corrected sentences from the LLMs frequently resulted in higher Word Error +Rates (WER), demonstrating the limitations of LLMs in speech applications. This +paper provides a detailed overview of these experiments, their results, and +implications, establishing that using LLMs' in-context learning capabilities to +correct potential errors in speech recognition transcriptions is still a +challenging task at the current stage. +" +ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought,Hanchong Zhang,http://arxiv.org/pdf/2310.17342v1.pdf,2023-10-26,['cs.cl'],2310.17342v1.pdf," Recently Large Language Models (LLMs) have been proven to have strong +abilities in various domains and tasks. We study the problem of prompt +designing in the text-to-SQL task and attempt to improve the LLMs' reasoning +ability when generating SQL queries. Besides the trivial few-shot in-context +learning setting, we design our chain-of-thought (CoT) prompt with a similar +method to schema linking. We provide a method named ACT-SQL to automatically +generate auto-CoT exemplars and thus the whole process doesn't need manual +labeling. Our approach is cost-saving since we only use the LLMs' API call once +when generating one SQL query. Furthermore, we extend our in-context learning +method to the multi-turn text-to-SQL task. The experiment results show that the +LLMs' performance can benefit from our ACT-SQL approach. Our approach achieves +SOTA performance on the Spider dev set among existing in-context learning +approaches. +" +COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning,Jing Pan,http://arxiv.org/pdf/2311.02248v1.pdf,2023-11-03,"['cs.cl', 'cs.ai', 'eess.as']",2311.02248v1.pdf," We present a data and cost efficient way of incorporating the speech modality +into a large language model (LLM). The resulting multi-modal LLM is a +COntextual Speech Model with Instruction-following/in-context-learning +Capabilities - COSMIC. Speech comprehension test question-answer (SQA) pairs +are generated using GPT-3.5 based on the speech transcriptions as a part of the +supervision for the instruction tuning. With fewer than 20M trainable +parameters and as little as 450 hours of English speech data for SQA +generation, COSMIC exhibits emergent instruction-following and in-context +learning capabilities in speech-to-text tasks. The model is able to follow the +given text instructions to generate text response even on the unseen EN$\to$X +speech-to-text translation (S2TT) task with zero-shot setting. We evaluate the +model's in-context learning via various tasks such as EN$\to$X S2TT and +few-shot domain adaptation. And instruction-following capabilities are +evaluated through a contextual biasing benchmark. Our results demonstrate the +efficacy of the proposed low cost recipe for building a speech LLM and that +with the new instruction-tuning data. +" +Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again,Bernal Jiménez Gutiérrez,http://arxiv.org/pdf/2203.08410v3.pdf,2022-03-16,"['cs.cl', 'cs.ir']",2203.08410v3.pdf," The strong few-shot in-context learning capability of large pre-trained +language models (PLMs) such as GPT-3 is highly appealing for application +domains such as biomedicine, which feature high and diverse demands of language +technologies but also high data annotation costs. In this paper, we present the +first systematic and comprehensive study to compare the few-shot performance of +GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on +two highly representative biomedical information extraction tasks, named entity +recognition and relation extraction. We follow the true few-shot setting to +avoid overestimating models' few-shot performance by model selection over a +large validation set. We also optimize GPT-3's performance with known +techniques such as contextual calibration and dynamic in-context example +retrieval. However, our results show that GPT-3 still significantly +underperforms compared to simply fine-tuning a smaller PLM. In addition, GPT-3 +in-context learning also yields smaller gains in accuracy when more training +data becomes available. Our in-depth analyses further reveal issues of the +in-context learning setting that may be detrimental to information extraction +tasks in general. Given the high cost of experimenting with GPT-3, we hope our +study provides guidance for biomedical researchers and practitioners towards +more promising directions such as fine-tuning small PLMs. +" +Exploring Effective Factors for Improving Visual In-Context Learning,Yanpeng Sun,http://arxiv.org/pdf/2304.04748v1.pdf,2023-04-10,['cs.cv'],2304.04748v1.pdf," The In-Context Learning (ICL) is to understand a new task via a few +demonstrations (aka. prompt) and predict new inputs without tuning the models. +While it has been widely studied in NLP, it is still a relatively new area of +research in computer vision. To reveal the factors influencing the performance +of visual in-context learning, this paper shows that prompt selection and +prompt fusion are two major factors that have a direct impact on the inference +performance of visual context learning. Prompt selection is the process of +identifying the most appropriate prompt or example to help the model understand +new tasks. This is important because providing the model with relevant prompts +can help it learn more effectively and efficiently. Prompt fusion involves +combining knowledge from different positions within the large-scale visual +model. By doing this, the model can leverage the diverse knowledge stored in +different parts of the model to improve its performance on new tasks. Based +these findings, we propose a simple framework prompt-SelF for visual in-context +learning. Specifically, we first use the pixel-level retrieval method to select +a suitable prompt, and then use different prompt fusion methods to activate all +the knowledge stored in the large-scale model, and finally ensemble the +prediction results obtained from different prompt fusion methods to obtain the +final prediction results. And we conduct extensive experiments on single-object +segmentation and detection tasks to demonstrate the effectiveness of +prompt-SelF. Remarkably, the prompt-SelF has outperformed OSLSM based +meta-learning in 1-shot segmentation for the first time. This indicated the +great potential of visual in-context learning. The source code and models will +be available at \url{https://github.com/syp2ysy/prompt-SelF}. +" +Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning,Yingcong Li,http://arxiv.org/pdf/2305.18869v2.pdf,2023-05-30,"['cs.lg', 'cs.ai', 'cs.cl']",2305.18869v2.pdf," Chain-of-thought (CoT) is a method that enables language models to handle +complex reasoning tasks by decomposing them into simpler steps. Despite its +success, the underlying mechanics of CoT are not yet fully understood. In an +attempt to shed light on this, our study investigates the impact of CoT on the +ability of transformers to in-context learn a simple to study, yet general +family of compositional functions: multi-layer perceptrons (MLPs). In this +setting, we find that the success of CoT can be attributed to breaking down +in-context learning of a compositional function into two distinct phases: +focusing on and filtering data related to each step of the composition and +in-context learning the single-step composition function. Through both +experimental and theoretical evidence, we demonstrate how CoT significantly +reduces the sample complexity of in-context learning (ICL) and facilitates the +learning of complex functions that non-CoT methods struggle with. Furthermore, +we illustrate how transformers can transition from vanilla in-context learning +to mastering a compositional function with CoT by simply incorporating +additional layers that perform the necessary data-filtering for CoT via the +attention mechanism. In addition to these test-time benefits, we show CoT helps +accelerate pretraining by learning shortcuts to represent complex functions and +filtering plays an important role in this process. These findings collectively +provide insights into the mechanics of CoT, inviting further investigation of +its role in complex reasoning tasks. +" +In-Context Learning through the Bayesian Prism,Kabir Ahuja,http://arxiv.org/pdf/2306.04891v1.pdf,2023-06-08,"['cs.lg', 'cs.cl']",2306.04891v1.pdf," In-context learning is one of the surprising and useful features of large +language models. How it works is an active area of research. Recently, stylized +meta-learning-like setups have been devised that train these models on a +sequence of input-output pairs $(x, f(x))$ from a function class using the +language modeling loss and observe generalization to unseen functions from the +same class. One of the main discoveries in this line of research has been that +for several problems such as linear regression, trained transformers learn +algorithms for learning functions in context. However, the inductive biases of +these models resulting in this behavior are not clearly understood. A model +with unlimited training data and compute is a Bayesian predictor: it learns the +pretraining distribution. It has been shown that high-capacity transformers +mimic the Bayesian predictor for linear regression. In this paper, we show +empirical evidence of transformers exhibiting the behavior of this ideal +learner across different linear and non-linear function classes. We also extend +the previous setups to work in the multitask setting and verify that +transformers can do in-context learning in this setup as well and the Bayesian +perspective sheds light on this setting also. Finally, via the example of +learning Fourier series, we study the inductive bias for in-context learning. +We find that in-context learning may or may not have simplicity bias depending +on the pretraining data distribution. +" +Explore In-Context Learning for 3D Point Cloud Understanding,Zhongbin Fang,http://arxiv.org/pdf/2306.08659v1.pdf,2023-06-14,['cs.cv'],2306.08659v1.pdf," With the rise of large-scale models trained on broad data, in-context +learning has become a new learning paradigm that has demonstrated significant +potential in natural language processing and computer vision tasks. Meanwhile, +in-context learning is still largely unexplored in the 3D point cloud domain. +Although masked modeling has been successfully applied for in-context learning +in 2D vision, directly extending it to 3D point clouds remains a formidable +challenge. In the case of point clouds, the tokens themselves are the point +cloud positions (coordinates) that are masked during inference. Moreover, +position embedding in previous works may inadvertently introduce information +leakage. To address these challenges, we introduce a novel framework, named +Point-In-Context, designed especially for in-context learning in 3D point +clouds, where both inputs and outputs are modeled as coordinates for each task. +Additionally, we propose the Joint Sampling module, carefully designed to work +in tandem with the general point sampling operator, effectively resolving the +aforementioned technical issues. We conduct extensive experiments to validate +the versatility and adaptability of our proposed methods in handling a wide +range of tasks. Furthermore, with a more effective prompt selection strategy, +our framework surpasses the results of individually trained models. +" +Scaling In-Context Demonstrations with Structured Attention,Tianle Cai,http://arxiv.org/pdf/2307.02690v1.pdf,2023-07-05,"['cs.cl', 'cs.ai', 'cs.lg']",2307.02690v1.pdf," The recent surge of large language models (LLMs) highlights their ability to +perform in-context learning, i.e., ""learning"" to perform a task from a few +demonstrations in the context without any parameter updates. However, their +capabilities of in-context learning are limited by the model architecture: 1) +the use of demonstrations is constrained by a maximum sentence length due to +positional embeddings; 2) the quadratic complexity of attention hinders users +from using more demonstrations efficiently; 3) LLMs are shown to be sensitive +to the order of the demonstrations. In this work, we tackle these challenges by +proposing a better architectural design for in-context learning. We propose +SAICL (Structured Attention for In-Context Learning), which replaces the +full-attention by a structured attention mechanism designed for in-context +learning, and removes unnecessary dependencies between individual +demonstrations, while making the model invariant to the permutation of +demonstrations. We evaluate SAICL in a meta-training framework and show that +SAICL achieves comparable or better performance than full attention while +obtaining up to 3.4x inference speed-up. SAICL also consistently outperforms a +strong Fusion-in-Decoder (FiD) baseline which processes each demonstration +independently. Finally, thanks to its linear nature, we demonstrate that SAICL +can easily scale to hundreds of demonstrations with continuous performance +gains with scaling. +" +DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning,Jing Xiong,http://arxiv.org/pdf/2310.02954v4.pdf,2023-10-04,['cs.cl'],2310.02954v4.pdf," Recent advances in natural language processing, primarily propelled by Large +Language Models (LLMs), have showcased their remarkable capabilities grounded +in in-context learning. A promising avenue for guiding LLMs in intricate +reasoning tasks involves the utilization of intermediate reasoning steps within +the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies +in the effective selection of exemplars for facilitating in-context learning. +In this study, we introduce a framework that leverages Dual Queries and +Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars +for in-context learning. Dual Queries first query LLM to obtain LLM-generated +knowledge such as CoT, then query the retriever to obtain the final exemplars +via both question and the knowledge. Moreover, for the second query, LoRe +employs dimensionality reduction techniques to refine exemplar selection, +ensuring close alignment with the input question's knowledge. Through extensive +experiments, we demonstrate that DQ-LoRe significantly outperforms prior +state-of-the-art methods in the automatic selection of exemplars for GPT-4, +enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further +reveals that DQ-LoRe consistently outperforms retrieval-based approaches in +terms of both performance and adaptability, especially in scenarios +characterized by distribution shifts. DQ-LoRe pushes the boundaries of +in-context learning and opens up new avenues for addressing complex reasoning +challenges. We will release the code soon. +" +OverPrompt: Enhancing ChatGPT Capabilities through an Efficient In-Context Learning Approach,Jiazheng Li,http://arxiv.org/pdf/2305.14973v1.pdf,2023-05-24,['cs.cl'],2305.14973v1.pdf," The exceptional performance of pre-trained large language models has +revolutionised various applications, but their adoption in production +environments is hindered by prohibitive costs and inefficiencies, particularly +when utilising long prompts. This paper proposes OverPrompt, an in-context +learning method aimed at improving LLM efficiency and performance by processing +multiple inputs in parallel. Evaluated across diverse datasets, OverPrompt +enhances task efficiency and integrates a diverse range of examples for +improved performance. Particularly, it amplifies fact-checking and sentiment +analysis tasks when supplemented with contextual information. Synthetic data +grouping further enhances performance, suggesting a viable approach for data +augmentation. +" +Crosslingual Retrieval Augmented In-context Learning for Bangla,Xiaoqian Li,http://arxiv.org/pdf/2311.00587v1.pdf,2023-11-01,['cs.cl'],2311.00587v1.pdf," The promise of Large Language Models (LLMs) in Natural Language Processing +has often been overshadowed by their limited performance in low-resource +languages such as Bangla. To address this, our paper presents a pioneering +approach that utilizes cross-lingual retrieval augmented in-context learning. +By strategically sourcing semantically similar prompts from high-resource +language, we enable multilingual pretrained language models (MPLMs), especially +the generative model BLOOMZ, to successfully boost performance on Bangla tasks. +Our extensive evaluation highlights that the cross-lingual retrieval augmented +prompts bring steady improvements to MPLMs over the zero-shot performance. +" +Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations,Kang Min Yoo,http://arxiv.org/pdf/2205.12685v2.pdf,2022-05-25,"['cs.cl', 'cs.ai', 'cs.lg']",2205.12685v2.pdf," Despite recent explosion of interests in in-context learning, the underlying +mechanism and the precise impact of the quality of demonstrations remain +elusive. Intuitively, ground-truth labels should have as much impact in +in-context learning (ICL) as supervised learning, but recent work reported that +the input-label correspondence is significantly less important than previously +thought. Intrigued by this counter-intuitive observation, we re-examine the +importance of ground-truth labels in in-context learning. With the introduction +of two novel metrics, namely Label-Correctness Sensitivity and Ground-truth +Label Effect Ratio (GLER), we were able to conduct quantifiable analysis on the +impact of ground-truth label demonstrations. Through extensive analyses, we +find that the correct input-label mappings can have varying impacts on the +downstream in-context learning performances, depending on the experimental +configuration. Through additional studies, we identify key components, such as +the verbosity of prompt templates and the language model size, as the +controlling factor to achieve more noise-resilient ICL. +" +In-context Learning and Induction Heads,Catherine Olsson,http://arxiv.org/pdf/2209.11895v1.pdf,2022-09-24,['cs.lg'],2209.11895v1.pdf," ""Induction heads"" are attention heads that implement a simple algorithm to +complete token sequences like [A][B] ... [A] -> [B]. In this work, we present +preliminary and indirect evidence for a hypothesis that induction heads might +constitute the mechanism for the majority of all ""in-context learning"" in large +transformer models (i.e. decreasing loss at increasing token indices). We find +that induction heads develop at precisely the same point as a sudden sharp +increase in in-context learning ability, visible as a bump in the training +loss. We present six complementary lines of evidence, arguing that induction +heads may be the mechanistic source of general in-context learning in +transformer models of any size. For small attention-only models, we present +strong, causal evidence; for larger models with MLPs, we present correlational +evidence. +" +Transformers learn in-context by gradient descent,Johannes von Oswald,http://arxiv.org/pdf/2212.07677v2.pdf,2022-12-15,"['cs.lg', 'cs.ai', 'cs.cl']",2212.07677v2.pdf," At present, the mechanisms of in-context learning in Transformers are not +well understood and remain mostly an intuition. In this paper, we suggest that +training Transformers on auto-regressive objectives is closely related to +gradient-based meta-learning formulations. We start by providing a simple +weight construction that shows the equivalence of data transformations induced +by 1) a single linear self-attention layer and by 2) gradient-descent (GD) on a +regression loss. Motivated by that construction, we show empirically that when +training self-attention-only Transformers on simple regression tasks either the +models learned by GD and Transformers show great similarity or, remarkably, the +weights found by optimization match the construction. Thus we show how trained +Transformers become mesa-optimizers i.e. learn models by gradient descent in +their forward pass. This allows us, at least in the domain of regression +problems, to mechanistically understand the inner workings of in-context +learning in optimized Transformers. Building on this insight, we furthermore +identify how Transformers surpass the performance of plain gradient descent by +learning an iterative curvature correction and learn linear models on deep data +representations to solve non-linear regression tasks. Finally, we discuss +intriguing parallels to a mechanism identified to be crucial for in-context +learning termed induction-head (Olsson et al., 2022) and show how it could be +understood as a specific case of in-context learning by gradient descent +learning within Transformers. Code to reproduce the experiments can be found at +https://github.com/google-research/self-organising-systems/tree/master/transformers_learn_icl_by_gd . +" +What Makes Good Examples for Visual In-Context Learning?,Yuanhan Zhang,http://arxiv.org/pdf/2301.13670v2.pdf,2023-01-31,['cs.cv'],2301.13670v2.pdf," Large-scale models trained on broad data have recently become the mainstream +architecture in computer vision due to their strong generalization performance. +In this paper, the main focus is on an emergent ability in large vision models, +known as in-context learning, which allows inference on unseen tasks by +conditioning on in-context examples (a.k.a.~prompt) without updating the model +parameters. This concept has been well-known in natural language processing but +has only been studied very recently for large vision models. We for the first +time provide a comprehensive investigation on the impact of in-context examples +in computer vision, and find that the performance is highly sensitive to the +choice of in-context examples. To overcome the problem, we propose a prompt +retrieval framework to automate the selection of in-context examples. +Specifically, we present (1) an unsupervised prompt retrieval method based on +nearest example search using an off-the-shelf model, and (2) a supervised +prompt retrieval method, which trains a neural network to choose examples that +directly maximize in-context learning performance. The results demonstrate that +our methods can bring non-trivial improvements to visual in-context learning in +comparison to the commonly-used random selection. +" +Compositional Exemplars for In-context Learning,Jiacheng Ye,http://arxiv.org/pdf/2302.05698v3.pdf,2023-02-11,"['cs.cl', 'cs.ai', 'cs.lg']",2302.05698v3.pdf," Large pretrained language models (LMs) have shown impressive In-Context +Learning (ICL) ability, where the model learns to do an unseen task via a +prompt consisting of input-output examples as the demonstration, without any +parameter updates. The performance of ICL is highly dominated by the quality of +the selected in-context examples. However, previous selection methods are +mostly based on simple heuristics, leading to sub-optimal performance. In this +work, we formulate in-context example selection as a subset selection problem. +We propose CEIL (Compositional Exemplars for In-context Learning), which is +instantiated by Determinantal Point Processes (DPPs) to model the interaction +between the given input and in-context examples, and optimized through a +carefully-designed contrastive learning objective to obtain preference from +LMs. We validate CEIL on 12 classification and generation datasets from 7 +distinct NLP tasks, including sentiment analysis, paraphrase detection, natural +language inference, commonsense reasoning, open-domain question answering, code +generation, and semantic parsing. Extensive experiments demonstrate not only +the state-of-the-art performance but also the transferability and +compositionality of CEIL, shedding new light on effective and efficient +in-context learning. Our code is released at +https://github.com/HKUNLP/icl-ceil. +" +ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction,Jiabang He,http://arxiv.org/pdf/2303.05063v4.pdf,2023-03-09,['cs.cl'],2303.05063v4.pdf," Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated +remarkable results in various natural language processing (NLP) tasks with +in-context learning, which involves inference based on a few demonstration +examples. Despite their successes in NLP tasks, no investigation has been +conducted to assess the ability of LLMs to perform document information +extraction (DIE) using in-context learning. Applying LLMs to DIE poses two +challenges: the modality and task gap. To this end, we propose a simple but +effective in-context learning framework called ICL-D3IE, which enables LLMs to +perform DIE with different types of demonstration examples. Specifically, we +extract the most difficult and distinct segments from hard training documents +as hard demonstrations for benefiting all test instances. We design +demonstrations describing relationships that enable LLMs to understand +positional relationships. We introduce formatting demonstrations for easy +answer extraction. Additionally, the framework improves diverse demonstrations +by updating them iteratively. Our experiments on three widely used benchmark +datasets demonstrate that the ICL-D3IE framework enables Davinci-003/ChatGPT to +achieve superior performance when compared to previous pre-trained methods +fine-tuned with full training in both the in-distribution (ID) setting and in +the out-of-distribution (OOD) setting. Code is available at +https://github.com/MAEHCM/ICL-D3IE. +" +The Closeness of In-Context Learning and Weight Shifting for Softmax Regression,Shuai Li,http://arxiv.org/pdf/2304.13276v1.pdf,2023-04-26,"['cs.cl', 'cs.lg']",2304.13276v1.pdf," Large language models (LLMs) are known for their exceptional performance in +natural language processing, making them highly effective in many human +life-related or even job-related tasks. The attention mechanism in the +Transformer architecture is a critical component of LLMs, as it allows the +model to selectively focus on specific input parts. The softmax unit, which is +a key part of the attention mechanism, normalizes the attention scores. Hence, +the performance of LLMs in various NLP tasks depends significantly on the +crucial role played by the attention mechanism with the softmax unit. + In-context learning, as one of the celebrated abilities of recent LLMs, is an +important concept in querying LLMs such as ChatGPT. Without further parameter +updates, Transformers can learn to predict based on few in-context examples. +However, the reason why Transformers becomes in-context learners is not well +understood. Recently, several works [ASA+22,GTLV22,ONR+22] have studied the +in-context learning from a mathematical perspective based on a linear +regression formulation $\min_x\| Ax - b \|_2$, which show Transformers' +capability of learning linear functions in context. + In this work, we study the in-context learning based on a softmax regression +formulation $\min_{x} \| \langle \exp(Ax), {\bf 1}_n \rangle^{-1} \exp(Ax) - b +\|_2$ of Transformer's attention mechanism. We show the upper bounds of the +data transformations induced by a single self-attention layer and by +gradient-descent on a $\ell_2$ regression loss for softmax prediction function, +which imply that when training self-attention-only Transformers for fundamental +regression tasks, the models learned by gradient-descent and Transformers show +great similarity. +" +MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning,Haozhe Zhao,http://arxiv.org/pdf/2309.07915v2.pdf,2023-09-14,"['cs.cl', 'cs.ai', 'cs.cv']",2309.07915v2.pdf," Since the resurgence of deep learning, vision-language models (VLMs) enhanced +by large language models (LLMs) have grown exponentially in popularity. +However, while LLMs can utilize extensive background knowledge and task +information with in-context learning, most VLMs still struggle with +understanding complex multi-modal prompts with multiple images, making VLMs +less effective in downstream vision-language tasks. In this paper, we address +the limitation above by 1) introducing MMICL, a new approach to allow the VLM +to deal with multi-modal inputs efficiently; 2) proposing a novel context +scheme to augment the in-context learning ability of the VLM; 3) constructing +the Multi-modal In-Context Learning (MIC) dataset, designed to enhance the +VLM's ability to understand complex multi-modal prompts. Our experiments +confirm that MMICL achieves new state-of-the-art zero-shot performance on a +wide range of general vision-language tasks, especially for complex benchmarks, +including MME and MMBench. Our analysis demonstrates that MMICL effectively +tackles the challenge of complex multi-modal prompt understanding and emerges +the impressive ICL ability. Furthermore, we observe that MMICL successfully +alleviates language bias in VLMs, a common issue for VLMs that often leads to +hallucination when faced with extensive textual context. +" +Visual In-Context Learning for Few-Shot Eczema Segmentation,Neelesh Kumar,http://arxiv.org/pdf/2309.16656v1.pdf,2023-09-28,"['cs.cv', 'cs.lg']",2309.16656v1.pdf," Automated diagnosis of eczema from digital camera images is crucial for +developing applications that allow patients to self-monitor their recovery. An +important component of this is the segmentation of eczema region from such +images. Current methods for eczema segmentation rely on deep neural networks +such as convolutional (CNN)-based U-Net or transformer-based Swin U-Net. While +effective, these methods require high volume of annotated data, which can be +difficult to obtain. Here, we investigate the capabilities of visual in-context +learning that can perform few-shot eczema segmentation with just a handful of +examples and without any need for retraining models. Specifically, we propose a +strategy for applying in-context learning for eczema segmentation with a +generalist vision model called SegGPT. When benchmarked on a dataset of +annotated eczema images, we show that SegGPT with just 2 representative example +images from the training dataset performs better (mIoU: 36.69) than a CNN U-Net +trained on 428 images (mIoU: 32.60). We also discover that using more number of +examples for SegGPT may in fact be harmful to its performance. Our result +highlights the importance of visual in-context learning in developing faster +and better solutions to skin imaging tasks. Our result also paves the way for +developing inclusive solutions that can cater to minorities in the demographics +who are typically heavily under-represented in the training data. +" +Learning To Retrieve Prompts for In-Context Learning,Ohad Rubin,http://arxiv.org/pdf/2112.08633v2.pdf,2021-12-16,"['cs.cl', 'cs.lg']",2112.08633v2.pdf," In-context learning is a recent paradigm in natural language understanding, +where a large pre-trained language model (LM) observes a test instance and a +few training examples as its input, and directly decodes the output without any +update to its parameters. However, performance has been shown to strongly +depend on the selected training examples (termed prompt). In this work, we +propose an efficient method for retrieving prompts for in-context learning +using annotated data and a LM. Given an input-output pair, we estimate the +probability of the output given the input and a candidate training example as +the prompt, and label training examples as positive or negative based on this +probability. We then train an efficient dense retriever from this data, which +is used to retrieve training examples as prompts at test time. We evaluate our +approach on three sequence-to-sequence tasks where language utterances are +mapped to meaning representations, and find that it substantially outperforms +prior work and multiple baselines across the board. +" +Semantic-Oriented Unlabeled Priming for Large-Scale Language Models,Yanchen Liu,http://arxiv.org/pdf/2202.06133v1.pdf,2022-02-12,['cs.cl'],2202.06133v1.pdf," Due to the high costs associated with finetuning large language models, +various recent works propose to adapt them to specific tasks without any +parameter updates through in-context learning. Unfortunately, for in-context +learning there is currently no way to leverage unlabeled data, which is often +much easier to obtain in large quantities than labeled examples. In this work, +we therefore investigate ways to make use of unlabeled examples to improve the +zero-shot performance of pretrained language models without any finetuning: We +introduce Semantic-Oriented Unlabeled Priming (SOUP), a method that classifies +examples by retrieving semantically similar unlabeled examples, assigning +labels to them in a zero-shot fashion, and then using them for in-context +learning. We also propose bag-of-contexts priming, a new priming strategy that +is more suitable for our setting and enables the usage of more examples than +fit into the context window. +" +Complementary Explanations for Effective In-Context Learning,Xi Ye,http://arxiv.org/pdf/2211.13892v2.pdf,2022-11-25,['cs.cl'],2211.13892v2.pdf," Large language models (LLMs) have exhibited remarkable capabilities in +learning from explanations in prompts, but there has been limited understanding +of exactly how these explanations function or why they are effective. This work +aims to better understand the mechanisms by which explanations are used for +in-context learning. We first study the impact of two different factors on the +performance of prompts with explanations: the computation trace (the way the +solution is decomposed) and the natural language used to express the prompt. By +perturbing explanations on three controlled tasks, we show that both factors +contribute to the effectiveness of explanations. We further study how to form +maximally effective sets of explanations for solving a given test query. We +find that LLMs can benefit from the complementarity of the explanation set: +diverse reasoning skills shown by different exemplars can lead to better +performance. Therefore, we propose a maximal marginal relevance-based exemplar +selection approach for constructing exemplar sets that are both relevant as +well as complementary, which successfully improves the in-context learning +performance across three real-world tasks on multiple LLMs. +" +Diverse Demonstrations Improve In-context Compositional Generalization,Itay Levy,http://arxiv.org/pdf/2212.06800v3.pdf,2022-12-13,['cs.cl'],2212.06800v3.pdf," In-context learning has shown great success in i.i.d semantic parsing splits, +where the training and test sets are drawn from the same distribution. In this +setup, models are typically prompted with demonstrations that are similar to +the input utterance. However, in the setup of compositional generalization, +where models are tested on outputs with structures that are absent from the +training set, selecting similar demonstrations is insufficient, as often no +example will be similar enough to the input. In this work, we propose a method +to select diverse demonstrations that aims to collectively cover all of the +structures required in the output program, in order to encourage the model to +generalize to new structures from these demonstrations. We empirically show +that combining diverse demonstrations with in-context learning substantially +improves performance across three compositional generalization semantic parsing +datasets in the pure in-context learning setup and when combined with +finetuning. +" +The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning,Hanlin Zhang,http://arxiv.org/pdf/2212.08686v1.pdf,2022-12-16,['cs.cl'],2212.08686v1.pdf," Pre-trained language models (LMs) have shown remarkable reasoning performance +using explanations (or ``chain-of-thought'' (CoT)) for in-context learning. On +the other hand, these reasoning tasks are usually presumed to be more +approachable for symbolic programming. To make progress towards understanding +in-context learning, we curate synthetic datasets containing equivalent +(natural, symbolic) data pairs, where symbolic examples contain first-order +logic rules and predicates from knowledge bases (KBs). Then we revisit +neuro-symbolic approaches and use Language Models as Logic Programmer (LMLP) +that learns from demonstrations containing logic rules and corresponding +examples to iteratively reason over KBs, recovering Prolog's backward chaining +algorithm. Comprehensive experiments are included to systematically compare +LMLP with CoT in deductive reasoning settings, showing that LMLP enjoys more +than 25% higher accuracy than CoT on length generalization benchmarks even with +fewer parameters. +" +Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering,Zhiyong Wu,http://arxiv.org/pdf/2212.10375v2.pdf,2022-12-20,"['cs.cl', 'cs.ai']",2212.10375v2.pdf," Despite the surprising few-shot performance of in-context learning (ICL), it +is still a common practice to randomly sample examples to serve as context. +This paper advocates a new principle for ICL: self-adaptive in-context +learning. The self-adaption mechanism is introduced to help each sample find an +in-context example permutation (i.e., selection and ordering) that can derive +the correct prediction, thus maximizing performance. To validate the +effectiveness of self-adaptive ICL, we propose a general select-then-rank +framework and instantiate it with new selection and ranking algorithms. Upon +extensive evaluation on eight different NLP datasets, our self-adaptive ICL +method achieves a 40% relative improvement over the common practice setting. +Further analysis reveals the enormous potential of self-adaptive ICL that it +might be able to close the gap between ICL and finetuning given more advanced +algorithms. Our code is released to facilitate future research in this area: +https://github.com/Shark-NLP/self-adaptive-ICL +" +Privacy-Preserving In-Context Learning for Large Language Models,Tong Wu,http://arxiv.org/pdf/2305.01639v2.pdf,2023-05-02,"['cs.lg', 'cs.ai', 'cs.cr']",2305.01639v2.pdf," In-context learning (ICL) is an important capability of Large Language Models +(LLMs), enabling these models to dynamically adapt based on specific, +in-context exemplars, thereby improving accuracy and relevance. However, LLM's +responses may leak the sensitive private information contained in in-context +exemplars. To address this challenge, we propose Differentially Private +In-context Learning (DP-ICL), a general paradigm for privatizing ICL tasks. The +key idea for DP-ICL paradigm is generating differentially private responses +through a noisy consensus among an ensemble of LLM's responses based on +disjoint exemplar sets. Based on the general paradigm of DP-ICL, we instantiate +several techniques showing how to privatize ICL for text classification and +language generation. We evaluate DP-ICL on four text classification benchmarks +and two language generation tasks, and our empirical results show that DP-ICL +achieves a strong utility-privacy tradeoff. +" +In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models,Suzanna Sia,http://arxiv.org/pdf/2305.03573v1.pdf,2023-05-05,"['cs.cl', 'cs.ai']",2305.03573v1.pdf," The phenomena of in-context learning has typically been thought of as +""learning from examples"". In this work which focuses on Machine Translation, we +present a perspective of in-context learning as the desired generation task +maintaining coherency with its context, i.e., the prompt examples. We first +investigate randomly sampled prompts across 4 domains, and find that +translation performance improves when shown in-domain prompts. Next, we +investigate coherency for the in-domain setting, which uses prompt examples +from a moving window. We study this with respect to other factors that have +previously been identified in the literature such as length, surface similarity +and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, +Bloom3B, XGLM2.9B), and three translation directions +(\texttt{en}$\rightarrow$\{\texttt{pt, de, fr}\}) suggest that the long-term +coherency of the prompts and the test sentence is a good indicator of +downstream translation performance. In doing so, we demonstrate the efficacy of +In-context Machine Translation for on-the-fly adaptation. +" +Small Models are Valuable Plug-ins for Large Language Models,Canwen Xu,http://arxiv.org/pdf/2305.08848v1.pdf,2023-05-15,"['cs.cl', 'cs.ai', 'cs.lg']",2305.08848v1.pdf," Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their +weights are often publicly unavailable and their immense sizes make the models +difficult to be tuned with common hardware. As a result, effectively tuning +these models with large-scale supervised data can be challenging. As an +alternative, In-Context Learning (ICL) can only use a small number of +supervised examples due to context length limits. In this paper, we propose +Super In-Context Learning (SuperICL) which allows black-box LLMs to work with +locally fine-tuned smaller models, resulting in superior performance on +supervised tasks. Our experiments demonstrate that SuperICL can improve +performance beyond state-of-the-art fine-tuned models while addressing the +instability problem of in-context learning. Furthermore, SuperICL can enhance +the capabilities of smaller models, such as multilinguality and +interpretability. +" +ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning,Jingyuan Selena She,http://arxiv.org/pdf/2305.19426v1.pdf,2023-05-30,"['cs.cl', 'cs.lg']",2305.19426v1.pdf," A number of recent benchmarks seek to assess how well models handle natural +language negation. However, these benchmarks lack the controlled example +paradigms that would allow us to infer whether a model had learned how negation +morphemes semantically scope. To fill these analytical gaps, we present the +Scoped Negation NLI (ScoNe-NLI) benchmark, which contains contrast sets of six +examples with up to two negations where either zero, one, or both negative +morphemes affect the NLI label. We use ScoNe-NLI to assess fine-tuning and +in-context learning strategies. We find that RoBERTa and DeBERTa models solve +ScoNe-NLI after many shot fine-tuning. For in-context learning, we test +InstructGPT models and find that most prompt strategies are not successful, +including those using step-by-step reasoning. To better understand this result, +we extend ScoNe with ScoNe-NLG, a sentence completion test set that embeds +negation reasoning in short narratives. Here, InstructGPT is successful, which +reveals the model can correctly reason about negation, but struggles to do so +on prompt-adapted NLI examples outside of its core pretraining regime. +" +GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models,Pawan Kumar Rajpoot,http://arxiv.org/pdf/2306.17519v2.pdf,2023-06-30,['cs.cl'],2306.17519v2.pdf," Relation extraction (RE) is a crucial task in natural language processing +(NLP) that aims to identify and classify relationships between entities +mentioned in text. In the financial domain, relation extraction plays a vital +role in extracting valuable information from financial documents, such as news +articles, earnings reports, and company filings. This paper describes our +solution to relation extraction on one such dataset REFinD. The dataset was +released along with shared task as a part of the Fourth Workshop on Knowledge +Discovery from Unstructured Data in Financial Services, co-located with SIGIR +2023. In this paper, we employed OpenAI models under the framework of +in-context learning (ICL). We utilized two retrieval strategies to find top K +relevant in-context learning demonstrations / examples from training data for a +given test example. The first retrieval mechanism, we employed, is a +learning-free dense retriever and the other system is a learning-based +retriever. We were able to achieve 3rd rank overall. Our best F1-score is +0.718. +" +Code-Style In-Context Learning for Knowledge-Based Question Answering,Zhijie Nie,http://arxiv.org/pdf/2309.04695v1.pdf,2023-09-09,"['cs.cl', 'cs.ai']",2309.04695v1.pdf," Current methods for Knowledge-Based Question Answering (KBQA) usually rely on +complex training techniques and model frameworks, leading to many limitations +in practical applications. Recently, the emergence of In-Context Learning (ICL) +capabilities in Large Language Models (LLMs) provides a simple and +training-free semantic parsing paradigm for KBQA: Given a small number of +questions and their labeled logical forms as demo examples, LLMs can understand +the task intent and generate the logic form for a new question. However, +current powerful LLMs have little exposure to logic forms during pre-training, +resulting in a high format error rate. To solve this problem, we propose a +code-style in-context learning method for KBQA, which converts the generation +process of unfamiliar logical form into the more familiar code generation +process for LLMs. Experimental results on three mainstream datasets show that +our method dramatically mitigated the formatting error problem in generating +logic forms while realizing a new SOTA on WebQSP, GrailQA, and GraphQ under the +few-shot setting. +" +Can Whisper perform speech-based in-context learning,Siyin Wang,http://arxiv.org/pdf/2309.07081v1.pdf,2023-09-13,"['eess.as', 'cs.cl', 'cs.sd']",2309.07081v1.pdf," This paper investigates the in-context learning abilities of the Whisper +automatic speech recognition (ASR) models released by OpenAI. A novel +speech-based in-context learning (SICL) approach is proposed for test-time +adaptation, which can reduce the word error rates (WERs) with only a small +number of labelled speech samples without gradient descent. Language-level +adaptation experiments using Chinese dialects showed that when applying SICL to +isolated word ASR, consistent and considerable relative WER reductions can be +achieved using Whisper models of any size on two dialects, which is on average +32.3%. A k-nearest-neighbours-based in-context example selection technique can +be applied to further improve the efficiency of SICL, which can increase the +average relative WER reduction to 36.4%. The findings are verified using +speaker adaptation or continuous speech recognition tasks, and both achieved +considerable relative WER reductions. Detailed quantitative analyses are also +provided to shed light on SICL's adaptability to phonological variances and +dialect-specific lexical nuances. +" +ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer,Arkadiy Saakyan,http://arxiv.org/pdf/2309.08583v1.pdf,2023-09-15,['cs.cl'],2309.08583v1.pdf," While state-of-the-art language models excel at the style transfer task, +current work does not address explainability of style transfer systems. +Explanations could be generated using large language models such as GPT-3.5 and +GPT-4, but the use of such complex systems is inefficient when smaller, widely +distributed, and transparent alternatives are available. We propose a framework +to augment and improve a formality style transfer dataset with explanations via +model distillation from ChatGPT. To further refine the generated explanations, +we propose a novel way to incorporate scarce expert human feedback using +in-context learning (ICLEF: In-Context Learning from Expert Feedback) by +prompting ChatGPT to act as a critic to its own outputs. We use the resulting +dataset of 9,960 explainable formality style transfer instances (e-GYAFC) to +show that current openly distributed instruction-tuned models (and, in some +settings, ChatGPT) perform poorly on the task, and that fine-tuning on our +high-quality dataset leads to significant improvements as shown by automatic +evaluation. In human evaluation, we show that models much smaller than ChatGPT +fine-tuned on our data align better with expert preferences. Finally, we +discuss two potential applications of models fine-tuned on the explainable +style transfer task: interpretable authorship verification and interpretable +adversarial attacks on AI-generated text detectors. +" +SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation,Zhehuai Chen,http://arxiv.org/pdf/2310.09424v1.pdf,2023-10-13,"['cs.cl', 'cs.hc', 'cs.sd', 'eess.as', '68t10', 'i.2.7']",2310.09424v1.pdf," We present a novel Speech Augmented Language Model (SALM) with {\em +multitask} and {\em in-context} learning capabilities. SALM comprises a frozen +text LLM, a audio encoder, a modality adapter module, and LoRA layers to +accommodate speech input and associated task instructions. The unified SALM not +only achieves performance on par with task-specific Conformer baselines for +Automatic Speech Recognition (ASR) and Speech Translation (AST), but also +exhibits zero-shot in-context learning capabilities, demonstrated through +keyword-boosting task for ASR and AST. Moreover, {\em speech supervised +in-context training} is proposed to bridge the gap between LLM training and +downstream speech tasks, which further boosts the in-context learning ability +of speech-to-text models. Proposed model is open-sourced via NeMo toolkit. +" +Utilising a Large Language Model to Annotate Subject Metadata: A Case Study in an Australian National Research Data Catalogue,Shiwei Zhang,http://arxiv.org/pdf/2310.11318v1.pdf,2023-10-17,"['cs.cl', 'cs.ai']",2310.11318v1.pdf," In support of open and reproducible research, there has been a rapidly +increasing number of datasets made available for research. As the availability +of datasets increases, it becomes more important to have quality metadata for +discovering and reusing them. Yet, it is a common issue that datasets often +lack quality metadata due to limited resources for data curation. Meanwhile, +technologies such as artificial intelligence and large language models (LLMs) +are progressing rapidly. Recently, systems based on these technologies, such as +ChatGPT, have demonstrated promising capabilities for certain data curation +tasks. This paper proposes to leverage LLMs for cost-effective annotation of +subject metadata through the LLM-based in-context learning. Our method employs +GPT-3.5 with prompts designed for annotating subject metadata, demonstrating +promising performance in automatic metadata annotation. However, models based +on in-context learning cannot acquire discipline-specific rules, resulting in +lower performance in several categories. This limitation arises from the +limited contextual information available for subject inference. To the best of +our knowledge, we are introducing, for the first time, an in-context learning +method that harnesses large language models for automated subject metadata +annotation. +" +Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks,Yifan Wang,http://arxiv.org/pdf/2311.01949v1.pdf,2023-11-03,['cs.cl'],2311.01949v1.pdf," In-context learning (ICL) ability has emerged with the increasing scale of +large language models (LLMs), enabling them to learn input-label mappings from +demonstrations and perform well on downstream tasks. However, under the +standard ICL setting, LLMs may sometimes neglect query-related information in +demonstrations, leading to incorrect predictions. To address this limitation, +we propose a new paradigm called Hint-enhanced In-Context Learning (HICL) to +explore the power of ICL in open-domain question answering, an important form +in knowledge-intensive tasks. HICL leverages LLMs' reasoning ability to extract +query-related knowledge from demonstrations, then concatenates the knowledge to +prompt LLMs in a more explicit way. Furthermore, we track the source of this +knowledge to identify specific examples, and introduce a Hint-related Example +Retriever (HER) to select informative examples for enhanced demonstrations. We +evaluate HICL with HER on 3 open-domain QA benchmarks, and observe average +performance gains of 2.89 EM score and 2.52 F1 score on gpt-3.5-turbo, 7.62 EM +score and 7.27 F1 score on LLaMA-2-Chat-7B compared with standard setting. +" +Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?,Sewon Min,http://arxiv.org/pdf/2202.12837v2.pdf,2022-02-25,"['cs.cl', 'cs.ai']",2202.12837v2.pdf," Large language models (LMs) are able to in-context learn -- perform a new +task via inference alone by conditioning on a few input-label pairs +(demonstrations) and making predictions for new inputs. However, there has been +little understanding of how the model learns and which aspects of the +demonstrations contribute to end task performance. In this paper, we show that +ground truth demonstrations are in fact not required -- randomly replacing +labels in the demonstrations barely hurts performance on a range of +classification and multi-choce tasks, consistently over 12 different models +including GPT-3. Instead, we find that other aspects of the demonstrations are +the key drivers of end task performance, including the fact that they provide a +few examples of (1) the label space, (2) the distribution of the input text, +and (3) the overall format of the sequence. Together, our analysis provides a +new way of understanding how and why in-context learning works, while opening +up new questions about how much can be learned from large language models +through inference alone. +" +Can Foundation Models Help Us Achieve Perfect Secrecy?,Simran Arora,http://arxiv.org/pdf/2205.13722v2.pdf,2022-05-27,"['cs.lg', 'cs.cl']",2205.13722v2.pdf," A key promise of machine learning is the ability to assist users with +personal tasks. Because the personal context required to make accurate +predictions is often sensitive, we require systems that protect privacy. A gold +standard privacy-preserving system will satisfy perfect secrecy, meaning that +interactions with the system provably reveal no private information. However, +privacy and quality appear to be in tension in existing systems for personal +tasks. Neural models typically require copious amounts of training to perform +well, while individual users typically hold a limited scale of data, so +federated learning (FL) systems propose to learn from the aggregate data of +multiple users. FL does not provide perfect secrecy, but rather practitioners +apply statistical notions of privacy -- i.e., the probability of learning +private information about a user should be reasonably low. The strength of the +privacy guarantee is governed by privacy parameters. Numerous privacy attacks +have been demonstrated on FL systems and it can be challenging to reason about +the appropriate privacy parameters for a privacy-sensitive use case. Therefore +our work proposes a simple baseline for FL, which both provides the stronger +perfect secrecy guarantee and does not require setting any privacy parameters. +We initiate the study of when and where an emerging tool in ML -- the +in-context learning abilities of recent pretrained models -- can be an +effective baseline alongside FL. We find in-context learning is competitive +with strong FL baselines on 6 of 7 popular benchmarks from the privacy +literature and a real-world case study, which is disjoint from the pretraining +data. We release our code here: https://github.com/simran-arora/focus +" +Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts,Nghia T. Le,http://arxiv.org/pdf/2210.03690v2.pdf,2022-10-07,"['cs.cl', 'cs.ai']",2210.03690v2.pdf," Anaphora resolution is an important task for information extraction across a +range of languages, text genres, and domains, motivating the need for methods +that do not require large annotated datasets. In-context learning has emerged +as a promising approach, yet there are a number of challenges in applying +in-context learning to resolve anaphora. For example, encoding a single +in-context demonstration that consists of: an anaphor, a paragraph-length +context, and a list of corresponding antecedents, requires conditioning a +language model on a long sequence of tokens, limiting the number of +demonstrations per prompt. In this paper, we present MICE (Mixtures of +In-Context Experts), which we demonstrate is effective for few-shot anaphora +resolution in scientific protocols (Tamari et al., 2021). Given only a handful +of training examples, MICE combines the predictions of hundreds of in-context +experts, yielding a 30% increase in F1 score over a competitive prompt +retrieval baseline. Furthermore, we show MICE can be used to train compact +student models without sacrificing performance. As far as we are aware, this is +the first work to present experimental results demonstrating the effectiveness +of in-context learning on the task of few-shot anaphora resolution in +scientific protocols. +" +What learning algorithm is in-context learning? Investigations with linear models,Ekin Akyürek,http://arxiv.org/pdf/2211.15661v3.pdf,2022-11-28,"['cs.lg', 'cs.cl']",2211.15661v3.pdf," Neural sequence models, especially transformers, exhibit a remarkable +capacity for in-context learning. They can construct new predictors from +sequences of labeled examples $(x, f(x))$ presented in the input without +further parameter updates. We investigate the hypothesis that transformer-based +in-context learners implement standard learning algorithms implicitly, by +encoding smaller models in their activations, and updating these implicit +models as new examples appear in the context. Using linear regression as a +prototypical problem, we offer three sources of evidence for this hypothesis. +First, we prove by construction that transformers can implement learning +algorithms for linear models based on gradient descent and closed-form ridge +regression. Second, we show that trained in-context learners closely match the +predictors computed by gradient descent, ridge regression, and exact +least-squares regression, transitioning between different predictors as +transformer depth and dataset noise vary, and converging to Bayesian estimators +for large widths and depths. Third, we present preliminary evidence that +in-context learners share algorithmic features with these predictors: learners' +late layers non-linearly encode weight vectors and moment matrices. These +results suggest that in-context learning is understandable in algorithmic +terms, and that (at least in the linear case) learners may rediscover standard +estimation algorithms. Code and reference implementations are released at +https://github.com/ekinakyurek/google-research/blob/master/incontext. +" +SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and its Retrieval,Qing Huang,http://arxiv.org/pdf/2212.08221v1.pdf,2022-12-16,['cs.se'],2212.08221v1.pdf," Pre-trained giant code models (PCMs) start coming into the developers' daily +practices. Understanding what types of and how much software knowledge is +packed into PCMs is the foundation for incorporating PCMs into software +engineering (SE) tasks and fully releasing their potential. In this work, we +conduct the first systematic study on the SE factual knowledge in the +state-of-the-art PCM CoPilot, focusing on APIs' Fully Qualified Names (FQNs), +the fundamental knowledge for effective code analysis, search and reuse. Driven +by FQNs' data distribution properties, we design a novel lightweight in-context +learning on Copilot for FQN inference, which does not require code compilation +as traditional methods or gradient update by recent FQN prompt-tuning. We +systematically experiment with five in-context-learning design factors to +identify the best in-context learning configuration that developers can adopt +in practice. With this best configuration, we investigate the effects of amount +of example prompts and FQN data properties on Copilot's FQN inference +capability. Our results confirm that Copilot stores diverse FQN knowledge and +can be applied for the FQN inference due to its high inference accuracy and +non-reliance on code analysis. Based on our experience interacting with +Copilot, we discuss various opportunities to improve human-CoPilot interaction +in the FQN inference task. +" +Transformers as Algorithms: Generalization and Stability in In-context Learning,Yingcong Li,http://arxiv.org/pdf/2301.07067v2.pdf,2023-01-17,"['cs.lg', 'cs.cl', 'stat.ml']",2301.07067v2.pdf," In-context learning (ICL) is a type of prompting where a transformer model +operates on a sequence of (input, output) examples and performs inference +on-the-fly. In this work, we formalize in-context learning as an algorithm +learning problem where a transformer model implicitly constructs a hypothesis +function at inference-time. We first explore the statistical aspects of this +abstraction through the lens of multitask learning: We obtain generalization +bounds for ICL when the input prompt is (1) a sequence of i.i.d. (input, label) +pairs or (2) a trajectory arising from a dynamical system. The crux of our +analysis is relating the excess risk to the stability of the algorithm +implemented by the transformer. We characterize when transformer/attention +architecture provably obeys the stability condition and also provide empirical +verification. For generalization on unseen tasks, we identify an inductive bias +phenomenon in which the transfer learning risk is governed by the task +complexity and the number of MTL tasks in a highly predictable manner. Finally, +we provide numerical evaluations that (1) demonstrate transformers can indeed +implement near-optimal algorithms on classical regression problems with i.i.d. +and dynamic data, (2) provide insights on stability, and (3) verify our +theoretical predictions. +" +Adaptive Machine Translation with Large Language Models,Yasmin Moslem,http://arxiv.org/pdf/2301.13294v3.pdf,2023-01-30,['cs.cl'],2301.13294v3.pdf," Consistency is a key requirement of high-quality translation. It is +especially important to adhere to pre-approved terminology and adapt to +corrected translations in domain-specific projects. Machine translation (MT) +has achieved significant progress in the area of domain adaptation. However, +real-time adaptation remains challenging. Large-scale language models (LLMs) +have recently shown interesting capabilities of in-context learning, where they +learn to replicate certain input-output text generation patterns, without +further fine-tuning. By feeding an LLM at inference time with a prompt that +consists of a list of translation pairs, it can then simulate the domain and +style characteristics. This work aims to investigate how we can utilize +in-context learning to improve real-time adaptive MT. Our extensive experiments +show promising results at translation time. For example, LLMs can adapt to a +set of in-domain sentence pairs and/or terminology while translating a new +sentence. We observe that the translation quality with few-shot in-context +learning can surpass that of strong encoder-decoder MT systems, especially for +high-resource languages. Moreover, we investigate whether we can combine MT +from strong encoder-decoder models with fuzzy matches, which can further +improve translation quality, especially for less supported languages. We +conduct our experiments across five diverse language pairs, namely +English-to-Arabic (EN-AR), English-to-Chinese (EN-ZH), English-to-French +(EN-FR), English-to-Kinyarwanda (EN-RW), and English-to-Spanish (EN-ES). +" +ScatterShot: Interactive In-context Example Curation for Text Transformation,Tongshuang Wu,http://arxiv.org/pdf/2302.07346v1.pdf,2023-02-14,"['cs.hc', 'cs.cl']",2302.07346v1.pdf," The in-context learning capabilities of LLMs like GPT-3 allow annotators to +customize an LLM to their specific tasks with a small number of examples. +However, users tend to include only the most obvious patterns when crafting +examples, resulting in underspecified in-context functions that fall short on +unseen cases. Further, it is hard to know when ""enough"" examples have been +included even for known patterns. In this work, we present ScatterShot, an +interactive system for building high-quality demonstration sets for in-context +learning. ScatterShot iteratively slices unlabeled data into task-specific +patterns, samples informative inputs from underexplored or not-yet-saturated +slices in an active learning manner, and helps users label more efficiently +with the help of an LLM and the current example set. In simulation studies on +two text perturbation scenarios, ScatterShot sampling improves the resulting +few-shot functions by 4-5 percentage points over random sampling, with less +variance as more examples are added. In a user study, ScatterShot greatly helps +users in covering different patterns in the input space and labeling in-context +examples more efficiently, resulting in better in-context learning and less +user effort. +" +Resources and Few-shot Learners for In-context Learning in Slavic Languages,Michal Štefánik,http://arxiv.org/pdf/2304.01922v1.pdf,2023-04-04,['cs.cl'],2304.01922v1.pdf," Despite the rapid recent progress in creating accurate and compact in-context +learners, most recent work focuses on in-context learning (ICL) for tasks in +English. However, the ability to interact with users of languages outside +English presents a great potential for broadening the applicability of language +technologies to non-English speakers. + In this work, we collect the infrastructure necessary for training and +evaluation of ICL in a selection of Slavic languages: Czech, Polish, and +Russian. We link a diverse set of datasets and cast these into a unified +instructional format through a set of transformations and newly-crafted +templates written purely in target languages. Using the newly-curated dataset, +we evaluate a set of the most recent in-context learners and compare their +results to the supervised baselines. Finally, we train, evaluate and publish a +set of in-context learning models that we train on the collected resources and +compare their performance to previous work. + We find that ICL models tuned in English are also able to learn some tasks +from non-English contexts, but multilingual instruction fine-tuning +consistently improves the ICL ability. We also find that the massive multitask +training can be outperformed by single-task training in the target language, +uncovering the potential for specializing in-context learners to the +language(s) of their application. +" +Boosting Theory-of-Mind Performance in Large Language Models via Prompting,Shima Rahimi Moghaddam,http://arxiv.org/pdf/2304.11490v3.pdf,2023-04-22,"['cs.ai', 'cs.cl']",2304.11490v3.pdf," Large language models (LLMs) excel in many tasks in 2023, but they still face +challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require +understanding agents' beliefs, goals, and mental states, are essential for +common-sense reasoning involving humans, making it crucial to enhance LLM +performance in this area. This study measures the ToM performance of GPT-4 and +three GPT-3.5 variants (Davinci-2, Davinci-3, GPT-3.5-Turbo), and investigates +the effectiveness of in-context learning in improving their ToM comprehension. +We evaluated prompts featuring two-shot chain of thought reasoning and +step-by-step thinking instructions. We found that LLMs trained with +Reinforcement Learning from Human Feedback (RLHF) (all models excluding +Davinci-2) improved their ToM accuracy via in-context learning. GPT-4 performed +best in zero-shot settings, reaching nearly 80% ToM accuracy, but still fell +short of the 87% human accuracy on the test set. However, when supplied with +prompts for in-context learning, all RLHF-trained LLMs exceeded 80% ToM +accuracy, with GPT-4 reaching 100%. These results demonstrate that appropriate +prompting enhances LLM ToM reasoning, and they underscore the context-dependent +nature of LLM cognitive capacities. +" +Unified Demonstration Retriever for In-Context Learning,Xiaonan Li,http://arxiv.org/pdf/2305.04320v2.pdf,2023-05-07,['cs.cl'],2305.04320v2.pdf," In-context learning is a new learning paradigm where a language model +conditions on a few input-output pairs (demonstrations) and a test input, and +directly outputs the prediction. It has been shown highly dependent on the +provided demonstrations and thus promotes the research of demonstration +retrieval: given a test input, relevant examples are retrieved from the +training set to serve as informative demonstrations for in-context learning. +While previous works focus on training task-specific retrievers for several +tasks separately, these methods are often hard to transfer and scale on various +tasks, and separately trained retrievers incur a lot of parameter storage and +deployment cost. In this paper, we propose Unified Demonstration Retriever +(\textbf{UDR}), a single model to retrieve demonstrations for a wide range of +tasks. To train UDR, we cast various tasks' training signals into a unified +list-wise ranking formulation by language model's feedback. Then we propose a +multi-task list-wise ranking training framework, with an iterative mining +strategy to find high-quality candidates, which can help UDR fully incorporate +various tasks' signals. Experiments on 30+ tasks across 13 task families and +multiple data domains show that UDR significantly outperforms baselines. +Further analyses show the effectiveness of each proposed component and UDR's +strong ability in various scenarios including different LMs (1.3B - 175B), +unseen datasets, varying demonstration quantities, etc. +" +Efficient Prompting via Dynamic In-Context Learning,Wangchunshu Zhou,http://arxiv.org/pdf/2305.11170v1.pdf,2023-05-18,"['cs.cl', 'cs.ai', 'cs.lg']",2305.11170v1.pdf," The primary way of building AI applications is shifting from training +specialist models to prompting generalist models. A common practice for +prompting generalist models, often referred to as in-context learning, is to +append a few examples (demonstrations) to the prompt to help the model better +understand the task. While effective, in-context learning can be inefficient +because it makes the input prompt much longer, consuming valuable space in the +context window and leading to larger computational costs. In this paper, we +propose DynaICL, a recipe for efficient prompting with black-box generalist +models that dynamically allocate in-context examples according to the input +complexity and the computational budget. To achieve this, we train a meta +controller that predicts the number of in-context examples suitable for the +generalist model to make a good prediction based on the performance-efficiency +trade-off for a specific input. We then dynamically allocate the number of +demonstrations for an input according to predictions from the meta controller +and the given computation budget. Experimental results show that dynamic +example allocation helps achieve a better performance-efficiency trade-off in +two practical settings where computational resources or the required +performance is constrained. Specifically, DynaICL saves up to 46% token budget +compared to the common practice that allocates the same number of in-context +examples to each input. We also find that a meta controller trained on a +certain backbone model and tasks can successfully generalize to unseen models +and tasks. +" +Post Hoc Explanations of Language Models Can Improve Language Models,Satyapriya Krishna,http://arxiv.org/pdf/2305.11426v2.pdf,2023-05-19,"['cs.cl', 'cs.ai']",2305.11426v2.pdf," Large Language Models (LLMs) have demonstrated remarkable capabilities in +performing complex tasks. Moreover, recent research has shown that +incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) +during in-context learning can significantly enhance the performance of these +models, particularly on tasks that require reasoning capabilities. However, +incorporating such rationales poses challenges in terms of scalability as this +requires a high degree of human involvement. In this work, we present a novel +framework, Amplifying Model Performance by Leveraging In-Context Learning with +Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges +by automating the process of rationale generation. To this end, we leverage +post hoc explanation methods which output attribution scores (explanations) +capturing the influence of each of the input features on model predictions. +More specifically, we construct automated natural language rationales that +embed insights from post hoc explanations to provide corrective signals to +LLMs. Extensive experimentation with real-world datasets demonstrates that our +framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% +over a wide range of tasks, including those where prior approaches which rely +on human-annotated rationales such as Chain-of-Thought prompting fall short. +Our work makes one of the first attempts at highlighting the potential of post +hoc explanations as valuable tools for enhancing the effectiveness of LLMs. +Furthermore, we conduct additional empirical analyses and ablation studies to +demonstrate the impact of each of the components of AMPLIFY, which, in turn, +leads to critical insights for refining in-context learning. +" +Explaining Emergent In-Context Learning as Kernel Regression,Chi Han,http://arxiv.org/pdf/2305.12766v2.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.lg']",2305.12766v2.pdf," Large language models (LLMs) have initiated a paradigm shift in transfer +learning. In contrast to the classic pretraining-then-finetuning procedure, in +order to use LLMs for downstream prediction tasks, one only needs to provide a +few demonstrations, known as in-context examples, without adding more or +updating existing model parameters. This in-context learning (ICL) capability +of LLMs is intriguing, and it is not yet fully understood how pretrained LLMs +acquire such capabilities. In this paper, we investigate the reason why a +transformer-based language model can accomplish in-context learning after +pre-training on a general language corpus by proposing one hypothesis that LLMs +can simulate kernel regression with internal representations when faced with +in-context examples. More concretely, we first prove that Bayesian inference on +in-context prompts can be asymptotically understood as kernel regression $\hat +y = \sum_i y_i K(x, x_i)/\sum_i K(x, x_i)$ as the number of in-context +demonstrations grows. Then, we empirically investigate the in-context behaviors +of language models. We find that during ICL, the attention and hidden features +in LLMs match the behaviors of a kernel regression. Finally, our theory +provides insights into multiple phenomena observed in the ICL field: why +retrieving demonstrative samples similar to test samples can help, why ICL +performance is sensitive to the output formats, and why ICL accuracy benefits +from selecting in-distribution and representative samples. +" +RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning,Alexander Scarlatos,http://arxiv.org/pdf/2305.14502v1.pdf,2023-05-23,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14502v1.pdf," Many recent developments in large language models focus on prompting them to +perform specific tasks. One effective prompting method is in-context learning, +where the model performs a (possibly new) generation/prediction task given one +(or more) examples. Past work has shown that the choice of examples can make a +large impact on task performance. However, finding good examples is not +straightforward since the definition of a representative group of examples can +vary greatly depending on the task. While there are many existing methods for +selecting in-context examples, they generally score examples independently, +ignoring the dependency between them and the order in which they are provided +to the large language model. In this work, we propose Retrieval for In-Context +Learning (RetICL), a learnable method for modeling and optimally selecting +examples sequentially for in-context learning. We frame the problem of +sequential example selection as a Markov decision process, design an example +retriever model using an LSTM, and train it using proximal policy optimization +(PPO). We validate RetICL on math problem solving datasets and show that it +outperforms both heuristic and learnable baselines, and achieves +state-of-the-art accuracy on the TabMWP dataset. We also use case studies to +show that RetICL implicitly learns representations of math problem solving +strategies. +" +In-Context Learning for Attention Scheme: from Single Softmax Regression to Multiple Softmax Regression via a Tensor Trick,Yeqi Gao,http://arxiv.org/pdf/2307.02419v1.pdf,2023-07-05,['cs.lg'],2307.02419v1.pdf," Large language models (LLMs) have brought significant and transformative +changes in human society. These models have demonstrated remarkable +capabilities in natural language understanding and generation, leading to +various advancements and impacts across several domains. + We consider the in-context learning under two formulation for attention +related regression in this work. Given matrices $A_1 \in \mathbb{R}^{n \times +d}$, and $A_2 \in \mathbb{R}^{n \times d}$ and $B \in \mathbb{R}^{n \times n}$, +the purpose is to solve some certain optimization problems: Normalized version +$\min_{X} \| D(X)^{-1} \exp(A_1 X A_2^\top) - B \|_F^2$ and Rescaled version +$\| \exp(A_1 X A_2^\top) - D(X) \cdot B \|_F^2$. Here $D(X) := \mathrm{diag}( +\exp(A_1 X A_2^\top) {\bf 1}_n )$. + Our regression problem shares similarities with previous studies on +softmax-related regression. Prior research has extensively investigated +regression techniques related to softmax regression: Normalized version $\| +\langle \exp(Ax) , {\bf 1}_n \rangle^{-1} \exp(Ax) - b \|_2^2$ and Resscaled +version $\| \exp(Ax) - \langle \exp(Ax), {\bf 1}_n \rangle b \|_2^2 $ + In contrast to previous approaches, we adopt a vectorization technique to +address the regression problem in matrix formulation. This approach expands the +dimension from $d$ to $d^2$, resembling the formulation of the regression +problem mentioned earlier. + Upon completing the lipschitz analysis of our regression function, we have +derived our main result concerning in-context learning. +" +SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design,Carl Edwards,http://arxiv.org/pdf/2307.11694v2.pdf,2023-06-19,"['cs.ai', 'cs.lg', 'q-bio.bm', 'q-bio.mn']",2307.11694v2.pdf," Predicting synergistic drug combinations can help accelerate discovery of +cancer treatments, particularly therapies personalized to a patient's specific +tumor via biopsied cells. In this paper, we propose a novel setting and models +for in-context drug synergy learning. We are given a small ""personalized +dataset"" of 10-20 drug synergy relationships in the context of specific cancer +cell targets. Our goal is to predict additional drug synergy relationships in +that context. Inspired by recent work that pre-trains a GPT language model (LM) +to ""in-context learn"" common function classes, we devise novel pre-training +schemes that enable a GPT model to in-context learn ""drug synergy functions"". +Our model -- which does not use any textual corpora, molecular fingerprints, +protein interaction or any other domain-specific knowledge -- is able to +achieve competitive results. We further integrate our in-context approach with +a genetic algorithm to optimize model prompts and select synergy candidates to +test after conducting a patient biopsy. Finally, we explore a novel task of +inverse drug design which can potentially enable the design of drugs that +synergize specifically to target a given patient's ""personalized dataset"". Our +findings can potentially have an important impact on precision cancer medicine, +and also raise intriguing questions on non-textual pre-training for LMs. +" +OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples,Ryuto Koike,http://arxiv.org/pdf/2307.11729v2.pdf,2023-07-21,['cs.cl'],2307.11729v2.pdf," Large Language Models (LLMs) have achieved human-level fluency in text +generation, making it difficult to distinguish between human-written and +LLM-generated texts. This poses a growing risk of misuse of LLMs and demands +the development of detectors to identify LLM-generated texts. However, existing +detectors lack robustness against attacks: they degrade detection accuracy by +simply paraphrasing LLM-generated texts. Furthermore, a malicious user might +attempt to deliberately evade the detectors based on detection results, but +this has not been assumed in previous studies. In this paper, we propose +OUTFOX, a framework that improves the robustness of LLM-generated-text +detectors by allowing both the detector and the attacker to consider each +other's output. In this framework, the attacker uses the detector's prediction +labels as examples for in-context learning and adversarially generates essays +that are harder to detect, while the detector uses the adversarially generated +essays as examples for in-context learning to learn to detect essays from a +strong attacker. Experiments in the domain of student essays show that the +proposed detector improves the detection performance on the attacker-generated +texts by up to +41.3 points in F1-score. Furthermore, the proposed detector +shows a state-of-the-art detection performance: up to 96.9 points in F1-score, +beating existing detectors on non-attacked texts. Finally, the proposed +attacker drastically degrades the performance of detectors by up to -57.0 +points F1-score, massively outperforming the baseline paraphrasing method for +evading detection. +" +Metric-Based In-context Learning: A Case Study in Text Simplification,Subha Vadlamannati,http://arxiv.org/pdf/2307.14632v1.pdf,2023-07-27,"['cs.cl', 'cs.ai']",2307.14632v1.pdf," In-context learning (ICL) for large language models has proven to be a +powerful approach for many natural language processing tasks. However, +determining the best method to select examples for ICL is nontrivial as the +results can vary greatly depending on the quality, quantity, and order of +examples used. In this paper, we conduct a case study on text simplification +(TS) to investigate how to select the best and most robust examples for ICL. We +propose Metric-Based in-context Learning (MBL) method that utilizes commonly +used TS metrics such as SARI, compression ratio, and BERT-Precision for +selection. Through an extensive set of experiments with various-sized GPT +models on standard TS benchmarks such as TurkCorpus and ASSET, we show that +examples selected by the top SARI scores perform the best on larger models such +as GPT-175B, while the compression ratio generally performs better on smaller +models such as GPT-13B and GPT-6.7B. Furthermore, we demonstrate that MBL is +generally robust to example orderings and out-of-domain test sets, and +outperforms strong baselines and state-of-the-art finetuned language models. +Finally, we show that the behaviour of large GPT models can be implicitly +controlled by the chosen metric. Our research provides a new framework for +selecting examples in ICL, and demonstrates its effectiveness in text +simplification tasks, breaking new ground for more accurate and efficient NLG +systems. +" +HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding,Hanzhuo Tan,http://arxiv.org/pdf/2308.09985v1.pdf,2023-08-19,['cs.cl'],2308.09985v1.pdf," Natural language understanding (NLU) is integral to various social media +applications. However, existing NLU models rely heavily on context for semantic +learning, resulting in compromised performance when faced with short and noisy +social media content. To address this issue, we leverage in-context learning +(ICL), wherein language models learn to make inferences by conditioning on a +handful of demonstrations to enrich the context and propose a novel +hashtag-driven in-context learning (HICL) framework. Concretely, we pre-train a +model #Encoder, which employs #hashtags (user-annotated topic labels) to drive +BERT-based pre-training through contrastive learning. Our objective here is to +enable #Encoder to gain the ability to incorporate topic-related semantic +information, which allows it to retrieve topic-related posts to enrich contexts +and enhance social media NLU with noisy contexts. To further integrate the +retrieved context with the source text, we employ a gradient-based method to +identify trigger terms useful in fusing information from both sources. For +empirical studies, we collected 45M tweets to set up an in-context NLU +benchmark, and the experimental results on seven downstream tasks show that +HICL substantially advances the previous state-of-the-art results. Furthermore, +we conducted extensive analyzes and found that: (1) combining source input with +a top-retrieved post from #Encoder is more effective than using semantically +similar posts; (2) trigger words can largely benefit in merging context from +the source and retrieved posts. +" +Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning,Yuchen Yang,http://arxiv.org/pdf/2310.04782v1.pdf,2023-10-07,['cs.cl'],2310.04782v1.pdf," In recent years, large-scale language models (LLMs) have gained attention for +their impressive text generation capabilities. However, these models often face +the challenge of ""hallucination,"" which undermines their reliability. In this +study, we introduce an uncertainty-aware in-context learning framework to +empower the model to enhance or reject its output in response to uncertainty. +Human-defined methods for estimating uncertainty typically assume that +""uncertainty is lower when the model's response is correct compared to when it +is incorrect."" However, setting a precise threshold to distinguish correctness +is challenging. Therefore, we introduce uncertainty information as an +intermediary variable that implicitly influences the model's behavior. Our +innovative uncertainty-aware in-context learning framework involves fine-tuning +the LLM using a calibration dataset. Our aim is to improve the model's +responses by filtering out answers with high uncertainty while considering the +model's knowledge limitations. We evaluate the model's knowledge by examining +multiple responses to the same question for the presence of a correct answer. +When the model lacks relevant knowledge, the response should indicate that the +question cannot be answered. Conversely, when the model has relevant knowledge, +the response should provide the correct answer. Extensive experiments confirm +the effectiveness of our framework, leading to two key findings. First, the +logit output values of the LLM partly reflect inherent uncertainty. Second, our +model autonomously recognizes uncertainty, resulting in improved responses. +" +In-Context Convergence of Transformers,Yu Huang,http://arxiv.org/pdf/2310.05249v1.pdf,2023-10-08,"['cs.lg', 'cs.ai', 'math.oc', 'stat.ml']",2310.05249v1.pdf," Transformers have recently revolutionized many domains in modern machine +learning and one salient discovery is their remarkable in-context learning +capability, where models can solve an unseen task by utilizing task-specific +prompts without further parameters fine-tuning. This also inspired recent +theoretical studies aiming to understand the in-context learning mechanism of +transformers, which however focused only on linear transformers. In this work, +we take the first step toward studying the learning dynamics of a one-layer +transformer with softmax attention trained via gradient descent in order to +in-context learn linear function classes. We consider a structured data model, +where each token is randomly sampled from a set of feature vectors in either +balanced or imbalanced fashion. For data with balanced features, we establish +the finite-time convergence guarantee with near-zero prediction error by +navigating our analysis over two phases of the training dynamics of the +attention map. More notably, for data with imbalanced features, we show that +the learning dynamics take a stage-wise convergence process, where the +transformer first converges to a near-zero prediction error for the query +tokens of dominant features, and then converges later to a near-zero prediction +error for the query tokens of under-represented features, respectively via one +and four training phases. Our proof features new techniques for analyzing the +competing strengths of two types of attention weights, the change of which +determines different training phases. +" +Large Language Model-Aware In-Context Learning for Code Generation,Jia Li,http://arxiv.org/pdf/2310.09748v1.pdf,2023-10-15,"['cs.se', 'cs.cl']",2310.09748v1.pdf," Large language models (LLMs) have shown impressive in-context learning (ICL) +ability in code generation. LLMs take a prompt consisting of requirement-code +examples and a new requirement as input, and output new programs. Existing +studies have found that ICL is highly dominated by the examples and thus arises +research on example selection. However, existing approaches randomly select +examples or only consider the textual similarity of requirements to retrieve, +leading to sub-optimal performance. In this paper, we propose a novel +learning-based selection approach named LAIL (LLM-Aware In-context Learning) +for code generation. Given a candidate example, we exploit LLMs themselves to +estimate it by considering the generation probabilities of ground-truth +programs given a requirement and the example. We then label candidate examples +as positive or negative through the probability feedback. Based on the labeled +data, we import a contrastive learning objective to train an effective +retriever that acquires the preference of LLMs in code generation. We apply +LAIL to three LLMs and evaluate it on three representative datasets (e.g., +MBJP, MBPP, and MBCPP). LATA outperforms the state-of-the-art baselines by +11.58%, 6.89%, and 5.07% on CodeGen, and 4.38%, 2.85%, and 2.74% on GPT-3.5 in +terms of Pass@1, respectively. +" +Two-stage LLM Fine-tuning with Less Specialization and More Generalization,Yihan Wang,http://arxiv.org/pdf/2211.00635v2.pdf,2022-11-01,"['cs.cl', 'cs.lg']",2211.00635v2.pdf," Pretrained large language models (LLMs) are general purpose problem solvers +applicable to a diverse set of tasks with prompts. They can be further improved +towards a specific task by fine-tuning on a specialized dataset. However, +fine-tuning usually makes the model narrowly specialized on this dataset with +reduced general in-context learning performances, which is undesirable whenever +the fine-tuned model needs to handle additional tasks where no fine-tuning data +is available. In this work, we first demonstrate that fine-tuning on a single +task indeed decreases LLMs' general in-context learning performance. We +discover one important cause of such forgetting, format specialization, where +the model overfits to the format of the fine-tuned task. We further show that +format specialization happens at the very beginning of fine-tuning. To solve +this problem, we propose Prompt Tuning with MOdel Tuning (ProMoT), a simple yet +effective two-stage fine-tuning framework that reduces format specialization +and improves generalization. ProMoT offloads task-specific format learning into +additional and removable parameters by first doing prompt tuning and then +fine-tuning the model itself with this soft prompt attached. With experiments +on several fine-tuning tasks and 8 in-context evaluation tasks, we show that +ProMoT achieves comparable performance on fine-tuned tasks to standard +fine-tuning, but with much less loss of in-context learning performances across +a board range of out-of-domain evaluation tasks. More importantly, ProMoT can +even enhance generalization on in-context learning tasks that are semantically +related to the fine-tuned task, e.g. ProMoT on En-Fr translation significantly +improves performance on other language pairs, and ProMoT on NLI improves +performance on summarization. Experiments also show that ProMoT can improve the +generalization performance of multi-task training. +" +On the Relation between Sensitivity and Accuracy in In-context Learning,Yanda Chen,http://arxiv.org/pdf/2209.07661v2.pdf,2022-09-16,"['cs.cl', 'cs.ai', 'cs.lg']",2209.07661v2.pdf," In-context learning (ICL) suffers from oversensitivity to the prompt, making +it unreliable in real-world scenarios. We study the sensitivity of ICL with +respect to multiple perturbation types. First, we find that label bias obscures +the true sensitivity, and therefore prior work may have significantly +underestimated ICL sensitivity. Second, we observe a strong negative +correlation between ICL sensitivity and accuracy: predictions sensitive to +perturbations are less likely to be correct. Motivated by these findings, we +propose \textsc{SenSel}, a few-shot selective prediction method that abstains +from sensitive predictions. Experiments on ten classification datasets show +that \textsc{SenSel} consistently outperforms two commonly used +confidence-based and entropy-based baselines on abstention decisions. +" +WinoDict: Probing language models for in-context word acquisition,Julian Martin Eisenschlos,http://arxiv.org/pdf/2209.12153v1.pdf,2022-09-25,"['cs.cl', 'cs.ai']",2209.12153v1.pdf," We introduce a new in-context learning paradigm to measure Large Language +Models' (LLMs) ability to learn novel words during inference. In particular, we +rewrite Winograd-style co-reference resolution problems by replacing the key +concept word with a synthetic but plausible word that the model must understand +to complete the task. Solving this task requires the model to make use of the +dictionary definition of the new word given in the prompt. This benchmark +addresses word acquisition, one important aspect of the diachronic degradation +known to afflict LLMs. As LLMs are frozen in time at the moment they are +trained, they are normally unable to reflect the way language changes over +time. We show that the accuracy of LLMs compared to the original Winograd tasks +decreases radically in our benchmark, thus identifying a limitation of current +models and providing a benchmark to measure future improvements in LLMs ability +to do in-context learning. +" +Data Curation Alone Can Stabilize In-context Learning,Ting-Yun Chang,http://arxiv.org/pdf/2212.10378v2.pdf,2022-12-20,['cs.cl'],2212.10378v2.pdf," In-context learning (ICL) enables large language models (LLMs) to perform new +tasks by prompting them with a sequence of training examples. However, it is +known that ICL is very sensitive to the choice of training examples: randomly +sampling examples from a training set leads to high variance in performance. In +this paper, we show that carefully curating a subset of training data greatly +stabilizes ICL performance without any other changes to the ICL algorithm +(e.g., prompt retrieval or calibration). We introduce two methods to choose +training subsets -- both score training examples individually, then select the +highest-scoring ones. CondAcc scores a training example by its average dev-set +ICL accuracy when combined with random training examples, while Datamodels +learns linear regressors that estimate how the presence of each training +example influences LLM outputs. Across five tasks and two LLMs, sampling from +stable subsets selected by CondAcc and Datamodels improves average accuracy +over sampling from the entire training set by 7.7% and 6.3%, respectively. +Surprisingly, the stable subset examples are not especially diverse in content +or low in perplexity, in contrast with other work suggesting that diversity and +perplexity are important when prompting LLMs. +" +A Survey on In-context Learning,Qingxiu Dong,http://arxiv.org/pdf/2301.00234v3.pdf,2022-12-31,"['cs.cl', 'cs.ai']",2301.00234v3.pdf," With the increasing ability of large language models (LLMs), in-context +learning (ICL) has become a new paradigm for natural language processing (NLP), +where LLMs make predictions only based on contexts augmented with a few +examples. It has been a new trend to explore ICL to evaluate and extrapolate +the ability of LLMs. In this paper, we aim to survey and summarize the progress +and challenges of ICL. We first present a formal definition of ICL and clarify +its correlation to related studies. Then, we organize and discuss advanced +techniques, including training strategies, demonstration designing strategies, +as well as related analysis. Finally, we discuss the challenges of ICL and +provide potential directions for further research. We hope that our work can +encourage more research on uncovering how ICL works and improving ICL. +" +Using In-Context Learning to Improve Dialogue Safety,Nicholas Meade,http://arxiv.org/pdf/2302.00871v3.pdf,2023-02-02,['cs.cl'],2302.00871v3.pdf," While large neural-based conversational models have become increasingly +proficient dialogue agents, recent work has highlighted safety issues with +these systems. For example, these systems can be goaded into generating toxic +content, which often perpetuates social biases or stereotypes. We investigate a +retrieval-based method for reducing bias and toxicity in responses from +chatbots. It uses in-context learning to steer a model towards safer +generations. Concretely, to generate a response to an unsafe dialogue context, +we retrieve demonstrations of safe responses to similar dialogue contexts. We +find our method performs competitively with strong baselines without requiring +training. For instance, using automatic evaluation, we find our best fine-tuned +baseline only generates safe responses to unsafe dialogue contexts from +DiaSafety 4.04% more than our approach. Finally, we also propose a re-ranking +procedure which can further improve response safeness. +" +Towards Few-Shot Identification of Morality Frames using In-Context Learning,Shamik Roy,http://arxiv.org/pdf/2302.02029v1.pdf,2023-02-03,['cs.cl'],2302.02029v1.pdf," Data scarcity is a common problem in NLP, especially when the annotation +pertains to nuanced socio-linguistic concepts that require specialized +knowledge. As a result, few-shot identification of these concepts is desirable. +Few-shot in-context learning using pre-trained Large Language Models (LLMs) has +been recently applied successfully in many NLP tasks. In this paper, we study +few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et +al., 2021), using LLMs. Morality frames are a representation framework that +provides a holistic view of the moral sentiment expressed in text, identifying +the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of +granularity, the moral sentiment expressed towards the entities mentioned in +the text. Previous studies relied on human annotation to identify morality +frames in text which is expensive. In this paper, we propose prompting-based +approaches using pretrained Large Language Models for identification of +morality frames, relying only on few-shot exemplars. We compare our models' +performance with few-shot RoBERTa and found promising results. +" +OpenICL: An Open-Source Framework for In-context Learning,Zhenyu Wu,http://arxiv.org/pdf/2303.02913v1.pdf,2023-03-06,['cs.cl'],2303.02913v1.pdf," In recent years, In-context Learning (ICL) has gained increasing attention +and emerged as the new paradigm for large language model (LLM) evaluation. +Unlike traditional fine-tuning methods, ICL instead adapts the pre-trained +models to unseen tasks without any parameter updates. However, the +implementation of ICL is sophisticated due to the diverse retrieval and +inference methods involved, as well as the varying pre-processing requirements +for different models, datasets, and tasks. A unified and flexible framework for +ICL is urgently needed to ease the implementation of the aforementioned +components. To facilitate ICL research, we introduce OpenICL, an open-source +toolkit for ICL and LLM evaluation. OpenICL is research-friendly with a highly +flexible architecture that users can easily combine different components to +suit their needs. It also provides various state-of-the-art retrieval and +inference methods to streamline the process of adapting ICL to cutting-edge +research. The effectiveness of OpenICL has been validated on a wide range of +NLP tasks, including classification, QA, machine translation, and semantic +parsing. As a side-product, we found OpenICL to be an efficient yet robust tool +for LLMs evaluation. OpenICL is released at +https://github.com/Shark-NLP/OpenICL +" +The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints,Parker Seegmiller,http://arxiv.org/pdf/2303.09366v2.pdf,2023-03-16,"['cs.cl', 'cs.lg']",2303.09366v2.pdf," Medications often impose temporal constraints on everyday patient activity. +Violations of such medical temporal constraints (MTCs) lead to a lack of +treatment adherence, in addition to poor health outcomes and increased +healthcare expenses. These MTCs are found in drug usage guidelines (DUGs) in +both patient education materials and clinical texts. Computationally +representing MTCs in DUGs will advance patient-centric healthcare applications +by helping to define safe patient activity patterns. We define a novel taxonomy +of MTCs found in DUGs and develop a novel context-free grammar (CFG) based +model to computationally represent MTCs from unstructured DUGs. Additionally, +we release three new datasets with a combined total of N = 836 DUGs labeled +with normalized MTCs. We develop an in-context learning (ICL) solution for +automatically extracting and normalizing MTCs found in DUGs, achieving an +average F1 score of 0.62 across all datasets. Finally, we rigorously +investigate ICL model performance against a baseline model, across datasets and +MTC types, and through in-depth error analysis. +" +How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?,Xin Xu,http://arxiv.org/pdf/2305.01555v4.pdf,2023-05-02,"['cs.cl', 'cs.ai', 'cs.db', 'cs.ir', 'cs.lg']",2305.01555v4.pdf," Scaling language models have revolutionized widespread NLP tasks, yet little +comprehensively explored few-shot relation extraction with large language +models. In this paper, we investigate principal methodologies, in-context +learning and data generation, for few-shot relation extraction via GPT-3.5 +through exhaustive experiments. To enhance few-shot performance, we further +propose task-related instructions and schema-constrained data generation. We +observe that in-context learning can achieve performance on par with previous +prompt learning approaches, and data generation with the large language model +can boost previous solutions to obtain new state-of-the-art few-shot results on +four widely-studied relation extraction datasets. We hope our work can inspire +future research for the capabilities of large language models in few-shot +relation extraction. Code is available in +https://github.com/zjunlp/DeepKE/tree/main/example/llm. +" +GPT-RE: In-context Learning for Relation Extraction using Large Language Models,Zhen Wan,http://arxiv.org/pdf/2305.02105v2.pdf,2023-05-03,['cs.cl'],2305.02105v2.pdf," In spite of the potential for ground-breaking achievements offered by large +language models (LLMs) (e.g., GPT-3), they still lag significantly behind +fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). +This is due to the two major shortcomings of LLMs in RE: (1) low relevance +regarding entity and relation in retrieved demonstrations for in-context +learning; and (2) the strong inclination to wrongly classify NULL examples into +other pre-defined labels. + In this paper, we propose GPT-RE to bridge the gap between LLMs and +fully-supervised baselines. GPT-RE successfully addresses the aforementioned +issues by (1) incorporating task-specific entity representations in +demonstration retrieval; and (2) enriching the demonstrations with gold +label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE +datasets, and observe that GPT-RE achieves improvements over not only existing +GPT-3 baselines, but also fully-supervised baselines. Specifically, GPT-RE +achieves SOTA performances on the Semeval and SciERC datasets, and competitive +performances on the TACRED and ACE05 datasets. +" +GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning,Xiangru Tang,http://arxiv.org/pdf/2305.05001v1.pdf,2023-05-08,['cs.cl'],2305.05001v1.pdf," This paper presents our contribution to the MEDIQA-2023 Dialogue2Note shared +task, encompassing both subtask A and subtask B. We approach the task as a +dialogue summarization problem and implement two distinct pipelines: (a) a +fine-tuning of a pre-trained dialogue summarization model and GPT-3, and (b) +few-shot in-context learning (ICL) using a large language model, GPT-4. Both +methods achieve excellent results in terms of ROUGE-1 F1, BERTScore F1 +(deberta-xlarge-mnli), and BLEURT, with scores of 0.4011, 0.7058, and 0.5421, +respectively. Additionally, we predict the associated section headers using +RoBERTa and SciBERT based classification models. Our team ranked fourth among +all teams, while each team is allowed to submit three runs as part of their +submission. We also utilize expert annotations to demonstrate that the notes +generated through the ICL GPT-4 are better than all other baselines. The code +for our submission is available. +" +Can We Edit Factual Knowledge by In-Context Learning?,Ce Zheng,http://arxiv.org/pdf/2305.12740v1.pdf,2023-05-22,['cs.cl'],2305.12740v1.pdf," Previous studies have shown that large language models (LLMs) like GPTs store +massive factual knowledge in their parameters. However, the stored knowledge +could be false or out-dated. Traditional knowledge editing methods refine LLMs +via fine-tuning on texts containing specific knowledge. However, with the +increasing scales of LLMs, these gradient-based approaches bring large +computation costs. The trend of model-as-a-service also makes it impossible to +modify knowledge in black-box LMs. Inspired by in-context learning (ICL), a new +paradigm based on demonstration contexts without parameter updating, we explore +whether ICL can edit factual knowledge. To answer this question, we give a +comprehensive empirical study of ICL strategies. Experiments show that +in-context knowledge editing (IKE), without any gradient and parameter +updating, achieves a competitive success rate compared to gradient-based +methods on GPT-J (6B) but with much fewer side effects, including less +over-editing on similar but unrelated facts and less knowledge forgetting on +previously stored knowledge. We also apply the method to larger LMs with tens +or hundreds of parameters like OPT-175B, which shows the scalability of our +method. The code is available at https://github.com/Zce1112zslx/IKE. +" +Concept-aware Training Improves In-context Learning Ability of Language Models,Michal Štefánik,http://arxiv.org/pdf/2305.13775v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.13775v1.pdf," Many recent language models (LMs) of Transformers family exhibit so-called +in-context learning (ICL) ability, manifested in the LMs' ability to modulate +their function by a task described in a natural language input. Previous work +curating these models assumes that ICL emerges from vast over-parametrization +or the scale of multi-task training. However, a complementary branch of recent +theoretical work attributes ICL emergence to specific properties of training +data and creates functional in-context learners in small-scale, synthetic +settings. + Inspired by recent findings on data properties driving the emergence of ICL, +we propose a method to create LMs able to better utilize the in-context +information, by constructing training scenarios where it is beneficial for the +LM to capture the analogical reasoning concepts. We measure that data sampling +of Concept-aware Training (CoAT) consistently improves models' reasoning +ability. As a result, the in-context learners trained with CoAT on only two +datasets of a single (QA) task perform comparably to larger models trained on +1600+ tasks. +" +Dr.ICL: Demonstration-Retrieved In-context Learning,Man Luo,http://arxiv.org/pdf/2305.14128v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14128v1.pdf," In-context learning (ICL), teaching a large language model (LLM) to perform a +task with few-shot demonstrations rather than adjusting the model parameters, +has emerged as a strong paradigm for using LLMs. While early studies primarily +used a fixed or random set of demonstrations for all test queries, recent +research suggests that retrieving semantically similar demonstrations to the +input from a pool of available demonstrations results in better performance. +This work expands the applicability of retrieval-based ICL approaches by +demonstrating that even simple word-overlap similarity measures such as BM25 +outperform randomly selected demonstrations. Furthermore, we extend the success +of retrieval-based ICL to instruction-finetuned LLMs as well as +Chain-of-Thought (CoT) prompting. For instruction-finetuned LLMs, we find that +although a model has already seen the training data at training time, +retrieving demonstrations from the training data at test time yields better +results compared to using no demonstrations or random demonstrations. Last but +not least, we train a task-specific demonstration retriever that outperforms +off-the-shelf retrievers. +" +Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning,Lean Wang,http://arxiv.org/pdf/2305.14160v1.pdf,2023-05-23,"['cs.cl', 'cs.lg']",2305.14160v1.pdf," In-context learning (ICL) emerges as a promising capability of large language +models (LLMs) by providing them with demonstration examples to perform diverse +tasks. However, the underlying mechanism of how LLMs learn from the provided +context remains under-explored. In this paper, we investigate the working +mechanism of ICL through an information flow lens. Our findings reveal that +label words in the demonstration examples function as anchors: (1) semantic +information aggregates into label word representations during the shallow +computation layers' processing; (2) the consolidated information in label words +serves as a reference for LLMs' final predictions. Based on these insights, we +introduce an anchor re-weighting method to improve ICL performance, a +demonstration compression technique to expedite inference, and an analysis +framework for diagnosing ICL errors in GPT2-XL. The promising applications of +our findings again validate the uncovered ICL working mechanism and pave the +way for future studies. +" +Probing in Context: Toward Building Robust Classifiers via Probing Large Language Models,Afra Amini,http://arxiv.org/pdf/2305.14171v2.pdf,2023-05-23,['cs.cl'],2305.14171v2.pdf," Large language models are able to learn new tasks in context, where they are +provided with instructions and a few annotated examples. However, the +effectiveness of in-context learning is dependent on the provided context, and +the performance on a downstream task can vary considerably, depending on the +instruction. Importantly, such dependency on the context can surface in +unpredictable ways, e.g., a seemingly more informative instruction might lead +to a worse performance. In this paper, we propose an alternative approach, +which we term in-context probing. Similar to in-context learning, we +contextualize the representation of the input with an instruction, but instead +of decoding the output prediction, we probe the contextualized representation +to predict the label. Through a series of experiments on a diverse set of +classification tasks, we show that in-context probing is significantly more +robust to changes in instructions. We further show that probing performs +competitive or superior to finetuning and can be particularly helpful to build +classifiers on top of smaller models, and with only a hundred training +examples. +" +Coverage-based Example Selection for In-Context Learning,Shivanshu Gupta,http://arxiv.org/pdf/2305.14907v3.pdf,2023-05-24,['cs.cl'],2305.14907v3.pdf," In-context learning (ICL), the ability of large language models to perform +novel tasks by conditioning on a prompt with a few task examples, requires +these examples to be informative about the test instance. The standard approach +of independently ranking and selecting the most similar examples selects +redundant examples while omitting important information. In this work, we show +that BERTScore-Recall (BSR) selects better examples that demonstrate more of +the salient aspects, e.g. reasoning patterns, of the test input. We further +extend BSR and many standard metrics to easily optimizable set-level metrics, +giving still better coverage of those salient aspects. On 15 datasets spanning +6 tasks and with 7 diverse LLMs, we show that (1) BSR is the superior metric +for in-context example selection across the board, and (2) for compositional +tasks, set selection using Set-BSR outperforms independent ranking by up to 17 +points on average and, despite being training-free, surpasses methods that +leverage task or LLM-specific training. +" +Transformers learn to implement preconditioned gradient descent for in-context learning,Kwangjun Ahn,http://arxiv.org/pdf/2306.00297v1.pdf,2023-06-01,"['cs.lg', 'cs.ai']",2306.00297v1.pdf," Motivated by the striking ability of transformers for in-context learning, +several works demonstrate that transformers can implement algorithms like +gradient descent. By a careful construction of weights, these works show that +multiple layers of transformers are expressive enough to simulate gradient +descent iterations. Going beyond the question of expressivity, we ask: Can +transformers learn to implement such algorithms by training over random problem +instances? To our knowledge, we make the first theoretical progress toward this +question via analysis of the loss landscape for linear transformers trained +over random instances of linear regression. For a single attention layer, we +prove the global minimum of the training objective implements a single +iteration of preconditioned gradient descent. Notably, the preconditioning +matrix not only adapts to the input distribution but also to the variance +induced by data inadequacy. For a transformer with $k$ attention layers, we +prove certain critical points of the training objective implement $k$ +iterations of preconditioned gradient descent. Our results call for future +theoretical studies on learning algorithms by training transformers. +" +In-Context Learning User Simulators for Task-Oriented Dialog Systems,Silvia Terragni,http://arxiv.org/pdf/2306.00774v1.pdf,2023-06-01,"['cs.cl', 'cs.lg']",2306.00774v1.pdf," This paper presents a novel application of large language models in user +simulation for task-oriented dialog systems, specifically focusing on an +in-context learning approach. By harnessing the power of these models, the +proposed approach generates diverse utterances based on user goals and limited +dialog examples. Unlike traditional simulators, this method eliminates the need +for labor-intensive rule definition or extensive annotated data, making it more +efficient and accessible. Additionally, an error analysis of the interaction +between the user simulator and dialog system uncovers common mistakes, +providing valuable insights into areas that require improvement. Our +implementation is available at +https://github.com/telepathylabsai/prompt-based-user-simulator. +" +Towards In-context Scene Understanding,Ivana Balažević,http://arxiv.org/pdf/2306.01667v2.pdf,2023-06-02,['cs.cv'],2306.01667v2.pdf," In-context learning$\unicode{x2013}$the ability to configure a model's +behavior with different prompts$\unicode{x2013}$has revolutionized the field of +natural language processing, alleviating the need for task-specific models and +paving the way for generalist models capable of assisting with any query. +Computer vision, in contrast, has largely stayed in the former regime: +specialized decoders and finetuning protocols are generally required to perform +dense tasks such as semantic segmentation and depth estimation. In this work we +explore a simple mechanism for in-context learning of such scene understanding +tasks: nearest neighbor retrieval from a prompt of annotated features. We +propose a new pretraining protocol$\unicode{x2013}$leveraging attention within +and across images$\unicode{x2013}$which yields representations particularly +useful in this regime. The resulting Hummingbird model, suitably prompted, +performs various scene understanding tasks without modification while +approaching the performance of specialists that have been finetuned for each +task. Moreover, Hummingbird can be configured to perform new tasks much more +efficiently than finetuned models, raising the possibility of scene +understanding in the interactive assistant regime. +" +Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding,Mu Cai,http://arxiv.org/pdf/2306.06094v1.pdf,2023-06-09,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2306.06094v1.pdf," Recently, large language models (LLMs) have made significant advancements in +natural language understanding and generation. However, their potential in +computer vision remains largely unexplored. In this paper, we introduce a new, +exploratory approach that enables LLMs to process images using the Scalable +Vector Graphics (SVG) format. By leveraging the XML-based textual descriptions +of SVG representations instead of raster images, we aim to bridge the gap +between the visual and textual modalities, allowing LLMs to directly understand +and manipulate images without the need for parameterized visual components. Our +method facilitates simple image classification, generation, and in-context +learning using only LLM capabilities. We demonstrate the promise of our +approach across discriminative and generative tasks, highlighting its (i) +robustness against distribution shift, (ii) substantial improvements achieved +by tapping into the in-context learning abilities of LLMs, and (iii) image +understanding and generation capabilities with human guidance. Our code, data, +and models can be found here https://github.com/mu-cai/svg-llm. +" +Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking,Qinyong Wang,http://arxiv.org/pdf/2307.01137v1.pdf,2023-07-03,"['cs.cl', 'cs.ai']",2307.01137v1.pdf," The biomedical field relies heavily on concept linking in various areas such +as literature mining, graph alignment, information retrieval, +question-answering, data, and knowledge integration. Although large language +models (LLMs) have made significant strides in many natural language processing +tasks, their effectiveness in biomedical concept mapping is yet to be fully +explored. This research investigates a method that exploits the in-context +learning (ICL) capabilities of large models for biomedical concept linking. The +proposed approach adopts a two-stage retrieve-and-rank framework. Initially, +biomedical concepts are embedded using language models, and then embedding +similarity is utilized to retrieve the top candidates. These candidates' +contextual information is subsequently incorporated into the prompt and +processed by a large language model to re-rank the concepts. This approach +achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7% +in chemical entity normalization, exhibiting a competitive performance relative +to supervised learning methods. Further, it showed a significant improvement, +with an over 20-point absolute increase in F1 score on an oncology matching +dataset. Extensive qualitative assessments were conducted, and the benefits and +potential shortcomings of using large language models within the biomedical +domain were discussed. were discussed. +" +Learning to Retrieve In-Context Examples for Large Language Models,Liang Wang,http://arxiv.org/pdf/2307.07164v1.pdf,2023-07-14,"['cs.cl', 'cs.ir']",2307.07164v1.pdf," Large language models (LLMs) have demonstrated their ability to learn +in-context, allowing them to perform various tasks based on a few input-output +examples. However, the effectiveness of in-context learning is heavily reliant +on the quality of the selected examples. In this paper, we propose a novel +framework to iteratively train dense retrievers that can identify high-quality +in-context examples for LLMs. Our framework initially trains a reward model +based on LLM feedback to evaluate the quality of candidate examples, followed +by knowledge distillation to train a bi-encoder based dense retriever. Our +experiments on a suite of 30 tasks demonstrate that our framework significantly +enhances in-context learning performance. Furthermore, we show the +generalization ability of our framework to unseen tasks during training. An +in-depth analysis reveals that our model improves performance by retrieving +examples with similar patterns, and the gains are consistent across LLMs of +varying sizes. +" +In-Context Learning Learns Label Relationships but Is Not Conventional Learning,Jannik Kossen,http://arxiv.org/pdf/2307.12375v3.pdf,2023-07-23,"['cs.cl', 'cs.ai', 'cs.lg']",2307.12375v3.pdf," The predictions of Large Language Models (LLMs) on downstream tasks often +improve significantly when including examples of the input--label relationship +in the context. However, there is currently no consensus about how this +in-context learning (ICL) ability of LLMs works. For example, while Xie et al. +(2021) liken ICL to a general-purpose learning algorithm, Min et al. (2022) +argue ICL does not even learn label relationships from in-context examples. In +this paper, we provide novel insights into how ICL leverages label information, +revealing both capabilities and limitations. To ensure we obtain a +comprehensive picture of ICL behavior, we study probabilistic aspects of ICL +predictions and thoroughly examine the dynamics of ICL as more examples are +provided. Our experiments show that ICL predictions almost always depend on +in-context labels, and that ICL can learn truly novel tasks in-context. +However, we also find that ICL struggles to fully overcome prediction +preferences acquired from pre-training data, and, further, that ICL does not +consider all in-context information equally. +" +Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning,Xindi Wang,http://arxiv.org/pdf/2307.15411v2.pdf,2023-07-28,['cs.cl'],2307.15411v2.pdf," Large language models (LLMs) have shown remarkable capacity for in-context +learning (ICL), where learning a new task from just a few training examples is +done without being explicitly pre-trained. However, despite the success of +LLMs, there has been little understanding of how ICL learns the knowledge from +the given prompts. In this paper, to make progress toward understanding the +learning behaviour of ICL, we train the same LLMs with the same demonstration +examples via ICL and supervised learning (SL), respectively, and investigate +their performance under label perturbations (i.e., noisy labels and label +imbalance) on a range of classification tasks. First, via extensive +experiments, we find that gold labels have significant impacts on the +downstream in-context performance, especially for large language models; +however, imbalanced labels matter little to ICL across all model sizes. Second, +when comparing with SL, we show empirically that ICL is less sensitive to label +perturbations than SL, and ICL gradually attains comparable performance to SL +as the model size increases. +" +Exploring Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning,Hunter McNichols,http://arxiv.org/pdf/2308.03234v1.pdf,2023-08-07,['cs.cl'],2308.03234v1.pdf," Multiple-choice questions (MCQs) are ubiquitous in almost all levels of +education since they are easy to administer, grade, and are a reliable format +in both assessments and practices. An important aspect of MCQs is the +distractors, i.e., incorrect options that are designed to target specific +misconceptions or insufficient knowledge among students. To date, the task of +crafting high-quality distractors has largely remained a labor-intensive +process for teachers and learning content designers, which has limited +scalability. In this work, we explore the task of automated distractor and +corresponding feedback message generation in math MCQs using large language +models. We establish a formulation of these two tasks and propose a simple, +in-context learning-based solution. Moreover, we explore using two non-standard +metrics to evaluate the quality of the generated distractors and feedback +messages. We conduct extensive experiments on these tasks using a real-world +MCQ dataset that contains student response information. Our findings suggest +that there is a lot of room for improvement in automated distractor and +feedback generation. We also outline several directions for future work +" +CausalLM is not optimal for in-context learning,Nan Ding,http://arxiv.org/pdf/2308.06912v2.pdf,2023-08-14,"['cs.lg', 'cs.cl']",2308.06912v2.pdf," Recent empirical evidence indicates that transformer based in-context +learning performs better when using a prefix language model (prefixLM), in +which in-context samples can all attend to each other, compared to causal +language models (causalLM), which use auto-regressive attention that prohibits +in-context samples to attend to future samples. While this result is intuitive, +it is not understood from a theoretical perspective. In this paper we take a +theoretical approach and analyze the convergence behavior of prefixLM and +causalLM under a certain parameter construction. Our analysis shows that both +LM types converge to their stationary points at a linear rate, but that while +prefixLM converges to the optimal solution of linear regression, causalLM +convergence dynamics follows that of an online gradient descent algorithm, +which is not guaranteed to be optimal even as the number of samples grows +infinitely. We supplement our theoretical claims with empirical experiments +over synthetic and real tasks and using various types of transformers. Our +experiments verify that causalLM consistently underperforms prefixLM in all +settings. +" +Exploring Demonstration Ensembling for In-context Learning,Muhammad Khalifa,http://arxiv.org/pdf/2308.08780v2.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.08780v2.pdf," In-context learning (ICL) operates by showing language models (LMs) examples +of input-output pairs for a given task, i.e., demonstrations. The standard +approach for ICL is to prompt the LM with concatenated demonstrations followed +by the test input. This approach suffers from some issues. First, concatenation +offers almost no control over the contribution of each demo to the model +prediction. This can be sub-optimal when some demonstrations are irrelevant to +the test example. Second, due to the input length limit of some transformer +models, it might be infeasible to fit many examples into the context, +especially when dealing with long-input tasks. In this work, we explore +Demonstration Ensembling (DENSE) as an alternative to simple concatenation. +DENSE predicts outputs using subsets (i.e., buckets) of the demonstrations and +then combines the output probabilities resulting from each subset to produce +the final prediction. We study different ensembling methods using GPT-j and +experiment on 12 language tasks. Our experiments show weighted max ensembling +to outperform vanilla concatenation by as large as 2.4 average points. Code +available at https://github.com/mukhal/icl-ensembling. +" +Context is Environment,Sharut Gupta,http://arxiv.org/pdf/2309.09888v2.pdf,2023-09-18,"['cs.lg', 'cs.ai', 'stat.ml']",2309.09888v2.pdf," Two lines of work are taking the central stage in AI research. On the one +hand, the community is making increasing efforts to build models that discard +spurious correlations and generalize better in novel test environments. +Unfortunately, the bitter lesson so far is that no proposal convincingly +outperforms a simple empirical risk minimization baseline. On the other hand, +large language models (LLMs) have erupted as algorithms able to learn +in-context, generalizing on-the-fly to eclectic contextual circumstances that +users enforce by means of prompting. In this paper, we argue that context is +environment, and posit that in-context learning holds the key to better domain +generalization. Via extensive theory and experiments, we show that paying +attention to context$\unicode{x2013}\unicode{x2013}$unlabeled examples as they +arrive$\unicode{x2013}\unicode{x2013}$allows our proposed In-Context Risk +Minimization (ICRM) algorithm to zoom-in on the test environment risk +minimizer, leading to significant out-of-distribution performance improvements. +From all of this, two messages are worth taking home. Researchers in domain +generalization should consider environment as context, and harness the adaptive +power of in-context learning. Researchers in LLMs should consider context as +environment, to better structure data towards generalization. +" +"Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning",Peter Ebert Christensen,http://arxiv.org/pdf/2309.10359v1.pdf,2023-09-19,['cs.cl'],2309.10359v1.pdf," Unsupported and unfalsifiable claims we encounter in our daily lives can +influence our view of the world. Characterizing, summarizing, and -- more +generally -- making sense of such claims, however, can be challenging. In this +work, we focus on fine-grained debate topics and formulate a new task of +distilling, from such claims, a countable set of narratives. We present a +crowdsourced dataset of 12 controversial topics, comprising more than 120k +arguments, claims, and comments from heterogeneous sources, each annotated with +a narrative label. We further investigate how large language models (LLMs) can +be used to synthesise claims using In-Context Learning. We find that generated +claims with supported evidence can be used to improve the performance of +narrative classification models and, additionally, that the same model can +infer the stance and aspect using a few training examples. Such a model can be +useful in applications which rely on narratives , e.g. fact-checking. +" +In-Context Learning for Text Classification with Many Labels,Aristides Milios,http://arxiv.org/pdf/2309.10954v1.pdf,2023-09-19,"['cs.cl', 'cs.lg']",2309.10954v1.pdf," In-context learning (ICL) using large language models for tasks with many +labels is challenging due to the limited context window, which makes it +difficult to fit a sufficient number of examples in the prompt. In this paper, +we use a pre-trained dense retrieval model to bypass this limitation, giving +the model only a partial view of the full label space for each inference call. +Testing with recent open-source LLMs (OPT, LLaMA), we set new state of the art +performance in few-shot settings for three common intent classification +datasets, with no finetuning. We also surpass fine-tuned performance on +fine-grained sentiment classification in certain cases. We analyze the +performance across number of in-context examples and different model scales, +showing that larger models are necessary to effectively and consistently make +use of larger context lengths for ICL. By running several ablations, we analyze +the model's use of: a) the similarity of the in-context examples to the current +input, b) the semantic content of the class names, and c) the correct +correspondence between examples and labels. We demonstrate that all three are +needed to varying degrees depending on the domain, contrary to certain recent +works. +" +Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation,Xinyu Tang,http://arxiv.org/pdf/2309.11765v1.pdf,2023-09-21,"['cs.lg', 'cs.cr']",2309.11765v1.pdf," We study the problem of in-context learning (ICL) with large language models +(LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak +or regurgitate the private examples demonstrated in the prompt. We propose a +novel algorithm that generates synthetic few-shot demonstrations from the +private dataset with formal differential privacy (DP) guarantees, and show +empirically that it can achieve effective ICL. We conduct extensive experiments +on standard benchmarks and compare our algorithm with non-private ICL and +zero-shot solutions. Our results demonstrate that our algorithm can achieve +competitive performance with strong privacy levels. These results open up new +possibilities for ICL with privacy protection for a broad range of +applications. +" +HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering,Tongxu Luo,http://arxiv.org/pdf/2309.12669v1.pdf,2023-09-22,['cs.cl'],2309.12669v1.pdf," Answering numerical questions over hybrid contents from the given tables and +text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs) +have gained significant attention in the NLP community. With the emergence of +large language models, In-Context Learning and Chain-of-Thought prompting have +become two particularly popular research topics in this field. In this paper, +we introduce a new prompting strategy called Hybrid prompt strategy and +Retrieval of Thought for TextTableQA. Through In-Context Learning, we prompt +the model to develop the ability of retrieval thinking when dealing with hybrid +data. Our method achieves superior performance compared to the fully-supervised +SOTA on the MultiHiertt dataset in the few-shot setting. +" +ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning,Hosein Hasanbeig,http://arxiv.org/pdf/2309.13701v2.pdf,2023-09-24,"['cs.cl', 'cs.ai', 'cs.hc']",2309.13701v2.pdf," From grading papers to summarizing medical documents, large language models +(LLMs) are evermore used for evaluation of text generated by humans and AI +alike. However, despite their extensive utility, LLMs exhibit distinct failure +modes, necessitating a thorough audit and improvement of their text evaluation +capabilities. Here we introduce ALLURE, a systematic approach to Auditing Large +Language Models Understanding and Reasoning Errors. ALLURE involves comparing +LLM-generated evaluations with annotated data, and iteratively incorporating +instances of significant deviation into the evaluator, which leverages +in-context learning (ICL) to enhance and improve robust evaluation of text by +LLMs. Through this iterative process, we refine the performance of the +evaluator LLM, ultimately reducing reliance on human annotators in the +evaluation process. We anticipate ALLURE to serve diverse applications of LLMs +in various domains related to evaluation of textual data, such as medical +summarization, education, and and productivity. +" +Dynamic Demonstrations Controller for In-Context Learning,Fei Zhao,http://arxiv.org/pdf/2310.00385v1.pdf,2023-09-30,"['cs.cl', 'cs.ai']",2310.00385v1.pdf," In-Context Learning (ICL) is a new paradigm for natural language processing +(NLP), where a large language model (LLM) observes a small number of +demonstrations and a test instance as its input, and directly makes predictions +without updating model parameters. Previous studies have revealed that ICL is +sensitive to the selection and the ordering of demonstrations. However, there +are few studies regarding the impact of the demonstration number on the ICL +performance within a limited input length of LLM, because it is commonly +believed that the number of demonstrations is positively correlated with model +performance. In this paper, we found this conclusion does not always hold true. +Through pilot experiments, we discover that increasing the number of +demonstrations does not necessarily lead to improved performance. Building upon +this insight, we propose a Dynamic Demonstrations Controller (D$^2$Controller), +which can improve the ICL performance by adjusting the number of demonstrations +dynamically. The experimental results show that D$^2$Controller yields a 5.4% +relative improvement on eight different sizes of LLMs across ten datasets. +Moreover, we also extend our method to previous ICL models and achieve +competitive results. +" +The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning,Tian Jin,http://arxiv.org/pdf/2310.04680v1.pdf,2023-10-07,"['cs.cl', 'cs.ai', 'cs.lg']",2310.04680v1.pdf," How does scaling the number of parameters in large language models (LLMs) +affect their core capabilities? We study two natural scaling techniques -- +weight pruning and simply training a smaller or larger model, which we refer to +as dense scaling -- and their effects on two core capabilities of LLMs: (a) +recalling facts presented during pre-training and (b) processing information +presented in-context during inference. By curating a suite of tasks that help +disentangle these two capabilities, we find a striking difference in how these +two abilities evolve due to scaling. Reducing the model size by more than 30\% +(via either scaling approach) significantly decreases the ability to recall +facts seen in pre-training. Yet, a 60--70\% reduction largely preserves the +various ways the model can process in-context information, ranging from +retrieving answers from a long context to learning parameterized functions from +in-context exemplars. The fact that both dense scaling and weight pruning +exhibit this behavior suggests that scaling model size has an inherently +disparate effect on fact recall and in-context learning. +" +Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning,Zhe Yang,http://arxiv.org/pdf/2310.08309v1.pdf,2023-10-12,['cs.cl'],2310.08309v1.pdf," Large Language Models (LLMs) have recently gained the In-Context Learning +(ICL) ability with the models scaling up, allowing them to quickly adapt to +downstream tasks with only a few demonstration examples prepended in the input +sequence. Nonetheless, the current practice of ICL treats all demonstration +examples equally, which still warrants improvement, as the quality of examples +is usually uneven. In this paper, we investigate how to determine approximately +optimal weights for demonstration examples and how to apply them during ICL. To +assess the quality of weights in the absence of additional validation data, we +design a masked self-prediction (MSP) score that exhibits a strong correlation +with the final ICL performance. To expedite the weight-searching process, we +discretize the continuous weight space and adopt beam search. With +approximately optimal weights obtained, we further propose two strategies to +apply them to demonstrations at different model positions. Experimental results +on 8 text classification tasks show that our approach outperforms conventional +ICL by a large margin. Our code are publicly available at +https:github.com/Zhe-Young/WICL. +" +How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?,Jingfeng Wu,http://arxiv.org/pdf/2310.08391v1.pdf,2023-10-12,"['stat.ml', 'cs.lg']",2310.08391v1.pdf," Transformers pretrained on diverse tasks exhibit remarkable in-context +learning (ICL) capabilities, enabling them to solve unseen tasks solely based +on input contexts without adjusting model parameters. In this paper, we study +ICL in one of its simplest setups: pretraining a linearly parameterized +single-layer linear attention model for linear regression with a Gaussian +prior. We establish a statistical task complexity bound for the attention model +pretraining, showing that effective pretraining only requires a small number of +independent tasks. Furthermore, we prove that the pretrained model closely +matches the Bayes optimal algorithm, i.e., optimally tuned ridge regression, by +achieving nearly Bayes optimal risk on unseen tasks under a fixed context +length. These theoretical findings complement prior experimental research and +shed light on the statistical foundations of ICL. +" +Generative Calibration for In-context Learning,Zhongtao Jiang,http://arxiv.org/pdf/2310.10266v1.pdf,2023-10-16,['cs.cl'],2310.10266v1.pdf," As one of the most exciting features of large language models (LLMs), +in-context learning is a mixed blessing. While it allows users to +fast-prototype a task solver with only a few training examples, the performance +is generally sensitive to various configurations of the prompt such as the +choice or order of the training examples. In this paper, we for the first time +theoretically and empirically identify that such a paradox is mainly due to the +label shift of the in-context model to the data distribution, in which LLMs +shift the label marginal $p(y)$ while having a good label conditional $p(x|y)$. +With this understanding, we can simply calibrate the in-context predictive +distribution by adjusting the label marginal, which is estimated via +Monte-Carlo sampling over the in-context model, i.e., generation of LLMs. We +call our approach as generative calibration. We conduct exhaustive experiments +with 12 text classification tasks and 12 LLMs scaling from 774M to 33B, +generally find that the proposed method greatly and consistently outperforms +the ICL as well as state-of-the-art calibration methods, by up to 27% absolute +in macro-F1. Meanwhile, the proposed method is also stable under different +prompt configurations. +" +"Last One Standing: A Comparative Analysis of Security and Privacy of Soft Prompt Tuning, LoRA, and In-Context Learning",Rui Wen,http://arxiv.org/pdf/2310.11397v1.pdf,2023-10-17,"['cs.cr', 'cs.lg']",2310.11397v1.pdf," Large Language Models (LLMs) are powerful tools for natural language +processing, enabling novel applications and user experiences. However, to +achieve optimal performance, LLMs often require adaptation with private data, +which poses privacy and security challenges. Several techniques have been +proposed to adapt LLMs with private data, such as Low-Rank Adaptation (LoRA), +Soft Prompt Tuning (SPT), and In-Context Learning (ICL), but their comparative +privacy and security properties have not been systematically investigated. In +this work, we fill this gap by evaluating the robustness of LoRA, SPT, and ICL +against three types of well-established attacks: membership inference, which +exposes data leakage (privacy); backdoor, which injects malicious behavior +(security); and model stealing, which can violate intellectual property +(privacy and security). Our results show that there is no silver bullet for +privacy and security in LLM adaptation and each technique has different +strengths and weaknesses. +" +MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations,Arkil Patel,http://arxiv.org/pdf/2310.11634v1.pdf,2023-10-18,['cs.cl'],2310.11634v1.pdf," Humans possess a remarkable ability to assign novel interpretations to +linguistic expressions, enabling them to learn new words and understand +community-specific connotations. However, Large Language Models (LLMs) have a +knowledge cutoff and are costly to finetune repeatedly. Therefore, it is +crucial for LLMs to learn novel interpretations in-context. In this paper, we +systematically analyse the ability of LLMs to acquire novel interpretations +using in-context learning. To facilitate our study, we introduce MAGNIFICo, an +evaluation suite implemented within a text-to-SQL semantic parsing framework +that incorporates diverse tokens and prompt settings to simulate real-world +complexity. Experimental results on MAGNIFICo demonstrate that LLMs exhibit a +surprisingly robust capacity for comprehending novel interpretations from +natural language descriptions as well as from discussions within long +conversations. Nevertheless, our findings also highlight the need for further +improvements, particularly when interpreting unfamiliar words or when composing +multiple novel interpretations simultaneously in the same example. +Additionally, our analysis uncovers the semantic predispositions in LLMs and +reveals the impact of recency bias for information presented in long contexts. +" +In-context Learning with Transformer Is Really Equivalent to a Contrastive Learning Pattern,Ruifeng Ren,http://arxiv.org/pdf/2310.13220v1.pdf,2023-10-20,['cs.lg'],2310.13220v1.pdf," Pre-trained large language models based on Transformers have demonstrated +amazing in-context learning (ICL) abilities. Given several demonstration +examples, the models can implement new tasks without any parameter updates. +However, it is still an open question to understand the mechanism of ICL. In +this paper, we interpret the inference process of ICL as a gradient descent +process in a contrastive learning pattern. Firstly, leveraging kernel methods, +we establish the relationship between gradient descent and self-attention +mechanism under generally used softmax attention setting instead of linear +attention setting. Then, we analyze the corresponding gradient descent process +of ICL from the perspective of contrastive learning without negative samples +and discuss possible improvements of this contrastive learning pattern, based +on which the self-attention layer can be further modified. Finally, we design +experiments to support our opinions. To the best of our knowledge, our work is +the first to provide the understanding of ICL from the perspective of +contrastive learning and has the potential to facilitate future model design by +referring to related works on contrastive learning. +" +In-Context Learning Creates Task Vectors,Roee Hendel,http://arxiv.org/pdf/2310.15916v1.pdf,2023-10-24,['cs.cl'],2310.15916v1.pdf," In-context learning (ICL) in Large Language Models (LLMs) has emerged as a +powerful new learning paradigm. However, its underlying mechanism is still not +well understood. In particular, it is challenging to map it to the ""standard"" +machine learning framework, where one uses a training set $S$ to find a +best-fitting function $f(x)$ in some hypothesis class. Here we make progress on +this problem by showing that the functions learned by ICL often have a very +simple structure: they correspond to the transformer LLM whose only inputs are +the query $x$ and a single ""task vector"" calculated from the training set. +Thus, ICL can be seen as compressing $S$ into a single task vector +$\boldsymbol{\theta}(S)$ and then using this task vector to modulate the +transformer to produce the output. We support the above claim via comprehensive +experiments across a range of models and tasks. +" +When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations,Aleksandar Petrov,http://arxiv.org/pdf/2310.19698v1.pdf,2023-10-30,"['cs.lg', 'cs.cl']",2310.19698v1.pdf," Context-based fine-tuning methods, including prompting, in-context learning, +soft prompting (also known as prompt tuning), and prefix-tuning, have gained +popularity due to their ability to often match the performance of full +fine-tuning with a fraction of the parameters. Despite their empirical +successes, there is little theoretical understanding of how these techniques +influence the internal computation of the model and their expressiveness +limitations. We show that despite the continuous embedding space being more +expressive than the discrete token space, soft-prompting and prefix-tuning are +strictly less expressive than full fine-tuning, even with the same number of +learnable parameters. Concretely, context-based fine-tuning cannot change the +relative attention pattern over the content and can only bias the outputs of an +attention layer in a fixed direction. This suggests that while techniques like +prompting, in-context learning, soft prompting, and prefix-tuning can +effectively elicit skills present in the pretrained model, they cannot learn +novel tasks that require new attention patterns. +" +Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection,Costas Mavromatis,http://arxiv.org/pdf/2310.20046v1.pdf,2023-10-30,['cs.cl'],2310.20046v1.pdf," Large Language Models (LLMs) can adapt to new tasks via in-context learning +(ICL). ICL is efficient as it does not require any parameter updates to the +trained LLM, but only few annotated examples as input for the LLM. In this +work, we investigate an active learning approach for ICL, where there is a +limited budget for annotating examples. We propose a model-adaptive +optimization-free algorithm, termed AdaICL, which identifies examples that the +model is uncertain about, and performs semantic diversity-based example +selection. Diversity-based sampling improves overall effectiveness, while +uncertainty sampling improves budget efficiency and helps the LLM learn new +information. Moreover, AdaICL poses its sampling strategy as a Maximum Coverage +problem, that dynamically adapts based on the model's feedback and can be +approximately solved via greedy algorithms. Extensive experiments on nine +datasets and seven LLMs show that AdaICL improves performance by 4.4% accuracy +points over SOTA (7.7% relative improvement), is up to 3x more budget-efficient +than performing annotations uniformly at random, while it outperforms SOTA with +2x fewer ICL examples. +" +DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase,Dawei Li,http://arxiv.org/pdf/2311.03319v1.pdf,2023-11-06,"['cs.cl', 'cs.ai']",2311.03319v1.pdf," In-Context Learning (ICL) combined with pre-trained large language models has +achieved promising results on various NLP tasks. However, ICL requires +high-quality annotated demonstrations which might not be available in +real-world scenarios. To overcome this limitation, we propose \textbf{D}ata +\textbf{A}ugmentation for \textbf{I}n-Context \textbf{L}earning +(\textbf{DAIL}). DAIL leverages the intuition that large language models are +more familiar with the content generated by themselves. It first utilizes the +language model to generate paraphrases of the test sample and employs majority +voting to determine the final result based on individual predictions. Our +extensive empirical evaluation shows that DAIL outperforms the standard ICL +method and other ensemble-based methods in the low-resource scenario. +Additionally, we explore the use of voting consistency as a confidence score of +the model when the logits of predictions are inaccessible. We believe our work +will stimulate further research on ICL in low-resource settings. +" +In-Context Exemplars as Clues to Retrieving from Large Associative Memory,Jiachen Zhao,http://arxiv.org/pdf/2311.03498v1.pdf,2023-11-06,"['cs.cl', 'cs.lg']",2311.03498v1.pdf," Recently, large language models (LLMs) have made remarkable progress in +natural language processing. The most representative ability of LLMs is +in-context learning (ICL), which enables LLMs to learn patterns from in-context +exemplars without training. The performance of ICL greatly depends on the +exemplars used. However, how to choose exemplars remains unclear due to the +lack of understanding of how in-context learning works. In this paper, we +present a novel perspective on ICL by conceptualizing it as contextual +retrieval from a model of associative memory. We establish a theoretical +framework of ICL based on Hopfield Networks. Based on our framework, we look +into how in-context exemplars influence the performance of ICL and propose more +efficient active exemplar selection. Our study sheds new light on the mechanism +of ICL by connecting it to memory retrieval, with potential implications for +advancing the understanding of LLMs. +" +Instruct Me More! Random Prompting for Visual In-Context Learning,Jiahao Zhang,http://arxiv.org/pdf/2311.03648v1.pdf,2023-11-07,['cs.cv'],2311.03648v1.pdf," Large-scale models trained on extensive datasets, have emerged as the +preferred approach due to their high generalizability across various tasks. +In-context learning (ICL), a popular strategy in natural language processing, +uses such models for different tasks by providing instructive prompts but +without updating model parameters. This idea is now being explored in computer +vision, where an input-output image pair (called an in-context pair) is +supplied to the model with a query image as a prompt to exemplify the desired +output. The efficacy of visual ICL often depends on the quality of the prompts. +We thus introduce a method coined Instruct Me More (InMeMo), which augments +in-context pairs with a learnable perturbation (prompt), to explore its +potential. Our experiments on mainstream tasks reveal that InMeMo surpasses the +current state-of-the-art performance. Specifically, compared to the baseline +without learnable prompt, InMeMo boosts mIoU scores by 7.35 and 15.13 for +foreground segmentation and single object detection tasks, respectively. Our +findings suggest that InMeMo offers a versatile and efficient way to enhance +the performance of visual ICL with lightweight training. Code is available at +https://github.com/Jackieam/InMeMo. +" +Selective Annotation Makes Language Models Better Few-Shot Learners,Hongjin Su,http://arxiv.org/pdf/2209.01975v1.pdf,2022-09-05,['cs.cl'],2209.01975v1.pdf," Many recent approaches to natural language tasks are built on the remarkable +abilities of large language models. Large language models can perform +in-context learning, where they learn a new task from a few task +demonstrations, without any parameter updates. This work examines the +implications of in-context learning for the creation of datasets for new +natural language tasks. Departing from recent in-context learning methods, we +formulate an annotation-efficient, two-step framework: selective annotation +that chooses a pool of examples to annotate from unlabeled data in advance, +followed by prompt retrieval that retrieves task examples from the annotated +pool at test time. Based on this framework, we propose an unsupervised, +graph-based selective annotation method, voke-k, to select diverse, +representative examples to annotate. Extensive experiments on 10 datasets +(covering classification, commonsense reasoning, dialogue, and text/code +generation) demonstrate that our selective annotation method improves the task +performance by a large margin. On average, vote-k achieves a 12.9%/11.4% +relative gain under an annotation budget of 18/100, as compared to randomly +selecting examples to annotate. Compared to state-of-the-art supervised +finetuning approaches, it yields similar performance with 10-100x less +annotation cost across 10 tasks. We further analyze the effectiveness of our +framework in various scenarios: language models with varying sizes, alternative +selective annotation methods, and cases where there is a test data domain +shift. We hope that our studies will serve as a basis for data annotations as +large language models are increasingly applied to new tasks. Our code is +available at https://github.com/HKUNLP/icl-selective-annotation. +" +In-context Example Selection with Influences,Tai Nguyen,http://arxiv.org/pdf/2302.11042v2.pdf,2023-02-21,"['cs.cl', 'cs.lg']",2302.11042v2.pdf," In-context learning (ICL) is a powerful paradigm emerged from large language +models (LLMs). Despite its promises, ICL performance is known to be highly +sensitive to input examples. In this work, we use $\textit{in-context +influences}$ to analyze few-shot ICL performance directly from the in-context +examples. Our proposed influence-based example selection method can identify +both positive and negative examples, outperforming several baselines when +evaluated on 9 SuperGLUE tasks. Our analysis uncovers up to a $16.3\%$ +performance gap between using the most negative in-context examples compared to +the most positive. In a case study, we apply our influence-based framework to +quantify the phenomena of recency bias in example ordering for few-shot ICL. +" +In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning,Xiaochuang Han,http://arxiv.org/pdf/2308.04275v1.pdf,2023-08-08,"['cs.cl', 'cs.ai', 'cs.lg']",2308.04275v1.pdf," In this note, we explore inference-time alignment through in-context +learning. We consider a vanilla pretrained language model Llama-2 before any +fine-tuning and retrieve an average of 9 demonstration alignment examples when +the model is prompted to follow chat-style instructions. Compared to direct +prompting, the in-context alignment without changing model weights leads to a +7x increase in win-rate w.r.t. the text-davinci-003 model from OpenAI, making +the vanilla language model comparable to strong baselines with alignment +fine-tuning. +" +"Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs",Ananya Singha,http://arxiv.org/pdf/2310.10358v1.pdf,2023-10-16,"['cs.cl', 'cs.ai']",2310.10358v1.pdf," Large language models (LLMs) are increasingly applied for tabular tasks using +in-context learning. The prompt representation for a table may play a role in +the LLMs ability to process the table. Inspired by prior work, we generate a +collection of self-supervised structural tasks (e.g. navigate to a cell and +row; transpose the table) and evaluate the performance differences when using 8 +formats. In contrast to past work, we introduce 8 noise operations inspired by +real-world messy data and adversarial inputs, and show that such operations can +impact LLM performance across formats for different structural understanding +tasks. +" +GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset,Ruibo Chen,http://arxiv.org/pdf/2310.18498v1.pdf,2023-10-27,"['eess.iv', 'cs.cv', 'cs.lg']",2310.18498v1.pdf," This technical report delves into the application of GPT-4 Vision (GPT-4V) in +the nuanced realm of COVID-19 image classification, leveraging the +transformative potential of in-context learning to enhance diagnostic +processes. +" +Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning,Haokun Liu,http://arxiv.org/pdf/2205.05638v2.pdf,2022-05-11,"['cs.lg', 'cs.ai', 'cs.cl']",2205.05638v2.pdf," Few-shot in-context learning (ICL) enables pre-trained language models to +perform a previously-unseen task without any gradient-based training by feeding +a small number of training examples as part of the input. ICL incurs +substantial computational, memory, and storage costs because it involves +processing all of the training examples every time a prediction is made. +Parameter-efficient fine-tuning (PEFT) (e.g. adapter modules, prompt tuning, +sparse update methods, etc.) offers an alternative paradigm where a small set +of parameters are trained to enable a model to perform the new task. In this +paper, we rigorously compare few-shot ICL and PEFT and demonstrate that the +latter offers better accuracy as well as dramatically lower computational +costs. Along the way, we introduce a new PEFT method called (IA)$^3$ that +scales activations by learned vectors, attaining stronger performance while +only introducing a relatively tiny amount of new parameters. We also propose a +simple recipe based on the T0 model called T-Few that can be applied to new +tasks without task-specific tuning or modifications. We validate the +effectiveness of T-Few on completely unseen tasks by applying it to the RAFT +benchmark, attaining super-human performance for the first time and +outperforming the state-of-the-art by 6% absolute. All of the code used in our +experiments is publicly available. +" +Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing,Linlu Qiu,http://arxiv.org/pdf/2205.12253v2.pdf,2022-05-24,['cs.cl'],2205.12253v2.pdf," Despite their strong performance on many tasks, pre-trained language models +have been shown to struggle on out-of-distribution compositional +generalization. Meanwhile, recent work has shown considerable improvements on +many NLP tasks from model scaling. Can scaling up model size also improve +compositional generalization in semantic parsing? We evaluate encoder-decoder +models up to 11B parameters and decoder-only models up to 540B parameters, and +compare model scaling curves for three different methods for applying a +pre-trained language model to a new task: fine-tuning all parameters, prompt +tuning, and in-context learning. We observe that fine-tuning generally has flat +or negative scaling curves on out-of-distribution compositional generalization +in semantic parsing evaluations. In-context learning has positive scaling +curves, but is generally outperformed by much smaller fine-tuned models. +Prompt-tuning can outperform fine-tuning, suggesting further potential +improvements from scaling as it exhibits a more positive scaling curve. +Additionally, we identify several error trends that vary with model scale. For +example, larger models are generally better at modeling the syntax of the +output space, but are also more prone to certain types of overfitting. Overall, +our study highlights limitations of current techniques for effectively +leveraging model scale for compositional generalization, while our analysis +also suggests promising directions for future work. +" +Controllable Dialogue Simulation with In-Context Learning,Zekun Li,http://arxiv.org/pdf/2210.04185v4.pdf,2022-10-09,"['cs.cl', 'cs.ai']",2210.04185v4.pdf," Building dialogue systems requires a large corpus of annotated dialogues. +Such datasets are usually created via crowdsourcing, which is expensive and +time-consuming. In this paper, we propose \textsc{Dialogic}, a novel dialogue +simulation method based on large language model in-context learning to automate +dataset creation. Seeded with a few annotated dialogues, \textsc{Dialogic} +automatically selects in-context examples for demonstration and prompts GPT-3 +to generate new dialogues and annotations in a controllable way. Our method can +rapidly expand a small set of dialogue data with minimum or zero \textit{human +involvement} and \textit{parameter update} and is thus much more cost-efficient +and time-saving than crowdsourcing. Experimental results on the MultiWOZ +dataset demonstrate that training a model on the simulated dialogues leads to +even better performance than using the same amount of human-generated dialogues +under the challenging low-resource settings, with as few as 85 dialogues as a +seed. When enough data is available, our method can still serve as an effective +data augmentation method. Human evaluation results also show that our simulated +dialogues have near-human fluency and annotation accuracy. The code and data +are available at \textbf{\url{https://github.com/Leezekun/dialogic}}. +" +XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing,Peng Shi,http://arxiv.org/pdf/2210.13693v1.pdf,2022-10-25,['cs.cl'],2210.13693v1.pdf," In-context learning using large language models has recently shown surprising +results for semantic parsing tasks such as Text-to-SQL translation. Prompting +GPT-3 or Codex using several examples of question-SQL pairs can produce +excellent results, comparable to state-of-the-art finetuning-based models. +However, existing work primarily focuses on English datasets, and it is unknown +whether large language models can serve as competitive semantic parsers for +other languages. To bridge this gap, our work focuses on cross-lingual +Text-to-SQL semantic parsing for translating non-English utterances into SQL +queries based on an English schema. We consider a zero-shot transfer learning +setting with the assumption that we do not have any labeled examples in the +target language (but have annotated examples in English). This work introduces +the XRICL framework, which learns to retrieve relevant English exemplars for a +given query to construct prompts. We also include global translation exemplars +for a target language to facilitate the translation process for large language +models. To systematically evaluate our model, we construct two new benchmark +datasets, XSpider and XKaggle-dbqa, which include questions in Chinese, +Vietnamese, Farsi, and Hindi. Our experiments show that XRICL effectively +leverages large pre-trained language models to outperform existing baselines. +Data and code are publicly available at https://github.com/Impavidity/XRICL. +" +Images Speak in Images: A Generalist Painter for In-Context Visual Learning,Xinlong Wang,http://arxiv.org/pdf/2212.02499v2.pdf,2022-12-05,['cs.cv'],2212.02499v2.pdf," In-context learning, as a new paradigm in NLP, allows the model to rapidly +adapt to various tasks with only a handful of prompts and examples. But in +computer vision, the difficulties for in-context learning lie in that tasks +vary significantly in the output representations, thus it is unclear how to +define the general-purpose task prompts that the vision model can understand +and transfer to out-of-domain tasks. In this work, we present Painter, a +generalist model which addresses these obstacles with an ""image""-centric +solution, that is, to redefine the output of core vision tasks as images, and +specify task prompts as also images. With this idea, our training process is +extremely simple, which performs standard masked image modeling on the stitch +of input and output image pairs. This makes the model capable of performing +tasks conditioned on visible image patches. Thus, during inference, we can +adopt a pair of input and output images from the same task as the input +condition, to indicate which task to perform. Without bells and whistles, our +generalist Painter can achieve competitive performance compared to +well-established task-specific models, on seven representative vision tasks +ranging from high-level visual understanding to low-level image processing. In +addition, Painter significantly outperforms recent generalist models on several +challenging tasks. +" +General-Purpose In-Context Learning by Meta-Learning Transformers,Louis Kirsch,http://arxiv.org/pdf/2212.04458v1.pdf,2022-12-08,"['cs.lg', 'cs.ai', 'cs.ne', 'stat.ml']",2212.04458v1.pdf," Modern machine learning requires system designers to specify aspects of the +learning pipeline, such as losses, architectures, and optimizers. +Meta-learning, or learning-to-learn, instead aims to learn those aspects, and +promises to unlock greater capabilities with less manual effort. One +particularly ambitious goal of meta-learning is to train general-purpose +in-context learning algorithms from scratch, using only black-box models with +minimal inductive bias. Such a model takes in training data, and produces +test-set predictions across a wide range of problems, without any explicit +definition of an inference model, training loss, or optimization algorithm. In +this paper we show that Transformers and other black-box models can be +meta-trained to act as general-purpose in-context learners. We characterize +phase transitions between algorithms that generalize, algorithms that memorize, +and algorithms that fail to meta-train at all, induced by changes in model +size, number of tasks, and meta-optimization. We further show that the +capabilities of meta-trained algorithms are bottlenecked by the accessible +state size (memory) determining the next prediction, unlike standard models +which are thought to be bottlenecked by parameter count. Finally, we propose +practical interventions such as biasing the training distribution that improve +the meta-training and meta-generalization of general-purpose learning +algorithms. +" +Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP,Omar Khattab,http://arxiv.org/pdf/2212.14024v2.pdf,2022-12-28,"['cs.cl', 'cs.ir']",2212.14024v2.pdf," Retrieval-augmented in-context learning has emerged as a powerful approach +for addressing knowledge-intensive tasks using frozen language models (LM) and +retrieval models (RM). Existing work has combined these in simple +""retrieve-then-read"" pipelines in which the RM retrieves passages that are +inserted into the LM prompt. To begin to fully realize the potential of frozen +LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that +relies on passing natural language texts in sophisticated pipelines between an +LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware +demonstrations, search for relevant passages, and generate grounded +predictions, systematically breaking down problems into small transformations +that the LM and RM can handle more reliably. We have written novel DSP programs +for answering questions in open-domain, multi-hop, and conversational settings, +establishing in early evaluations new state-of-the-art in-context learning +results and delivering 37-120%, 8-39%, and 80-290% relative gains against the +vanilla LM (GPT-3.5), a standard retrieve-then-read pipeline, and a +contemporaneous self-ask pipeline, respectively. We release DSP at +https://github.com/stanfordnlp/dsp +" +How Does In-Context Learning Help Prompt Tuning?,Simeng Sun,http://arxiv.org/pdf/2302.11521v1.pdf,2023-02-22,['cs.cl'],2302.11521v1.pdf," Fine-tuning large language models is becoming ever more impractical due to +their rapidly-growing scale. This motivates the use of parameter-efficient +adaptation methods such as prompt tuning (PT), which adds a small number of +tunable embeddings to an otherwise frozen model, and in-context learning (ICL), +in which demonstrations of the task are provided to the model in natural +language without any additional training. Recently, Singhal et al. (2022) +propose ``instruction prompt tuning'' (IPT), which combines PT with ICL by +concatenating a natural language demonstration with learned prompt embeddings. +While all of these methods have proven effective on different tasks, how they +interact with each other remains unexplored. In this paper, we empirically +study when and how in-context examples improve prompt tuning by measuring the +effectiveness of ICL, PT, and IPT on five text generation tasks with multiple +base language models. We observe that (1) IPT does \emph{not} always outperform +PT, and in fact requires the in-context demonstration to be semantically +similar to the test input to yield improvements; (2) PT is unstable and +exhibits high variance, but combining PT and ICL (into IPT) consistently +reduces variance across all five tasks; and (3) prompts learned for a specific +source task via PT exhibit positive transfer when paired with in-context +examples of a different target task. Our results offer actionable insights on +choosing a suitable parameter-efficient adaptation method for a given task. +" +Larger language models do in-context learning differently,Jerry Wei,http://arxiv.org/pdf/2303.03846v2.pdf,2023-03-07,['cs.cl'],2303.03846v2.pdf," We study how in-context learning (ICL) in language models is affected by +semantic priors versus input-label mappings. We investigate two setups-ICL with +flipped labels and ICL with semantically-unrelated labels-across various model +families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM). First, experiments +on ICL with flipped labels show that overriding semantic priors is an emergent +ability of model scale. While small language models ignore flipped labels +presented in-context and thus rely primarily on semantic priors from +pretraining, large models can override semantic priors when presented with +in-context exemplars that contradict priors, despite the stronger semantic +priors that larger models may hold. We next study semantically-unrelated label +ICL (SUL-ICL), in which labels are semantically unrelated to their inputs +(e.g., foo/bar instead of negative/positive), thereby forcing language models +to learn the input-label mappings shown in in-context exemplars in order to +perform the task. The ability to do SUL-ICL also emerges primarily with scale, +and large-enough language models can even perform linear classification in a +SUL-ICL setting. Finally, we evaluate instruction-tuned models and find that +instruction tuning strengthens both the use of semantic priors and the capacity +to learn input-label mappings, but more of the former. +" +How Many Demonstrations Do You Need for In-context Learning?,Jiuhai Chen,http://arxiv.org/pdf/2303.08119v3.pdf,2023-03-14,['cs.ai'],2303.08119v3.pdf," Large language models (LLMs) are capable to perform complex reasoning by +in-context learning (ICL) when provided with a few input-output demonstrations +(demos) and more powerful when intermediate reasoning steps (""chain of thoughts +(CoT)"") of the demos are given. Is it necessary to use multi-demo in ICL? In +this paper, we study ICL using fewer demos for each test query on the tasks +in~\cite{wei2022chain}. Surprisingly, we do not observe significant degradation +when using only one randomly chosen demo. To study this phenomenon, for each +test query, we categorize demos into ""correct demos"" leading to the correct +answer, and ""wrong demos"" resulting in wrong answers. Our analysis reveals an +inherent bias in those widely studied datasets: most demos are correct for a +majority of test queries, which explains the good performance of using one +random demo. Moreover, ICL (with and w/o CoT) using only one correct demo +significantly outperforms all-demo ICL adopted by most previous works, +indicating the weakness of LLMs in finding correct demo(s) for input queries, +which is difficult to evaluate on the biased datasets. Furthermore, we observe +a counterintuitive behavior of ICL using multi-demo, i.e., its accuracy +degrades(improves) when given more correct(wrong) demos. This implies that ICL +can be easily misguided by interference among demos and their spurious +correlations. Our analyses highlight several fundamental challenges that need +to be addressed in LLMs training, ICL, and benchmark design. +" +Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions,Jia-Hong Huang,http://arxiv.org/pdf/2304.03147v1.pdf,2023-04-06,"['cs.cv', 'cs.ai']",2304.03147v1.pdf," Deep neural networks have been critical in the task of Visual Question +Answering (VQA), with research traditionally focused on improving model +accuracy. Recently, however, there has been a trend towards evaluating the +robustness of these models against adversarial attacks. This involves assessing +the accuracy of VQA models under increasing levels of noise in the input, which +can target either the image or the proposed query question, dubbed the main +question. However, there is currently a lack of proper analysis of this aspect +of VQA. This work proposes a new method that utilizes semantically related +questions, referred to as basic questions, acting as noise to evaluate the +robustness of VQA models. It is hypothesized that as the similarity of a basic +question to the main question decreases, the level of noise increases. To +generate a reasonable noise level for a given main question, a pool of basic +questions is ranked based on their similarity to the main question, and this +ranking problem is cast as a LASSO optimization problem. Additionally, this +work proposes a novel robustness measure, R_score, and two basic question +datasets to standardize the analysis of VQA model robustness. The experimental +results demonstrate that the proposed evaluation method effectively analyzes +the robustness of VQA models. Moreover, the experiments show that in-context +learning with a chain of basic questions can enhance model accuracy. +" +GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information,Qiao Jin,http://arxiv.org/pdf/2304.09667v3.pdf,2023-04-19,"['cs.cl', 'cs.ai', 'q-bio.gn']",2304.09667v3.pdf," While large language models (LLMs) have been successfully applied to various +tasks, they still face challenges with hallucinations. Augmenting LLMs with +domain-specific tools such as database utilities can facilitate easier and more +precise access to specialized knowledge. In this paper, we present GeneGPT, a +novel method for teaching LLMs to use the Web APIs of the National Center for +Biotechnology Information (NCBI) for answering genomics questions. +Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs +by in-context learning and an augmented decoding algorithm that can detect and +execute API calls. Experimental results show that GeneGPT achieves +state-of-the-art performance on eight tasks in the GeneTuring benchmark with an +average score of 0.83, largely surpassing retrieval-augmented LLMs such as the +new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as +well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1) +API demonstrations have good cross-task generalizability and are more useful +than documentations for in-context learning; (2) GeneGPT can generalize to +longer chains of API calls and answer multi-hop questions in GeneHop, a novel +dataset introduced in this work; (3) Different types of errors are enriched in +different tasks, providing valuable insights for future improvements. +" +DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction,Mohammadreza Pourreza,http://arxiv.org/pdf/2304.11015v3.pdf,2023-04-21,"['cs.cl', 'cs.ai', 'cs.db', 'cs.hc']",2304.11015v3.pdf," There is currently a significant gap between the performance of fine-tuned +models and prompting approaches using Large Language Models (LLMs) on the +challenging task of text-to-SQL, as evaluated on datasets such as Spider. To +improve the performance of LLMs in the reasoning process, we study how +decomposing the task into smaller sub-tasks can be effective. In particular, we +show that breaking down the generation problem into sub-problems and feeding +the solutions of those sub-problems into LLMs can be an effective approach for +significantly improving their performance. Our experiments with three LLMs show +that this approach consistently improves their simple few-shot performance by +roughly 10%, pushing the accuracy of LLMs towards SOTA or surpassing it. On the +holdout test set of Spider, the SOTA, in terms of execution accuracy, was 79.9 +and the new SOTA at the time of this writing using our approach is 85.3. Our +approach with in-context learning beats many heavily fine-tuned models by at +least 5%. Additionally, when evaluated on the BIRD benchmark, our approach +achieved an execution accuracy of 55.9%, setting a new SOTA on its holdout test +set. +" +Few-shot In-context Learning for Knowledge Base Question Answering,Tianle Li,http://arxiv.org/pdf/2305.01750v2.pdf,2023-05-02,"['cs.cl', 'cs.ai']",2305.01750v2.pdf," Question answering over knowledge bases is considered a difficult problem due +to the challenge of generalizing to a wide variety of possible natural language +questions. Additionally, the heterogeneity of knowledge base schema items +between different knowledge bases often necessitates specialized training for +different knowledge base question-answering (KBQA) datasets. To handle +questions over diverse KBQA datasets with a unified training-free framework, we +propose KB-BINDER, which for the first time enables few-shot in-context +learning over KBQA tasks. Firstly, KB-BINDER leverages large language models +like Codex to generate logical forms as the draft for a specific question by +imitating a few demonstrations. Secondly, KB-BINDER grounds on the knowledge +base to bind the generated draft to an executable one with BM25 score matching. +The experimental results on four public heterogeneous KBQA datasets show that +KB-BINDER can achieve a strong performance with only a few in-context +demonstrations. Especially on GraphQA and 3-hop MetaQA, KB-BINDER can even +outperform the state-of-the-art trained models. On GrailQA and WebQSP, our +model is also on par with other fully-trained models. We believe KB-BINDER can +serve as an important baseline for future research. Our code is available at +https://github.com/ltl3A87/KB-BINDER. +" +How Do In-Context Examples Affect Compositional Generalization?,Shengnan An,http://arxiv.org/pdf/2305.04835v3.pdf,2023-05-08,"['cs.cl', 'cs.ai']",2305.04835v3.pdf," Compositional generalization--understanding unseen combinations of seen +primitives--is an essential reasoning capability in human intelligence. The AI +community mainly studies this capability by fine-tuning neural networks on lots +of training samples, while it is still unclear whether and how in-context +learning--the prevailing few-shot paradigm based on large language +models--exhibits compositional generalization. In this paper, we present CoFe, +a test suite to investigate in-context compositional generalization. We find +that the compositional generalization performance can be easily affected by the +selection of in-context examples, thus raising the research question what the +key factors are to make good in-context examples for compositional +generalization. We study three potential factors: similarity, diversity and +complexity. Our systematic experiments indicate that in-context examples should +be structurally similar to the test case, diverse from each other, and +individually simple. Furthermore, two strong limitations are observed: +in-context compositional generalization on fictional words is much weaker than +that on commonly used ones; it is still critical that the in-context examples +should cover required linguistic structures, even though the backbone model has +been pre-trained on large corpus. We hope our analysis would facilitate the +understanding and utilization of in-context learning paradigm. +" +Symbol tuning improves in-context learning in language models,Jerry Wei,http://arxiv.org/pdf/2305.08298v1.pdf,2023-05-15,['cs.cl'],2305.08298v1.pdf," We present symbol tuning - finetuning language models on in-context +input-label pairs where natural language labels (e.g., ""positive/negative +sentiment"") are replaced with arbitrary symbols (e.g., ""foo/bar""). Symbol +tuning leverages the intuition that when a model cannot use instructions or +natural language labels to figure out a task, it must instead do so by learning +the input-label mappings. + We experiment with symbol tuning across Flan-PaLM models up to 540B +parameters and observe benefits across various settings. First, symbol tuning +boosts performance on unseen in-context learning tasks and is much more robust +to underspecified prompts, such as those without instructions or without +natural language labels. Second, symbol-tuned models are much stronger at +algorithmic reasoning tasks, with up to 18.2% better performance on the List +Functions benchmark and up to 15.3% better performance on the Simple Turing +Concepts benchmark. Finally, symbol-tuned models show large improvements in +following flipped-labels presented in-context, meaning that they are more +capable of using in-context information to override prior semantic knowledge. +" +Text Classification via Large Language Models,Xiaofei Sun,http://arxiv.org/pdf/2305.08377v3.pdf,2023-05-15,['cs.cl'],2305.08377v3.pdf," Despite the remarkable success of large-scale Language Models (LLMs) such as +GPT-3, their performances still significantly underperform fine-tuned models in +the task of text classification. This is due to (1) the lack of reasoning +ability in addressing complex linguistic phenomena (e.g., intensification, +contrast, irony etc); (2) limited number of tokens allowed in in-context +learning. + In this paper, we introduce Clue And Reasoning Prompting (CARP). CARP adopts +a progressive reasoning strategy tailored to addressing the complex linguistic +phenomena involved in text classification: CARP first prompts LLMs to find +superficial clues (e.g., keywords, tones, semantic relations, references, etc), +based on which a diagnostic reasoning process is induced for final decisions. +To further address the limited-token issue, CARP uses a fine-tuned model on the +supervised dataset for $k$NN demonstration search in the in-context learning, +allowing the model to take the advantage of both LLM's generalization ability +and the task-specific evidence provided by the full labeled dataset. +Remarkably, CARP yields new SOTA performances on 4 out of 5 widely-used +text-classification benchmarks, 97.39 (+1.24) on SST-2, 96.40 (+0.72) on +AGNews, 98.78 (+0.25) on R8 and 96.95 (+0.6) on R52, and a performance +comparable to SOTA on MR (92.39 v.s. 93.3). More importantly, we find that CARP +delivers impressive abilities on low-resource and domain-adaptation setups. +Specifically, using 16 examples per class, CARP achieves comparable +performances to supervised models with 1,024 examples per class. +" +Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text,Hanieh Khorashadizadeh,http://arxiv.org/pdf/2305.08804v1.pdf,2023-05-15,['cs.cl'],2305.08804v1.pdf," Knowledge graphs can represent information about the real-world using +entities and their relations in a structured and semantically rich manner and +they enable a variety of downstream applications such as question-answering, +recommendation systems, semantic search, and advanced analytics. However, at +the moment, building a knowledge graph involves a lot of manual effort and thus +hinders their application in some situations and the automation of this process +might benefit especially for small organizations. Automatically generating +structured knowledge graphs from a large volume of natural language is still a +challenging task and the research on sub-tasks such as named entity extraction, +relation extraction, entity and relation linking, and knowledge graph +construction aims to improve the state of the art of automatic construction and +completion of knowledge graphs from text. The recent advancement of foundation +models with billions of parameters trained in a self-supervised manner with +large volumes of training data that can be adapted to a variety of downstream +tasks has helped to demonstrate high performance on a large range of Natural +Language Processing (NLP) tasks. In this context, one emerging paradigm is +in-context learning where a language model is used as it is with a prompt that +provides instructions and some examples to perform a task without changing the +parameters of the model using traditional approaches such as fine-tuning. This +way, no computing resources are needed for re-training/fine-tuning the models +and the engineering effort is minimal. Thus, it would be beneficial to utilize +such capabilities for generating knowledge graphs from text. +" +"What In-Context Learning ""Learns"" In-Context: Disentangling Task Recognition and Task Learning",Jane Pan,http://arxiv.org/pdf/2305.09731v1.pdf,2023-05-16,"['cs.cl', 'cs.lg']",2305.09731v1.pdf," Large language models (LLMs) exploit in-context learning (ICL) to solve tasks +with only a few demonstrations, but its mechanisms are not yet well-understood. +Some works suggest that LLMs only recall already learned concepts from +pre-training, while others hint that ICL performs implicit learning over +demonstrations. We characterize two ways through which ICL leverages +demonstrations. Task recognition (TR) captures the extent to which LLMs can +recognize a task through demonstrations -- even without ground-truth labels -- +and apply their pre-trained priors, whereas task learning (TL) is the ability +to capture new input-label mappings unseen in pre-training. Using a wide range +of classification datasets and three LLM families (GPT-3, LLaMA and OPT), we +design controlled experiments to disentangle the roles of TR and TL in ICL. We +show that (1) models can achieve non-trivial performance with only TR, and TR +does not further improve with larger models or more demonstrations; (2) LLMs +acquire TL as the model scales, and TL's performance consistently improves with +more demonstrations in context. Our findings unravel two different forces +behind ICL and we advocate for discriminating them in future ICL research due +to their distinct nature. +" +Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning,Dong-Ho Lee,http://arxiv.org/pdf/2305.10613v3.pdf,2023-05-17,['cs.cl'],2305.10613v3.pdf," Temporal knowledge graph (TKG) forecasting benchmarks challenge models to +predict future facts using knowledge of past facts. In this paper, we apply +large language models (LLMs) to these benchmarks using in-context learning +(ICL). We investigate whether and to what extent LLMs can be used for TKG +forecasting, especially without any fine-tuning or explicit modules for +capturing structural and temporal information. For our experiments, we present +a framework that converts relevant historical facts into prompts and generates +ranked predictions using token probabilities. Surprisingly, we observe that +LLMs, out-of-the-box, perform on par with state-of-the-art TKG models carefully +designed and trained for TKG forecasting. Our extensive evaluation presents +performances across several models and datasets with different characteristics, +compares alternative heuristics for preparing contextual information, and +contrasts to prominent TKG methods and simple frequency and recency baselines. +We also discover that using numerical indices instead of entity/relation names, +i.e., hiding semantic information, does not significantly affect the +performance ($\pm$0.4\% Hit@1). This shows that prior semantic knowledge is +unnecessary; instead, LLMs can leverage the existing patterns in the context to +achieve such performance. Our analysis also reveals that ICL enables LLMs to +learn irregular patterns from the historical context, going beyond simple +predictions based on common or recent information. +" +Learning In-context Learning for Named Entity Recognition,Jiawei Chen,http://arxiv.org/pdf/2305.11038v3.pdf,2023-05-18,['cs.cl'],2305.11038v3.pdf," Named entity recognition in real-world applications suffers from the +diversity of entity types, the emergence of new entity types, and the lack of +high-quality annotations. To address the above problems, this paper proposes an +in-context learning-based NER approach, which can effectively inject in-context +NER ability into PLMs and recognize entities of novel types on-the-fly using +only a few demonstrative instances. Specifically, we model PLMs as a +meta-function $\mathcal{ \lambda_ {\text{instruction, demonstrations, text}}. +M}$, and a new entity extractor can be implicitly constructed by applying new +instruction and demonstrations to PLMs, i.e., $\mathcal{ (\lambda . M) +}$(instruction, demonstrations) $\to$ $\mathcal{F}$ where $\mathcal{F}$ will be +a new entity extractor, i.e., $\mathcal{F}$: text $\to$ entities. To inject the +above in-context NER ability into PLMs, we propose a meta-function pre-training +algorithm, which pre-trains PLMs by comparing the (instruction, +demonstration)-initialized extractor with a surrogate golden extractor. +Experimental results on 4 few-shot NER datasets show that our method can +effectively inject in-context NER ability into PLMs and significantly +outperforms the PLMs+fine-tuning counterparts. +" +PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning,Chengfeng Dou,http://arxiv.org/pdf/2305.11508v2.pdf,2023-05-19,"['cs.cl', 'cs.ai', 'i.2.7']",2305.11508v2.pdf," The patient-centered medical dialogue systems strive to offer diagnostic +interpretation services to users who are less knowledgeable about medical +knowledge, through emphasizing the importance of providing responses specific +to the patients. It is difficult for the large language models (LLMs) to +guarantee the specificity of responses in spite of its promising performance +even in some tasks in medical field. Inspired by in-context learning, we +propose PlugMed, a Plug-and-Play Medical Dialogue System, for addressing this +challenge. PlugMed is equipped with two modules, the prompt generation (PG) +module and the response ranking (RR) module, to enhances LLMs' dialogue +strategies for improving the specificity of the dialogue. The PG module is +designed to stimulate the imitative ability of LLMs by providing them with real +dialogues from similar patients as prompts. The RR module incorporates +fine-tuned small model as response filter to enable the selection of +appropriate responses generated by LLMs. Furthermore, we introduce a new +evaluation method based on matching both user's intent and high-frequency +medical term to effectively assess the specificity of the responses. We conduct +experimental evaluations on three medical dialogue datasets, and the results, +including both automatic and human evaluation, demonstrate the effectiveness of +our approach. +" +ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings,Shibo Hao,http://arxiv.org/pdf/2305.11554v3.pdf,2023-05-19,"['cs.cl', 'cs.lg']",2305.11554v3.pdf," Augmenting large language models (LLMs) with external tools has emerged as a +promising approach to solving complex problems. However, traditional methods, +which finetune LLMs with tool demonstration data, can be both costly and +restricted to a predefined set of tools. Recent in-context learning paradigm +alleviates these issues, but the limited context length only allows for a few +shots of demonstrations, leading to suboptimal understandings of the tools. +Moreover, when there are numerous tools to choose from, in-context learning +could completely fail to work. In this paper, we propose an alternative +approach, $\textbf{ToolkenGPT}$, which combines the benefits of both sides. Our +approach represents each $\underline{tool}$ as a to$\underline{ken}$ +($\textit{toolken}$) and learns an embedding for it, enabling tool calls in the +same way as generating a regular word token. Once a toolken is triggered, the +LLM is prompted to complete arguments for the tool to execute. ToolkenGPT +offers the flexibility to plug in an arbitrary number of tools by expanding the +set of toolkens on the fly. In addition, it improves tool use by allowing +extensive demonstration data for learning the toolken embeddings. In diverse +domains, including numerical reasoning, knowledge-based question answering, and +embodied plan generation, our approach effectively augments LLMs with tools and +substantially outperforms various latest baselines. ToolkenGPT demonstrates the +promising ability to use relevant tools from a large tool set in complex +scenarios. +" +Iterative Forward Tuning Boosts In-context Learning in Language Models,Jiaxi Yang,http://arxiv.org/pdf/2305.13016v2.pdf,2023-05-22,['cs.cl'],2305.13016v2.pdf," Large language models (LLMs) have exhibited an emergent in-context learning +(ICL) ability. However, the ICL models that can solve ordinary cases are hardly +extended to solve more complex tasks by processing the demonstration examples +once. This single-turn ICL is incoordinate with the decision making process of +humans by learning from analogy. In this paper, we propose an effective and +efficient two-stage framework to boost ICL in LLMs by exploiting a dual form +between Transformer attention and gradient descent-based optimization. +Concretely, we divide the ICL process into ""Deep-Thinking"" and inference +stages. The ""Deep-Thinking"" stage performs iterative forward optimization of +demonstrations, which is expected to boost the reasoning abilities of LLMs at +test time by ""thinking"" demonstrations multiple times. It produces accumulated +meta-gradients by manipulating the Key-Value matrices in the self-attention +modules of the Transformer. Then, the inference stage only takes the test query +as input without concatenating demonstrations and applies the learned +meta-gradients through attention for output prediction. In this way, +demonstrations are not required during the inference stage since they are +already learned and stored in the definitive meta-gradients. LLMs can be +effectively and efficiently adapted to downstream tasks. Extensive experiments +on ten classification and multiple-choice datasets show that our method +achieves substantially better performance than standard ICL in terms of both +accuracy and efficiency. +" +Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations,Chenglei Si,http://arxiv.org/pdf/2305.13299v1.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.lg']",2305.13299v1.pdf," In-context learning (ICL) is an important paradigm for adapting large +language models (LLMs) to new tasks, but the generalization behavior of ICL +remains poorly understood. We investigate the inductive biases of ICL from the +perspective of feature bias: which feature ICL is more likely to use given a +set of underspecified demonstrations in which two features are equally +predictive of the labels. First, we characterize the feature biases of GPT-3 +models by constructing underspecified demonstrations from a range of NLP +datasets and feature combinations. We find that LLMs exhibit clear feature +biases - for example, demonstrating a strong bias to predict labels according +to sentiment rather than shallow lexical features, like punctuation. Second, we +evaluate the effect of different interventions that are designed to impose an +inductive bias in favor of a particular feature, such as adding a natural +language instruction or using semantically relevant label words. We find that, +while many interventions can influence the learner to prefer a particular +feature, it can be difficult to overcome strong prior biases. Overall, our +results provide a broader picture of the types of features that ICL may be more +likely to exploit and how to impose inductive biases that are better aligned +with the intended task. +" +Exploring Diverse In-Context Configurations for Image Captioning,Xu Yang,http://arxiv.org/pdf/2305.14800v5.pdf,2023-05-24,['cs.cv'],2305.14800v5.pdf," After discovering that Language Models (LMs) can be good in-context few-shot +learners, numerous strategies have been proposed to optimize in-context +sequence configurations. Recently, researchers in Vision-Language (VL) domains +also develop their few-shot learners, while they only use the simplest way, +ie., randomly sampling, to configure in-context image-text pairs. In order to +explore the effects of varying configurations on VL in-context learning, we +devised four strategies for image selection and four for caption assignment to +configure in-context image-text pairs for image captioning. Here Image +Captioning is used as the case study since it can be seen as the +visually-conditioned LM. Our comprehensive experiments yield two +counter-intuitive but valuable insights, highlighting the distinct +characteristics of VL in-context learning due to multi-modal synergy, as +compared to the NLP case. Furthermore, in our exploration of optimal +combination strategies, we observed an average performance enhancement of 20.9 +of CIDEr scores compared to the baseline. The code is given in +https://github.com/yongliang-wu/ExploreCfg. +" +Estimating Large Language Model Capabilities without Labeled Test Data,Harvey Yiyun Fu,http://arxiv.org/pdf/2305.14802v2.pdf,2023-05-24,['cs.cl'],2305.14802v2.pdf," Large Language Models (LLMs) have the impressive ability to perform +in-context learning (ICL) from only a few examples, but the success of ICL +varies widely from task to task. Thus, it is important to quickly determine +whether ICL is applicable to a new task, but directly evaluating ICL accuracy +can be expensive in situations where test data is expensive to annotate -- the +exact situations where ICL is most appealing. In this paper, we propose the +task of ICL accuracy estimation, in which we predict the accuracy of an LLM +when doing in-context learning on a new task given only unlabeled test data for +that task. To perform ICL accuracy estimation, we propose a method that trains +a meta-model using LLM confidence scores as features. We compare our method to +several strong accuracy estimation baselines on a new benchmark that covers 4 +LLMs and 3 task collections. The meta-model improves over all baselines across +8 out of 12 settings and achieves the same estimation performance as directly +evaluating on 40 collected labeled test examples per task. At the same time, no +existing approach provides an accurate and reliable ICL accuracy estimation in +every setting, highlighting the need for better ways to measure the uncertainty +of LLM predictions. +" +BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer,Akari Asai,http://arxiv.org/pdf/2305.14857v1.pdf,2023-05-24,['cs.cl'],2305.14857v1.pdf," Despite remarkable advancements in few-shot generalization in natural +language processing, most models are developed and evaluated primarily in +English. To facilitate research on few-shot cross-lingual transfer, we +introduce a new benchmark, called BUFFET, which unifies 15 diverse tasks across +54 languages in a sequence-to-sequence format and provides a fixed set of +few-shot examples and instructions. BUFFET is designed to establish a rigorous +and equitable evaluation framework for few-shot cross-lingual transfer across a +broad range of tasks and languages. Using BUFFET, we perform thorough +evaluations of state-of-the-art multilingual large language models with +different transfer methods, namely in-context learning and fine-tuning. Our +findings reveal significant room for improvement in few-shot in-context +cross-lingual transfer. In particular, ChatGPT with in-context learning often +performs worse than much smaller mT5-base models fine-tuned on English task +data and few-shot in-language examples. Our analysis suggests various avenues +for future research in few-shot cross-lingual transfer, such as improved +pretraining, understanding, and future evaluations. +" +Adversarial Demonstration Attacks on Large Language Models,Jiongxiao Wang,http://arxiv.org/pdf/2305.14950v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.cr', 'cs.lg']",2305.14950v2.pdf," With the emergence of more powerful large language models (LLMs), such as +ChatGPT and GPT-4, in-context learning (ICL) has gained significant prominence +in leveraging these models for specific tasks by utilizing data-label pairs as +precondition prompts. While incorporating demonstrations can greatly enhance +the performance of LLMs across various tasks, it may introduce a new security +concern: attackers can manipulate only the demonstrations without changing the +input to perform an attack. In this paper, we investigate the security concern +of ICL from an adversarial perspective, focusing on the impact of +demonstrations. We propose a novel attack method named advICL, which aims to +manipulate only the demonstration without changing the input to mislead the +models. Our results demonstrate that as the number of demonstrations increases, +the robustness of in-context learning would decrease. Additionally, we also +identify the intrinsic property of the demonstrations is that they can be used +(prepended) with different inputs. As a result, it introduces a more practical +threat model in which an attacker can attack the test input example even +without knowing and manipulating it. To achieve it, we propose the transferable +version of advICL, named Transferable-advICL. Our experiment shows that the +adversarial demonstration generated by Transferable-advICL can successfully +attack the unseen test input examples. We hope that our study reveals the +critical security risks associated with ICL and underscores the need for +extensive research on the robustness of ICL, particularly given its increasing +significance in the advancement of LLMs. +" +Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations,Wei-Lin Chen,http://arxiv.org/pdf/2305.15035v2.pdf,2023-05-24,['cs.cl'],2305.15035v2.pdf," Large language models (LLMs) have exhibited striking in-context learning +(ICL) ability to adapt to target tasks with a few input-output demonstrations. +For better ICL, different methods are proposed to select representative +demonstrations from existing training corpora. However, such settings are not +aligned with real-world practices, as end-users usually query LMs without +access to demonstration pools. In this work, we introduce Self-ICL -- a simple +framework which bootstraps LMs' intrinsic capabilities to perform zero-shot +ICL. Given a test input, Self-ICL first prompts the model to generate +pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via +zero-shot prompting. Finally, we perform ICL for the test input with the +pseudo-input-label pairs as demonstrations. Evaluation on 23 BIG-Bench Hard +tasks shows Self-ICL outperforms zero-shot baselines on both average accuracy +and head-to-head comparison. Moreover, with zero-shot chain-of-thought, +Self-ICL achieves results comparable to using real demonstrations. +Additionally, we conduct a range of analyses to validate Self-ICL's +effectiveness and provide insights for its behaviors under different settings. +" +Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing,Shufan Wang,http://arxiv.org/pdf/2305.15338v1.pdf,2023-05-24,"['cs.ai', 'cs.cl']",2305.15338v1.pdf," In executable task-oriented semantic parsing, the system aims to translate +users' utterances in natural language to machine-interpretable programs (API +calls) that can be executed according to pre-defined API specifications. With +the popularity of Large Language Models (LLMs), in-context learning offers a +strong baseline for such scenarios, especially in data-limited regimes. +However, LLMs are known to hallucinate and therefore pose a formidable +challenge in constraining generated content. Thus, it remains uncertain if LLMs +can effectively perform task-oriented utterance-to-API generation where +respecting API's structural and task-specific constraints is crucial. + In this work, we seek to measure, analyze and mitigate such constraints +violations. First, we identify the categories of various constraints in +obtaining API-semantics from task-oriented utterances, and define fine-grained +metrics that complement traditional ones. Second, we leverage these metrics to +conduct a detailed error analysis of constraints violations seen in +state-of-the-art LLMs, which motivates us to investigate two mitigation +strategies: Semantic-Retrieval of Demonstrations (SRD) and API-aware +Constrained Decoding (API-CD). Our experiments show that these strategies are +effective at reducing constraints violations and improving the quality of the +generated API calls, but require careful consideration given their +implementation complexity and latency. +" +What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks,Taicheng Guo,http://arxiv.org/pdf/2305.18365v2.pdf,2023-05-27,"['cs.cl', 'cs.ai']",2305.18365v2.pdf," Large Language Models (LLMs) with strong abilities in natural language +processing tasks have emerged and have been applied in various kinds of areas +such as science, finance and software engineering. However, the capability of +LLMs to advance the field of chemistry remains unclear. In this paper, rather +than pursuing state-of-the-art performance, we aim to evaluate capabilities of +LLMs in a wide range of tasks across the chemistry domain. We identify three +key chemistry-related capabilities including understanding, reasoning and +explaining to explore in LLMs and establish a benchmark containing eight +chemistry tasks. Our analysis draws on widely recognized datasets facilitating +a broad exploration of the capacities of LLMs within the context of practical +chemistry. Five LLMs (GPT-4, GPT-3.5, Davinci-003, Llama and Galactica) are +evaluated for each chemistry task in zero-shot and few-shot in-context learning +settings with carefully selected demonstration examples and specially crafted +prompts. Our investigation found that GPT-4 outperformed other models and LLMs +exhibit different competitive levels in eight chemistry tasks. In addition to +the key findings from the comprehensive benchmark analysis, our work provides +insights into the limitation of current LLMs and the impact of in-context +learning settings on LLMs' performance across various chemistry tasks. The code +and datasets used in this study are available at +https://github.com/ChemFoundationModels/ChemLLMBench. +" +Mitigating Label Biases for In-context Learning,Yu Fei,http://arxiv.org/pdf/2305.19148v3.pdf,2023-05-28,"['cs.cl', 'cs.ai', 'cs.lg']",2305.19148v3.pdf," Various design settings for in-context learning (ICL), such as the choice and +order of the in-context examples, can bias a model toward a particular +prediction without being reflective of an understanding of the task. While many +studies discuss these design choices, there have been few systematic +investigations into categorizing them and mitigating their impact. In this +work, we define a typology for three types of label biases in ICL for text +classification: vanilla-label bias, context-label bias, and domain-label bias +(which we conceptualize and detect for the first time). + Our analysis demonstrates that prior label bias calibration methods fall +short of addressing all three types of biases. Specifically, domain-label bias +restricts LLMs to random-level performance on many tasks regardless of the +choice of in-context examples. To mitigate the effect of these biases, we +propose a simple bias calibration method that estimates a language model's +label bias using random in-domain words from the task corpus. After controlling +for this estimated bias when making predictions, our novel domain-context +calibration significantly improves the ICL performance of GPT-J and GPT-3 on a +wide range of tasks. The gain is substantial on tasks with large domain-label +bias (up to 37% in Macro-F1). Furthermore, our results generalize to models +with different scales, pretraining methods, and manually-designed task +instructions, showing the prevalence of label biases in ICL. +" +"What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization",Yufeng Zhang,http://arxiv.org/pdf/2305.19420v2.pdf,2023-05-30,"['stat.ml', 'cs.lg']",2305.19420v2.pdf," In this paper, we conduct a comprehensive study of In-Context Learning (ICL) +by addressing several open questions: (a) What type of ICL estimator is learned +by large language models? (b) What is a proper performance metric for ICL and +what is the error rate? (c) How does the transformer architecture enable ICL? +To answer these questions, we adopt a Bayesian view and formulate ICL as a +problem of predicting the response corresponding to the current covariate, +given a number of examples drawn from a latent variable model. To answer (a), +we show that, without updating the neural network parameters, ICL implicitly +implements the Bayesian model averaging algorithm, which is proven to be +approximately parameterized by the attention mechanism. For (b), we analyze the +ICL performance from an online learning perspective and establish a +$\mathcal{O}(1/T)$ regret bound for perfectly pretrained ICL, where $T$ is the +number of examples in the prompt. To answer (c), we show that, in addition to +encoding Bayesian model averaging via attention, the transformer architecture +also enables a fine-grained statistical analysis of pretraining under realistic +assumptions. In particular, we prove that the error of pretrained model is +bounded by a sum of an approximation error and a generalization error, where +the former decays to zero exponentially as the depth grows, and the latter +decays to zero sublinearly with the number of tokens in the pretraining +dataset. Our results provide a unified understanding of the transformer and its +ICL ability with bounds on ICL regret, approximation, and generalization, which +deepens our knowledge of these essential aspects of modern language models. +" +Augmenting Language Models with Long-Term Memory,Weizhi Wang,http://arxiv.org/pdf/2306.07174v1.pdf,2023-06-12,['cs.cl'],2306.07174v1.pdf," Existing large language models (LLMs) can only afford fix-sized inputs due to +the input length limit, preventing them from utilizing rich long-context +information from past inputs. To address this, we propose a framework, Language +Models Augmented with Long-Term Memory (LongMem), which enables LLMs to +memorize long history. We design a novel decoupled network architecture with +the original backbone LLM frozen as a memory encoder and an adaptive residual +side-network as a memory retriever and reader. Such a decoupled memory design +can easily cache and update long-term past contexts for memory retrieval +without suffering from memory staleness. Enhanced with memory-augmented +adaptation training, LongMem can thus memorize long past context and use +long-term memory for language modeling. The proposed memory retrieval module +can handle unlimited-length context in its memory bank to benefit various +downstream tasks. Typically, LongMem can enlarge the long-form memory to 65k +tokens and thus cache many-shot extra demonstration examples as long-form +memory for in-context learning. Experiments show that our method outperforms +strong long-context models on ChapterBreak, a challenging long-context modeling +benchmark, and achieves remarkable improvements on memory-augmented in-context +learning over LLMs. The results demonstrate that the proposed method is +effective in helping language models to memorize and utilize long-form +contents. Our code is open-sourced at https://aka.ms/LongMem. +" +Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression,Allan Raventós,http://arxiv.org/pdf/2306.15063v2.pdf,2023-06-26,"['cs.lg', 'cs.ai', 'cs.cl']",2306.15063v2.pdf," Pretrained transformers exhibit the remarkable ability of in-context learning +(ICL): they can learn tasks from just a few examples provided in the prompt +without updating any weights. This raises a foundational question: can ICL +solve fundamentally $\textit{new}$ tasks that are very different from those +seen during pretraining? To probe this question, we examine ICL's performance +on linear regression while varying the diversity of tasks in the pretraining +dataset. We empirically demonstrate a $\textit{task diversity threshold}$ for +the emergence of ICL. Below this threshold, the pretrained transformer cannot +solve unseen regression tasks, instead behaving like a Bayesian estimator with +the $\textit{non-diverse pretraining task distribution}$ as the prior. Beyond +this threshold, the transformer significantly outperforms this estimator; its +behavior aligns with that of ridge regression, corresponding to a Gaussian +prior over $\textit{all tasks}$, including those not seen during pretraining. +Thus, when pretrained on data with task diversity greater than the threshold, +transformers $\textit{can}$ optimally solve fundamentally new tasks in-context. +Importantly, this capability hinges on it deviating from the Bayes optimal +estimator with the pretraining distribution as the prior. This study also +explores the effect of regularization, model capacity and task structure and +underscores, in a concrete example, the critical role of task diversity, +alongside data and model scale, in the emergence of ICL. Code is available at +https://github.com/mansheej/icl-task-diversity. +" +Understanding In-Context Learning via Supportive Pretraining Data,Xiaochuang Han,http://arxiv.org/pdf/2306.15091v1.pdf,2023-06-26,['cs.cl'],2306.15091v1.pdf," In-context learning (ICL) improves language models' performance on a variety +of NLP tasks by simply demonstrating a handful of examples at inference time. +It is not well understood why ICL ability emerges, as the model has never been +specifically trained on such demonstrations. Unlike prior work that explores +implicit mechanisms behind ICL, we study ICL via investigating the pretraining +data. Specifically, we first adapt an iterative, gradient-based approach to +find a small subset of pretraining data that supports ICL. We observe that a +continued pretraining on this small subset significantly improves the model's +ICL ability, by up to 18%. We then compare the supportive subset constrastively +with random subsets of pretraining data and discover: (1) The supportive +pretraining data to ICL do not have a higher domain relevance to downstream +tasks. (2) The supportive pretraining data have a higher mass of rarely +occurring, long-tail tokens. (3) The supportive pretraining data are +challenging examples where the information gain from long-range context is +below average, indicating learning to incorporate difficult long-range context +encourages ICL. Our work takes a first step towards understanding ICL via +analyzing instance-level pretraining data. Our insights have a potential to +enhance the ICL ability of language models by actively guiding the construction +of pretraining data in the future. +" +Schema-learning and rebinding as mechanisms of in-context learning and emergence,Sivaramakrishnan Swaminathan,http://arxiv.org/pdf/2307.01201v1.pdf,2023-06-16,"['cs.cl', 'cs.ai']",2307.01201v1.pdf," In-context learning (ICL) is one of the most powerful and most unexpected +capabilities to emerge in recent transformer-based large language models +(LLMs). Yet the mechanisms that underlie it are poorly understood. In this +paper, we demonstrate that comparable ICL capabilities can be acquired by an +alternative sequence prediction learning method using clone-structured causal +graphs (CSCGs). Moreover, a key property of CSCGs is that, unlike +transformer-based LLMs, they are {\em interpretable}, which considerably +simplifies the task of explaining how ICL works. Specifically, we show that it +uses a combination of (a) learning template (schema) circuits for pattern +completion, (b) retrieving relevant templates in a context-sensitive manner, +and (c) rebinding of novel tokens to appropriate slots in the templates. We go +on to marshall evidence for the hypothesis that similar mechanisms underlie ICL +in LLMs. For example, we find that, with CSCGs as with LLMs, different +capabilities emerge at different levels of overparameterization, suggesting +that overparameterization helps in learning more complex template (schema) +circuits. By showing how ICL can be achieved with small models and datasets, we +open up a path to novel architectures, and take a vital step towards a more +general understanding of the mechanics behind this important capability. +" +Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps,Zongxia Li,http://arxiv.org/pdf/2307.05052v1.pdf,2023-07-11,"['cs.cl', 'cs.ai']",2307.05052v1.pdf," We investigate the role of various demonstration components in the in-context +learning (ICL) performance of large language models (LLMs). Specifically, we +explore the impacts of ground-truth labels, input distribution, and +complementary explanations, particularly when these are altered or perturbed. +We build on previous work, which offers mixed findings on how these elements +influence ICL. To probe these questions, we employ explainable NLP (XNLP) +methods and utilize saliency maps of contrastive demonstrations for both +qualitative and quantitative analysis. Our findings reveal that flipping +ground-truth labels significantly affects the saliency, though it's more +noticeable in larger LLMs. Our analysis of the input distribution at a granular +level reveals that changing sentiment-indicative terms in a sentiment analysis +task to neutral ones does not have as substantial an impact as altering +ground-truth labels. Finally, we find that the effectiveness of complementary +explanations in boosting ICL performance is task-dependent, with limited +benefits seen in sentiment analysis tasks compared to symbolic reasoning tasks. +These insights are critical for understanding the functionality of LLMs and +guiding the development of effective demonstrations, which is increasingly +relevant in light of the growing use of LLMs in applications such as ChatGPT. +Our research code is publicly available at https://github.com/paihengxu/XICL. +" +In-context learning for model-free system identification,Marco Forgione,http://arxiv.org/pdf/2308.13380v1.pdf,2023-08-25,"['eess.sy', 'cs.lg', 'cs.sy']",2308.13380v1.pdf," In traditional system identification, we estimate a model of an unknown +dynamical system based on given input/output sequences and available physical +knowledge. Yet, is it also possible to understand the intricacies of dynamical +systems not solely from their input/output patterns, but by observing the +behavior of other systems within the same class? This central question drives +the study presented in this paper. + In response to this query, we introduce a novel paradigm for system +identification, addressing two primary tasks: one-step-ahead prediction and +multi-step simulation. Unlike conventional methods, we do not directly estimate +a model for the specific system. Instead, we pretrain a meta model that +represents a class of dynamical systems. This meta model is trained from a +potentially infinite stream of synthetic data, generated by systems randomly +extracted from a certain distribution. At its core, the meta model serves as an +implicit representation of the main characteristics of a class of dynamical +systems. When provided with a brief context from a new system - specifically, a +short input/output sequence - the meta model implicitly discerns its dynamics, +enabling predictions of its behavior. + The proposed approach harnesses the power of Transformer architectures, +renowned for their in-context learning capabilities in Natural Language +Processing tasks. For one-step prediction, a GPT-like decoder-only architecture +is utilized, whereas the simulation problem employs an encoder-decoder +structure. + Initial experimental results affirmatively answer our foundational question, +opening doors to fresh research avenues in system identification. +" +Ambiguity-Aware In-Context Learning with Large Language Models,Lingyu Gao,http://arxiv.org/pdf/2309.07900v1.pdf,2023-09-14,"['cs.cl', 'cs.ir']",2309.07900v1.pdf," In-context learning (ICL) i.e. showing LLMs only a few task-specific +demonstrations has led to downstream gains with no task-specific fine-tuning +required. However, LLMs are sensitive to the choice of prompts, and therefore a +crucial research question is how to select good demonstrations for ICL. One +effective strategy is leveraging semantic similarity between the ICL +demonstrations and test inputs by using a text retriever, which however is +sub-optimal as that does not consider the LLM's existing knowledge about that +task. From prior work (Min et al., 2022), we already know that labels paired +with the demonstrations bias the model predictions. This leads us to our +hypothesis whether considering LLM's existing knowledge about the task, +especially with respect to the output label space can help in a better +demonstration selection strategy. Through extensive experimentation on three +text classification tasks, we find that it is beneficial to not only choose +semantically similar ICL demonstrations but also to choose those demonstrations +that help resolve the inherent label ambiguity surrounding the test example. +Interestingly, we find that including demonstrations that the LLM previously +mis-classified and also fall on the test example's decision boundary, brings +the most performance gain. +" +Are Human-generated Demonstrations Necessary for In-context Learning?,Rui Li,http://arxiv.org/pdf/2309.14681v2.pdf,2023-09-26,"['cs.lg', 'cs.ai']",2309.14681v2.pdf," Despite the promising few-shot ability of large language models (LLMs), the +standard paradigm of In-context Learning (ICL) suffers the disadvantages of +susceptibility to selected demonstrations and the intricacy to generate these +demonstrations. In this paper, we raise the fundamental question that whether +human-generated demonstrations are necessary for ICL. To answer this question, +we propose self-contemplation prompting strategy (SEC), a paradigm free from +human-crafted demonstrations. The key point of SEC is that, instead of using +hand-crafted examples as demonstrations in ICL, SEC asks LLMs to first create +demonstrations on their own, based on which the final output is generated. SEC +is a flexible framework and can be adapted to both the vanilla ICL and the +chain-of-thought (CoT), but with greater ease: as the manual-generation process +of both examples and rationale can be saved. Extensive experiments in +arithmetic reasoning, commonsense reasoning, multi-task language understanding, +and code generation benchmarks, show that SEC, which does not require +hand-crafted demonstrations, significantly outperforms the zero-shot learning +strategy, and achieves comparable results to ICL with hand-crafted +demonstrations. This demonstrates that, for many tasks, contemporary LLMs +possess a sufficient level of competence to exclusively depend on their own +capacity for decision making, removing the need for external training data. +Code is available at https://github.com/ruili33/SEC. +" +Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning,Mustafa Shukor,http://arxiv.org/pdf/2310.00647v1.pdf,2023-10-01,"['cs.cv', 'cs.mm']",2310.00647v1.pdf," Following the success of Large Language Models (LLMs), Large Multimodal +Models (LMMs), such as the Flamingo model and its subsequent competitors, have +started to emerge as natural steps towards generalist agents. However, +interacting with recent LMMs reveals major limitations that are hardly captured +by the current evaluation benchmarks. Indeed, task performances (e.g., VQA +accuracy) alone do not provide enough clues to understand their real +capabilities, limitations, and to which extent such models are aligned to human +expectations. To refine our understanding of those flaws, we deviate from the +current evaluation paradigm and propose the EvALign-ICL framework, in which we +(1) evaluate 8 recent open-source LMMs (based on the Flamingo architecture such +as OpenFlamingo and IDEFICS) on 5 different axes; hallucinations, abstention, +compositionality, explainability and instruction following. Our evaluation on +these axes reveals major flaws in LMMs. To efficiently address these problems, +and inspired by the success of in-context learning (ICL) in LLMs, (2) we +explore ICL as a solution and study how it affects these limitations. Based on +our ICL study, (3) we push ICL further and propose new multimodal ICL +approaches such as; Multitask-ICL, Chain-of-Hindsight-ICL, and +Self-Correcting-ICL. Our findings are as follows; (1) Despite their success, +LMMs have flaws that remain unsolved with scaling alone. (2) The effect of ICL +on LMMs flaws is nuanced; despite its effectiveness for improved +explainability, abstention, and instruction following, ICL does not improve +compositional abilities, and actually even amplifies hallucinations. (3) The +proposed ICL variants are promising as post-hoc approaches to efficiently +tackle some of those flaws. The code is available here: +https://evalign-icl.github.io/ +" +Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions,Satwik Bhattamishra,http://arxiv.org/pdf/2310.03016v1.pdf,2023-10-04,"['cs.lg', 'cs.cl']",2310.03016v1.pdf," In order to understand the in-context learning phenomenon, recent works have +adopted a stylized experimental framework and demonstrated that Transformers +can learn gradient-based learning algorithms for various classes of real-valued +functions. However, the limitations of Transformers in implementing learning +algorithms, and their ability to learn other forms of algorithms are not well +understood. Additionally, the degree to which these capabilities are confined +to attention-based models is unclear. Furthermore, it remains to be seen +whether the insights derived from these stylized settings can be extrapolated +to pretrained Large Language Models (LLMs). In this work, we take a step +towards answering these questions by demonstrating the following: (a) On a +test-bed with a variety of Boolean function classes, we find that Transformers +can nearly match the optimal learning algorithm for 'simpler' tasks, while +their performance deteriorates on more 'complex' tasks. Additionally, we find +that certain attention-free models perform (almost) identically to Transformers +on a range of tasks. (b) When provided a teaching sequence, i.e. a set of +examples that uniquely identifies a function in a class, we show that +Transformers learn more sample-efficiently. Interestingly, our results show +that Transformers can learn to implement two distinct algorithms to solve a +single task, and can adaptively select the more sample-efficient algorithm +depending on the sequence of in-context examples. (c) Lastly, we show that +extant LLMs, e.g. LLaMA-2, GPT-4, can compete with nearest-neighbor baselines +on prediction tasks that are guaranteed to not be in their training set. +" +SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA,Jonathan Tonglet,http://arxiv.org/pdf/2310.06675v2.pdf,2023-10-10,['cs.cl'],2310.06675v2.pdf," Question answering over hybrid contexts is a complex task, which requires the +combination of information extracted from unstructured texts and structured +tables in various ways. Recently, In-Context Learning demonstrated significant +performance advances for reasoning tasks. In this paradigm, a large language +model performs predictions based on a small set of supporting exemplars. The +performance of In-Context Learning depends heavily on the selection procedure +of the supporting exemplars, particularly in the case of HybridQA, where +considering the diversity of reasoning chains and the large size of the hybrid +contexts becomes crucial. In this work, we present Selection of ExEmplars for +hybrid Reasoning (SEER), a novel method for selecting a set of exemplars that +is both representative and diverse. The key novelty of SEER is that it +formulates exemplar selection as a Knapsack Integer Linear Program. The +Knapsack framework provides the flexibility to incorporate diversity +constraints that prioritize exemplars with desirable attributes, and capacity +constraints that ensure that the prompt size respects the provided capacity +budgets. The effectiveness of SEER is demonstrated on FinQA and TAT-QA, two +real-world benchmarks for HybridQA, where it outperforms previous exemplar +selection methods. +" +How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations,Tianyu Guo,http://arxiv.org/pdf/2310.10616v1.pdf,2023-10-16,['cs.lg'],2310.10616v1.pdf," While large language models based on the transformer architecture have +demonstrated remarkable in-context learning (ICL) capabilities, understandings +of such capabilities are still in an early stage, where existing theory and +mechanistic understanding focus mostly on simple scenarios such as learning +simple function classes. This paper takes initial steps on understanding ICL in +more complex scenarios, by studying learning with representations. Concretely, +we construct synthetic in-context learning problems with a compositional +structure, where the label depends on the input through a possibly complex but +fixed representation function, composed with a linear function that differs in +each instance. By construction, the optimal ICL algorithm first transforms the +inputs by the representation function, and then performs linear ICL on top of +the transformed dataset. We show theoretically the existence of transformers +that approximately implement such algorithms with mild depth and size. +Empirically, we find trained transformers consistently achieve near-optimal ICL +performance in this setting, and exhibit the desired dissection where lower +layers transforms the dataset and upper layers perform linear ICL. Through +extensive probing and a new pasting experiment, we further reveal several +mechanisms within the trained transformers, such as concrete copying behaviors +on both the inputs and the representations, linear ICL capability of the upper +layers alone, and a post-ICL representation selection mechanism in a harder +mixture setting. These observed mechanisms align well with our theory and may +shed light on how transformers perform ICL in more realistic scenarios. +" +Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning,Anirudh Som,http://arxiv.org/pdf/2310.10707v1.pdf,2023-10-16,"['cs.cl', 'cs.ai']",2310.10707v1.pdf," Paraphrasing of offensive content is a better alternative to content removal +and helps improve civility in a communication environment. Supervised +paraphrasers; however, rely heavily on large quantities of labelled data to +help preserve meaning and intent. They also retain a large portion of the +offensiveness of the original content, which raises questions on their overall +usability. In this paper we aim to assist practitioners in developing usable +paraphrasers by exploring In-Context Learning (ICL) with large language models +(LLMs), i.e., using a limited number of input-label demonstration pairs to +guide the model in generating desired outputs for specific queries. Our study +focuses on key factors such as -- number and order of demonstrations, exclusion +of prompt instruction, and reduction in measured toxicity. We perform +principled evaluation on three datasets, including our proposed Context-Aware +Polite Paraphrase dataset, comprising of dialogue-style rude utterances, polite +paraphrases, and additional dialogue context. We evaluate our approach using +two closed source and one open source LLM. Our results reveal that ICL is +comparable to supervised methods in generation quality, while being +qualitatively better by 25% on human evaluation and attaining lower toxicity by +76%. Also, ICL-based paraphrasers only show a slight reduction in performance +even with just 10% training data. +" +O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models,Yuchen Xiao,http://arxiv.org/pdf/2310.14403v1.pdf,2023-10-22,"['cs.ai', 'cs.cl']",2310.14403v1.pdf," Recent advancements in large language models (LLMs) have exhibited promising +performance in solving sequential decision-making problems. By imitating +few-shot examples provided in the prompts (i.e., in-context learning), an LLM +agent can interact with an external environment and complete given tasks +without additional training. However, such few-shot examples are often +insufficient to generate high-quality solutions for complex and long-horizon +tasks, while the limited context length cannot consume larger-scale +demonstrations. To this end, we propose an offline learning framework that +utilizes offline data at scale (e.g, logs of human interactions) to facilitate +the in-context learning performance of LLM agents. We formally define +LLM-powered policies with both text-based approaches and code-based approaches. +We then introduce an Offline Data-driven Discovery and Distillation (O3D) +framework to improve LLM-powered policies without finetuning. O3D automatically +discovers reusable skills and distills generalizable knowledge across multiple +tasks based on offline interaction data, advancing the capability of solving +downstream tasks. Empirical results under two interactive decision-making +benchmarks (ALFWorld and WebShop) demonstrate that O3D can notably enhance the +decision-making capabilities of LLMs through the offline discovery and +distillation process, and consistently outperform baselines across various LLMs +with both text-based-policy and code-based-policy. +" +Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models,Deqing Fu,http://arxiv.org/pdf/2310.17086v1.pdf,2023-10-26,"['cs.lg', 'cs.ai', 'cs.cl']",2310.17086v1.pdf," Transformers are remarkably good at in-context learning (ICL) -- learning +from demonstrations without parameter updates -- but how they perform ICL +remains a mystery. Recent work suggests that Transformers may learn in-context +by internally running Gradient Descent, a first-order optimization method. In +this paper, we instead demonstrate that Transformers learn to implement +higher-order optimization methods to perform ICL. Focusing on in-context linear +regression, we show that Transformers learn to implement an algorithm very +similar to Iterative Newton's Method, a higher-order optimization method, +rather than Gradient Descent. Empirically, we show that predictions from +successive Transformer layers closely match different iterations of Newton's +Method linearly, with each middle layer roughly computing 3 iterations. In +contrast, exponentially more Gradient Descent steps are needed to match an +additional Transformers layer; this suggests that Transformers have an +comparable rate of convergence with high-order methods such as Iterative +Newton, which are exponentially faster than Gradient Descent. We also show that +Transformers can learn in-context on ill-conditioned data, a setting where +Gradient Descent struggles but Iterative Newton succeeds. Finally, we show +theoretical results which support our empirical findings and have a close +correspondence with them: we prove that Transformers can implement $k$ +iterations of Newton's method with $\mathcal{O}(k)$ layers. +" +Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time,Zichang Liu,http://arxiv.org/pdf/2310.17157v1.pdf,2023-10-26,['cs.lg'],2310.17157v1.pdf," Large language models (LLMs) with hundreds of billions of parameters have +sparked a new wave of exciting AI applications. However, they are +computationally expensive at inference time. Sparsity is a natural approach to +reduce this cost, but existing methods either require costly retraining, have +to forgo LLM's in-context learning ability, or do not yield wall-clock time +speedup on modern hardware. We hypothesize that contextual sparsity, which are +small, input-dependent sets of attention heads and MLP parameters that yield +approximately the same output as the dense model for a given input, can address +these issues. We show that contextual sparsity exists, that it can be +accurately predicted, and that we can exploit it to speed up LLM inference in +wall-clock time without compromising LLM's quality or in-context learning +ability. Based on these insights, we propose DejaVu, a system that uses a +low-cost algorithm to predict contextual sparsity on the fly given inputs to +each layer, along with an asynchronous and hardware-aware implementation that +speeds up LLM inference. We validate that DejaVu can reduce the inference +latency of OPT-175B by over 2X compared to the state-of-the-art +FasterTransformer, and over 6X compared to the widely used Hugging Face +implementation, without compromising model quality. The code is available at +https://github.com/FMInference/DejaVu. +" +Improving Input-label Mapping with Demonstration Replay for In-context Learning,Zhuocheng Gong,http://arxiv.org/pdf/2310.19572v1.pdf,2023-10-30,['cs.cl'],2310.19572v1.pdf," In-context learning (ICL) is an emerging capability of large autoregressive +language models where a few input-label demonstrations are appended to the +input to enhance the model's understanding of downstream NLP tasks, without +directly adjusting the model parameters. The effectiveness of ICL can be +attributed to the strong language modeling capabilities of large language +models (LLMs), which enable them to learn the mapping between input and labels +based on in-context demonstrations. Despite achieving promising results, the +causal nature of language modeling in ICL restricts the attention to be +backward only, i.e., a token only attends to its previous tokens, failing to +capture the full input-label information and limiting the model's performance. +In this paper, we propose a novel ICL method called Repeated Demonstration with +Sliding Causal Attention, (RdSca). Specifically, we duplicate later +demonstrations and concatenate them to the front, allowing the model to +`observe' the later information even under the causal restriction. Besides, we +introduce sliding causal attention, which customizes causal attention to avoid +information leakage. Experimental results show that our method significantly +improves the input-label mapping in ICL demonstrations. We also conduct an +in-depth analysis of how to customize the causal attention without training, +which has been an unexplored area in previous research. +" +Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models,Steve Yadlowsky,http://arxiv.org/pdf/2311.00871v1.pdf,2023-11-01,"['cs.lg', 'cs.cl', 'stat.ml']",2311.00871v1.pdf," Transformer models, notably large language models (LLMs), have the remarkable +ability to perform in-context learning (ICL) -- to perform new tasks when +prompted with unseen input-output examples without any explicit model training. +In this work, we study how effectively transformers can bridge between their +pretraining data mixture, comprised of multiple distinct task families, to +identify and learn new tasks in-context which are both inside and outside the +pretraining distribution. Building on previous work, we investigate this +question in a controlled setting, where we study transformer models trained on +sequences of $(x, f(x))$ pairs rather than natural language. Our empirical +results show transformers demonstrate near-optimal unsupervised model selection +capabilities, in their ability to first in-context identify different task +families and in-context learn within them when the task families are +well-represented in their pretraining data. However when presented with tasks +or functions which are out-of-domain of their pretraining data, we demonstrate +various failure modes of transformers and degradation of their generalization +for even simple extrapolation tasks. Together our results highlight that the +impressive ICL abilities of high-capacity sequence models may be more closely +tied to the coverage of their pretraining data mixtures than inductive biases +that create fundamental generalization capabilities. +" +Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning,Mingyang Geng,http://arxiv.org/pdf/2304.11384v3.pdf,2023-04-22,['cs.se'],2304.11384v3.pdf," Code comment generation aims at generating natural language descriptions for +a code snippet to facilitate developers' program comprehension activities. +Despite being studied for a long time, a bottleneck for existing approaches is +that given a code snippet, they can only generate one comment while developers +usually need to know information from diverse perspectives such as what is the +functionality of this code snippet and how to use it. To tackle this +limitation, this study empirically investigates the feasibility of utilizing +large language models (LLMs) to generate comments that can fulfill developers' +diverse intents. Our intuition is based on the facts that (1) the code and its +pairwise comment are used during the pre-training process of LLMs to build the +semantic connection between the natural language and programming language, and +(2) comments in the real-world projects, which are collected for the +pre-training, usually contain different developers' intents. We thus postulate +that the LLMs can already understand the code from different perspectives after +the pre-training. Indeed, experiments on two large-scale datasets demonstrate +the rationale of our insights: by adopting the in-context learning paradigm and +giving adequate prompts to the LLM (e.g., providing it with ten or more +examples), the LLM can significantly outperform a state-of-the-art supervised +learning approach on generating comments with multiple intents. Results also +show that customized strategies for constructing the prompts and +post-processing strategies for reranking the results can both boost the LLM's +performances, which shed light on future research directions for using LLMs to +achieve comment generation. +" +Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision,Zhiqing Sun,http://arxiv.org/pdf/2305.03047v1.pdf,2023-05-04,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cy']",2305.03047v1.pdf," Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised +fine-tuning (SFT) with human annotations and reinforcement learning from human +feedback (RLHF) to align the output of large language models (LLMs) with human +intentions, ensuring they are helpful, ethical, and reliable. However, this +dependence can significantly constrain the true potential of AI-assistant +agents due to the high cost of obtaining human supervision and the related +issues on quality, reliability, diversity, self-consistency, and undesirable +biases. To address these challenges, we propose a novel approach called +SELF-ALIGN, which combines principle-driven reasoning and the generative power +of LLMs for the self-alignment of AI agents with minimal human supervision. Our +approach encompasses four stages: first, we use an LLM to generate synthetic +prompts, and a topic-guided method to augment the prompt diversity; second, we +use a small set of human-written principles for AI models to follow, and guide +the LLM through in-context learning from demonstrations (of principles +application) to produce helpful, ethical, and reliable responses to user's +queries; third, we fine-tune the original LLM with the high-quality +self-aligned responses so that the resulting model can generate desirable +responses for each query directly without the principle set and the +demonstrations anymore; and finally, we offer a refinement step to address the +issues of overly-brief or indirect responses. Applying SELF-ALIGN to the +LLaMA-65b base language model, we develop an AI assistant named Dromedary. With +fewer than 300 lines of human annotations (including < 200 seed prompts, 16 +generic principles, and 5 exemplars for in-context learning). Dromedary +significantly surpasses the performance of several state-of-the-art AI systems, +including Text-Davinci-003 and Alpaca, on benchmark datasets with various +settings. +" +One for All: Towards Training One Graph Model for All Classification Tasks,Hao Liu,http://arxiv.org/pdf/2310.00149v1.pdf,2023-09-29,['cs.lg'],2310.00149v1.pdf," Designing a single model that addresses multiple tasks has been a +long-standing objective in artificial intelligence. Recently, large language +models have demonstrated exceptional capability in integrating and solving +different tasks within the language domain. However, a unified model for +various tasks on graphs remains underexplored, primarily due to the challenges +unique to the graph learning domain. First, graph data from different areas +carry distinct attributes and follow different distributions. Such discrepancy +makes it hard to represent graphs in a single representation space. Second, +tasks on graphs diversify into node, link, and graph tasks, requiring distinct +embedding strategies. Finally, an appropriate graph prompting paradigm for +in-context learning is unclear. Striving to handle all the aforementioned +challenges, we propose One for All (OFA), the first general framework that can +use a single graph model to address the above challenges. Specifically, OFA +proposes text-attributed graphs to unify different graph data by describing +nodes and edges with natural language and uses language models to encode the +diverse and possibly cross-domain text attributes to feature vectors in the +same embedding space. Furthermore, OFA introduces the concept of +nodes-of-interest to standardize different tasks with a single task +representation. For in-context learning on graphs, OFA introduces a novel graph +prompting paradigm that appends prompting substructures to the input graph, +which enables it to address varied tasks without fine-tuning. We train the OFA +model using graph data from multiple domains (including citation networks, +molecular graphs, knowledge graphs, etc.) simultaneously and evaluate its +ability in supervised, few-shot, and zero-shot learning scenarios. OFA performs +well across different tasks, making it the first general-purpose graph +classification model across domains. +" +The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design,Yoav Levine,http://arxiv.org/pdf/2110.04541v3.pdf,2021-10-09,"['cs.cl', 'cs.lg']",2110.04541v3.pdf," Pretraining Neural Language Models (NLMs) over a large corpus involves +chunking the text into training examples, which are contiguous text segments of +sizes processable by the neural architecture. We highlight a bias introduced by +this common practice: we prove that the pretrained NLM can model much stronger +dependencies between text segments that appeared in the same training example, +than it can between text segments that appeared in different training examples. +This intuitive result has a twofold role. First, it formalizes the motivation +behind a broad line of recent successful NLM training heuristics, proposed for +the pretraining and fine-tuning stages, which do not necessarily appear related +at first glance. Second, our result clearly indicates further improvements to +be made in NLM pretraining for the benefit of Natural Language Understanding +tasks. As an example, we propose ""kNN-Pretraining"": we show that including +semantically related non-neighboring sentences in the same pretraining example +yields improved sentence representations and open domain question answering +abilities. This theoretically motivated degree of freedom for pretraining +example design indicates new training schemes for self-improving +representations. +" +MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning,Constantin Eichenberg,http://arxiv.org/pdf/2112.05253v2.pdf,2021-12-09,"['cs.cv', 'cs.cl', 'i.2.7; i.4.8; i.5.1']",2112.05253v2.pdf," Large-scale pretraining is fast becoming the norm in Vision-Language (VL) +modeling. However, prevailing VL approaches are limited by the requirement for +labeled data and the use of complex multi-step pretraining objectives. We +present MAGMA - a simple method for augmenting generative language models with +additional modalities using adapter-based finetuning. Building on Frozen, we +train a series of VL models that autoregressively generate text from arbitrary +combinations of visual and textual input. The pretraining is entirely +end-to-end using a single language modeling objective, simplifying optimization +compared to previous approaches. Importantly, the language model weights remain +unchanged during training, allowing for transfer of encyclopedic knowledge and +in-context learning abilities from language pretraining. MAGMA outperforms +Frozen on open-ended generative tasks, achieving state of the art results on +the OKVQA benchmark and competitive results on a range of other popular VL +benchmarks, while pretraining on 0.2% of the number of samples used to train +SimVLM. +" +Black-Box Tuning for Language-Model-as-a-Service,Tianxiang Sun,http://arxiv.org/pdf/2201.03514v4.pdf,2022-01-10,"['cs.cl', 'cs.ai']",2201.03514v4.pdf," Extremely large pre-trained language models (PTMs) such as GPT-3 are usually +released as a service. It allows users to design task-specific prompts to query +the PTMs through some black-box APIs. In such a scenario, which we call +Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually +unavailable. Can we optimize the task prompts by only accessing the model +inference APIs? This paper proposes the black-box tuning framework to optimize +the continuous prompt prepended to the input text via derivative-free +optimization. Instead of optimizing in the original high-dimensional prompt +space, which is intractable for traditional derivative-free optimization, we +perform optimization in a randomly generated subspace due to the low intrinsic +dimensionality of large PTMs. The experimental results show that the black-box +tuning with RoBERTa on a few labeled samples not only significantly outperforms +manual prompt and GPT-3's in-context learning, but also surpasses the +gradient-based counterparts, i.e., prompt tuning and full model tuning. +" +Contrastive Learning for Prompt-Based Few-Shot Language Learners,Yiren Jian,http://arxiv.org/pdf/2205.01308v1.pdf,2022-05-03,"['cs.cl', 'cs.ai']",2205.01308v1.pdf," The impressive performance of GPT-3 using natural language prompts and +in-context learning has inspired work on better fine-tuning of moderately-sized +models under this paradigm. Following this line of work, we present a +contrastive learning framework that clusters inputs from the same class for +better generality of models trained with only limited examples. Specifically, +we propose a supervised contrastive framework that clusters inputs from the +same class under different augmented ""views"" and repel the ones from different +classes. We create different ""views"" of an example by appending it with +different language prompts and contextual demonstrations. Combining a +contrastive loss with the standard masked language modeling (MLM) loss in +prompt-based few-shot learners, the experimental results show that our method +can improve over the state-of-the-art methods in a diverse set of 15 language +tasks. Our framework makes minimal assumptions on the task or the base model, +and can be applied to many recent methods with little modification. The code +will be made available at: https://github.com/yiren-jian/LM-SupCon. +" +Instruction Induction: From Few Examples to Natural Language Task Descriptions,Or Honovich,http://arxiv.org/pdf/2205.10782v1.pdf,2022-05-22,['cs.cl'],2205.10782v1.pdf," Large language models are able to perform a task by conditioning on a few +input-output demonstrations - a paradigm known as in-context learning. We show +that language models can explicitly infer an underlying task from a few +demonstrations by prompting them to generate a natural language instruction +that fits the examples. To explore this ability, we introduce the instruction +induction challenge, compile a dataset consisting of 24 tasks, and define a +novel evaluation metric based on executing the generated instruction. We +discover that, to a large extent, the ability to generate instructions does +indeed emerge when using a model that is both large enough and aligned to +follow instructions; InstructGPT achieves 65.7% of human performance in our +execution-based metric, while the original GPT-3 model reaches only 9.8% of +human performance. This surprising result suggests that instruction induction +might be a viable learning paradigm in and of itself, where instead of fitting +a set of latent continuous parameters to the data, one searches for the best +description in the natural language hypothesis space. +" +Exploring Length Generalization in Large Language Models,Cem Anil,http://arxiv.org/pdf/2207.04901v2.pdf,2022-07-11,"['cs.cl', 'cs.lg']",2207.04901v2.pdf," The ability to extrapolate from short problem instances to longer ones is an +important form of out-of-distribution generalization in reasoning tasks, and is +crucial when learning from datasets where longer problem instances are rare. +These include theorem proving, solving quantitative mathematics problems, and +reading/summarizing novels. In this paper, we run careful empirical studies +exploring the length generalization capabilities of transformer-based language +models. We first establish that naively finetuning transformers on length +generalization tasks shows significant generalization deficiencies independent +of model scale. We then show that combining pretrained large language models' +in-context learning abilities with scratchpad prompting (asking the model to +output solution steps before producing an answer) results in a dramatic +improvement in length generalization. We run careful failure analyses on each +of the learning modalities and identify common sources of mistakes that +highlight opportunities in equipping language models with the ability to +generalize to longer problems. +" +Large Language Models are few(1)-shot Table Reasoners,Wenhu Chen,http://arxiv.org/pdf/2210.06710v2.pdf,2022-10-13,['cs.cl'],2210.06710v2.pdf," Recent literature has shown that large language models (LLMs) are generally +excellent few-shot reasoners to solve text reasoning tasks. However, the +capability of LLMs on table reasoning tasks is yet to be explored. In this +paper, we aim at understanding how well LLMs can perform table-related tasks +with few-shot in-context learning. Specifically, we evaluated LLMs on popular +table QA and fact verification datasets like WikiTableQuestion, FetaQA, +TabFact, and FEVEROUS and found that LLMs are competent at complex reasoning +over table structures, though these models are not pre-trained on any table +corpus. When combined with `chain of thoughts' prompting, LLMs can achieve very +strong performance with only a 1-shot demonstration, even on par with some SoTA +models. We show that LLMs are even more competent at generating comprehensive +long-form answers on FetaQA than tuned T5-large. We further manually studied +the reasoning chains elicited from LLMs and found that these reasoning chains +are highly consistent with the underlying semantic form. We believe that LLMs +can serve as a simple yet generic baseline for future research. The code and +data are released in https://github.com/wenhuchen/TableCoT. +" +Explanations from Large Language Models Make Small Reasoners Better,Shiyang Li,http://arxiv.org/pdf/2210.06726v1.pdf,2022-10-13,['cs.cl'],2210.06726v1.pdf," Integrating free-text explanations to in-context learning of large language +models (LLM) is shown to elicit strong reasoning capabilities along with +reasonable explanations. In this paper, we consider the problem of leveraging +the explanations generated by LLM to improve the training of small reasoners, +which are more favorable in real-production deployment due to their low cost. +We systematically explore three explanation generation approaches from LLM and +utilize a multi-task learning framework to facilitate small models to acquire +strong reasoning power together with explanation generation capabilities. +Experiments on multiple reasoning tasks show that our method can consistently +and significantly outperform finetuning baselines across different settings, +and even perform better than finetuning/prompting a 60x larger GPT-3 (175B) +model by up to 9.5% in accuracy. As a side benefit, human evaluation further +shows that our method can generate high-quality explanations to justify its +predictions, moving towards the goal of explainable AI. +" +Prompting Language Models for Linguistic Structure,Terra Blevins,http://arxiv.org/pdf/2211.07830v2.pdf,2022-11-15,['cs.cl'],2211.07830v2.pdf," Although pretrained language models (PLMs) can be prompted to perform a wide +range of language tasks, it remains an open question how much this ability +comes from generalizable linguistic understanding versus surface-level lexical +patterns. To test this, we present a structured prompting approach for +linguistic structured prediction tasks, allowing us to perform zero- and +few-shot sequence tagging with autoregressive PLMs. We evaluate this approach +on part-of-speech tagging, named entity recognition, and sentence chunking, +demonstrating strong few-shot performance in all cases. We also find that while +PLMs contain significant prior knowledge of task labels due to task leakage +into the pretraining corpus, structured prompting can also retrieve linguistic +structure with arbitrary labels. These findings indicate that the in-context +learning ability and linguistic knowledge of PLMs generalizes beyond +memorization of their training data. +" +Visual Programming: Compositional visual reasoning without training,Tanmay Gupta,http://arxiv.org/pdf/2211.11559v1.pdf,2022-11-18,"['cs.cv', 'cs.ai', 'cs.cl']",2211.11559v1.pdf," We present VISPROG, a neuro-symbolic approach to solving complex and +compositional visual tasks given natural language instructions. VISPROG avoids +the need for any task-specific training. Instead, it uses the in-context +learning ability of large language models to generate python-like modular +programs, which are then executed to get both the solution and a comprehensive +and interpretable rationale. Each line of the generated program may invoke one +of several off-the-shelf computer vision models, image processing routines, or +python functions to produce intermediate outputs that may be consumed by +subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 +diverse tasks - compositional visual question answering, zero-shot reasoning on +image pairs, factual knowledge object tagging, and language-guided image +editing. We believe neuro-symbolic approaches like VISPROG are an exciting +avenue to easily and effectively expand the scope of AI systems to serve the +long tail of complex tasks that people may wish to perform. +" +Self-Prompting Large Language Models for Zero-Shot Open-Domain QA,Junlong Li,http://arxiv.org/pdf/2212.08635v2.pdf,2022-12-16,"['cs.cl', 'cs.ai']",2212.08635v2.pdf," Open-Domain Question Answering (ODQA) aims at answering factoid questions +without explicitly providing specific background documents. In a zero-shot +setting, this task is more challenging since no data is available to train +customized models like Retriever-Readers. Recently, Large Language Models +(LLMs) like GPT-3 have shown their power in zero-shot ODQA with direct +prompting methods, but these methods are still far from releasing the full +powerfulness of LLMs only in an implicitly invoking way. In this paper, we +propose a Self-Prompting framework to explicitly utilize the massive knowledge +stored in the parameters of LLMs and their strong instruction understanding +abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo +QA pairs with background passages and explanations from scratch and then use +those generated elements for in-context learning. Experimental results show our +method surpasses previous SOTA methods significantly on three widely-used ODQA +datasets, and even achieves comparable performance with some Retriever-Reader +models fine-tuned on full training data. +" +"Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments",Yu Gu,http://arxiv.org/pdf/2212.09736v2.pdf,2022-12-19,"['cs.cl', 'cs.ai', 'i.2.7']",2212.09736v2.pdf," A key missing capacity of current language models (LMs) is grounding to +real-world environments. Most existing work for grounded language understanding +uses LMs to directly generate plans that can be executed in the environment to +achieve the desired effects. It thereby casts the burden of ensuring +grammaticality, faithfulness, and controllability all on the LMs. We propose +Pangu, a generic framework for grounded language understanding that capitalizes +on the discriminative ability of LMs instead of their generative ability. Pangu +consists of a symbolic agent and a neural LM working in a concerted fashion: +The agent explores the environment to incrementally construct valid plans, and +the LM evaluates the plausibility of the candidate plans to guide the search +process. A case study on the challenging problem of knowledge base question +answering (KBQA), which features a massive environment, demonstrates the +remarkable effectiveness and flexibility of Pangu: A BERT-base LM is sufficient +for setting a new record on standard KBQA datasets, and larger LMs further +bring substantial gains. Pangu also enables, for the first time, effective +few-shot in-context learning for KBQA with large LMs such as Codex. +" +Ontologically Faithful Generation of Non-Player Character Dialogues,Nathaniel Weir,http://arxiv.org/pdf/2212.10618v2.pdf,2022-12-20,['cs.cl'],2212.10618v2.pdf," We introduce a language generation task grounded in a popular video game +environment. KNUDGE (KNowledge Constrained User-NPC Dialogue GEneration) +requires models to produce trees of dialogue between video game characters that +accurately reflect quest and entity specifications stated in natural language. +KNUDGE is constructed from side quest dialogues drawn directly from game data +of Obsidian Entertainment's The Outer Worlds, leading to real-world +complexities in generation: (1) dialogues are branching trees as opposed to +linear chains of utterances; (2) utterances must remain faithful to the game +lore -- character personas, backstories, and entity relationships; and (3) a +dialogue must accurately reveal new quest details to the human player. We +report results for a set of neural generation models using supervised and +in-context learning techniques; we find competent performance but room for +future work addressing the challenges of creating realistic, game-quality +dialogues. +" +Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers,Chengyi Wang,http://arxiv.org/pdf/2301.02111v1.pdf,2023-01-05,"['cs.cl', 'cs.sd', 'eess.as']",2301.02111v1.pdf," We introduce a language modeling approach for text to speech synthesis (TTS). +Specifically, we train a neural codec language model (called Vall-E) using +discrete codes derived from an off-the-shelf neural audio codec model, and +regard TTS as a conditional language modeling task rather than continuous +signal regression as in previous work. During the pre-training stage, we scale +up the TTS training data to 60K hours of English speech which is hundreds of +times larger than existing systems. Vall-E emerges in-context learning +capabilities and can be used to synthesize high-quality personalized speech +with only a 3-second enrolled recording of an unseen speaker as an acoustic +prompt. Experiment results show that Vall-E significantly outperforms the +state-of-the-art zero-shot TTS system in terms of speech naturalness and +speaker similarity. In addition, we find Vall-E could preserve the speaker's +emotion and acoustic environment of the acoustic prompt in synthesis. See +https://aka.ms/valle for demos of our work. +" +Batch Prompting: Efficient Inference with Large Language Model APIs,Zhoujun Cheng,http://arxiv.org/pdf/2301.08721v2.pdf,2023-01-19,"['cs.cl', 'cs.ai']",2301.08721v2.pdf," Performing inference on large volumes of samples with large language models +(LLMs) can be computationally and financially costly in industry and real-world +use. We propose batch prompting, a simple yet effective prompting approach that +enables the LLM to run inference in batches, instead of one sample at a time. +Our method reduces both token and time costs while retaining downstream +performance. We theoretically demonstrate that under a few-shot in-context +learning setting, the inference costs decrease almost inverse linearly with the +number of samples in each batch. We extensively validate the effectiveness of +batch prompting on ten datasets across commonsense QA, arithmetic reasoning, +and NLI/NLU: batch prompting significantly~(up to 5x with six samples in batch) +reduces the LLM (Codex) inference token and time costs while achieving better +or comparable performance. For state-of-the-art Chat-based LLMs, e.g., GPT-3.5 +and GPT-4, we show the benefits of batch prompting also hold. Further analysis +shows that the number of samples in each batch and the complexity of tasks +affect its performance. Moreover, batch prompting can be applied across +different reasoning methods using LLMs. Our code can be found at the site +https://github.com/xlang-ai/batch-prompting. +" +Looped Transformers as Programmable Computers,Angeliki Giannou,http://arxiv.org/pdf/2301.13196v1.pdf,2023-01-30,"['cs.lg', 'cs.ai']",2301.13196v1.pdf," We present a framework for using transformer networks as universal computers +by programming them with specific weights and placing them in a loop. Our input +sequence acts as a punchcard, consisting of instructions and memory for data +read/writes. We demonstrate that a constant number of encoder layers can +emulate basic computing blocks, including embedding edit operations, non-linear +functions, function calls, program counters, and conditional branches. Using +these building blocks, we emulate a small instruction-set computer. This allows +us to map iterative algorithms to programs that can be executed by a looped, +13-layer transformer. We show how this transformer, instructed by its input, +can emulate a basic calculator, a basic linear algebra library, and in-context +learning algorithms that employ backpropagation. Our work highlights the +versatility of the attention mechanism, and demonstrates that even shallow +transformers can execute full-fledged, general-purpose programs. +" +Grounding Language Models to Images for Multimodal Inputs and Outputs,Jing Yu Koh,http://arxiv.org/pdf/2301.13823v4.pdf,2023-01-31,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2301.13823v4.pdf," We propose an efficient method to ground pretrained text-only language models +to the visual domain, enabling them to process arbitrarily interleaved +image-and-text data, and generate text interleaved with retrieved images. Our +method leverages the abilities of language models learnt from large scale +text-only pretraining, such as in-context learning and free-form text +generation. We keep the language model frozen, and finetune input and output +linear layers to enable cross-modality interactions. This allows our model to +process arbitrarily interleaved image-and-text inputs, and generate free-form +text interleaved with retrieved images. We achieve strong zero-shot performance +on grounded tasks such as contextual image retrieval and multimodal dialogue, +and showcase compelling interactive abilities. Our approach works with any +off-the-shelf language model and paves the way towards an effective, general +solution for leveraging pretrained language models in visually grounded +settings. +" +ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics,Zhangir Azerbayev,http://arxiv.org/pdf/2302.12433v1.pdf,2023-02-24,"['cs.cl', 'cs.ai', 'cs.lo']",2302.12433v1.pdf," We introduce ProofNet, a benchmark for autoformalization and formal proving +of undergraduate-level mathematics. The ProofNet benchmarks consists of 371 +examples, each consisting of a formal theorem statement in Lean 3, a natural +language theorem statement, and a natural language proof. The problems are +primarily drawn from popular undergraduate pure mathematics textbooks and cover +topics such as real and complex analysis, linear algebra, abstract algebra, and +topology. We intend for ProofNet to be a challenging benchmark that will drive +progress in autoformalization and automatic theorem proving. We report baseline +results on statement autoformalization via in-context learning. Moreover, we +introduce two novel statement autoformalization methods: prompt retrieval and +distilled backtranslation. +" +Finding Support Examples for In-Context Learning,Xiaonan Li,http://arxiv.org/pdf/2302.13539v3.pdf,2023-02-27,['cs.cl'],2302.13539v3.pdf," Additionally, the strong dependency among in-context examples makes it an +NP-hard combinatorial optimization problem and enumerating all permutations is +infeasible. Hence we propose LENS, a fiLter-thEN-Search method to tackle this +challenge in two stages: First we filter the dataset to obtain informative +in-context examples individually. Specifically, we propose a novel metric, +InfoScore, to evaluate the example's in-context informativeness based on the +language model's feedback, and further propose a progressive filtering process +to filter out uninformative examples. Then we propose diversity-guided example +search which iteratively refines and evaluates the selected example +permutations, to find examples that fully depict the task. The experimental +results show that LENS significantly outperforms a wide range of baselines. +" +In-Context Instruction Learning,Seonghyeon Ye,http://arxiv.org/pdf/2302.14691v1.pdf,2023-02-28,"['cs.cl', 'cs.ai']",2302.14691v1.pdf," Instruction learning of Large Language Models (LLMs) has enabled zero-shot +task generalization. However, instruction learning has been predominantly +approached as a fine-tuning problem, including instruction tuning and +reinforcement learning from human feedback, where LLMs are multi-task +fine-tuned on various tasks with instructions. In this paper, we present a +surprising finding that applying in-context learning to instruction learning, +referred to as In-Context Instruction Learning (ICIL), significantly improves +the zero-shot task generalization performance for both pretrained and +instruction-fine-tuned models. One of the core advantages of ICIL is that it +uses a single fixed prompt to evaluate all tasks, which is a concatenation of +cross-task demonstrations. In particular, we demonstrate that the most powerful +instruction-fine-tuned baseline (text-davinci-003) also benefits from ICIL by +9.3%, indicating that the effect of ICIL is complementary to instruction-based +fine-tuning. +" +Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling,Ziqiang Zhang,http://arxiv.org/pdf/2303.03926v1.pdf,2023-03-07,"['cs.cl', 'cs.ai', 'cs.sd', 'eess.as']",2303.03926v1.pdf," We propose a cross-lingual neural codec language model, VALL-E X, for +cross-lingual speech synthesis. Specifically, we extend VALL-E and train a +multi-lingual conditional codec language model to predict the acoustic token +sequences of the target language speech by using both the source language +speech and the target language text as prompts. VALL-E X inherits strong +in-context learning capabilities and can be applied for zero-shot cross-lingual +text-to-speech synthesis and zero-shot speech-to-speech translation tasks. +Experimental results show that it can generate high-quality speech in the +target language via just one speech utterance in the source language as a +prompt while preserving the unseen speaker's voice, emotion, and acoustic +environment. Moreover, VALL-E X effectively alleviates the foreign accent +problems, which can be controlled by a language ID. Audio samples are available +at \url{https://aka.ms/vallex}. +" +Self-planning Code Generation with Large Language Models,Xue Jiang,http://arxiv.org/pdf/2303.06689v2.pdf,2023-03-12,['cs.se'],2303.06689v2.pdf," Although large language models have demonstrated impressive ability in code +generation, they are still struggling to address the complicated intent +provided by humans. It is widely acknowledged that humans typically employ +planning to decompose complex problems and schedule the solution steps prior to +implementation. Thus we introduce planning into code generation to help the +model understand complex intent and reduce the difficulty of problem solving. +This paper proposes a self-planning code generation method with large language +model, which consists of two phases, namely planning phase and implementation +phase. Specifically, in the planning phase, the language model plans out the +solution steps from the intent combined with in-context learning. Then it +enters the implementation phase, where the model generates code step by step, +guided by the solution steps. The effectiveness of self-planning code +generation has been rigorously evaluated on multiple code generation datasets +and the results have demonstrated a marked superiority over naive direct +generation approaches with language model. The improvement in performance is +substantial, highlighting the significance of self-planning in code generation +tasks. +" +GPT is becoming a Turing machine: Here are some ways to program it,Ana Jojic,http://arxiv.org/pdf/2303.14310v1.pdf,2023-03-25,['cs.cl'],2303.14310v1.pdf," We demonstrate that, through appropriate prompting, GPT-3 family of models +can be triggered to perform iterative behaviours necessary to execute (rather +than just write or recall) programs that involve loops, including several +popular algorithms found in computer science curricula or software developer +interviews. We trigger execution and description of Iterations by Regimenting +Self-Attention (IRSA) in one (or a combination) of three ways: 1) Using strong +repetitive structure in an example of an execution path of a target program for +one particular input, 2) Prompting with fragments of execution paths, and 3) +Explicitly forbidding (skipping) self-attention to parts of the generated text. +On a dynamic program execution, IRSA leads to larger accuracy gains than +replacing the model with the much more powerful GPT-4. IRSA has promising +applications in education, as the prompts and responses resemble student +assignments in data structures and algorithms classes. Our findings hold +implications for evaluating LLMs, which typically target the in-context +learning: We show that prompts that may not even cover one full task example +can trigger algorithmic behaviour, allowing solving problems previously thought +of as hard for LLMs, such as logical puzzles. Consequently, prompt design plays +an even more critical role in LLM performance than previously recognized. +" +When Brain-inspired AI Meets AGI,Lin Zhao,http://arxiv.org/pdf/2303.15935v1.pdf,2023-03-28,['cs.ai'],2303.15935v1.pdf," Artificial General Intelligence (AGI) has been a long-standing goal of +humanity, with the aim of creating machines capable of performing any +intellectual task that humans can do. To achieve this, AGI researchers draw +inspiration from the human brain and seek to replicate its principles in +intelligent machines. Brain-inspired artificial intelligence is a field that +has emerged from this endeavor, combining insights from neuroscience, +psychology, and computer science to develop more efficient and powerful AI +systems. In this article, we provide a comprehensive overview of brain-inspired +AI from the perspective of AGI. We begin with the current progress in +brain-inspired AI and its extensive connection with AGI. We then cover the +important characteristics for both human intelligence and AGI (e.g., scaling, +multimodality, and reasoning). We discuss important technologies toward +achieving AGI in current AI systems, such as in-context learning and prompt +tuning. We also investigate the evolution of AGI systems from both algorithmic +and infrastructural perspectives. Finally, we explore the limitations and +future of AGI. +" +Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning,Namrata Shivagunde,http://arxiv.org/pdf/2303.16445v1.pdf,2023-03-29,['cs.cl'],2303.16445v1.pdf," Language model probing is often used to test specific capabilities of these +models. However, conclusions from such studies may be limited when the probing +benchmarks are small and lack statistical power. In this work, we introduce +new, larger datasets for negation (NEG-1500-SIMP) and role reversal (ROLE-1500) +inspired by psycholinguistic studies. We dramatically extend existing NEG-136 +and ROLE-88 benchmarks using GPT3, increasing their size from 18 and 44 +sentence pairs to 750 each. We also create another version of extended negation +dataset (NEG-1500-SIMP-TEMP), created using template-based generation. It +consists of 770 sentence pairs. We evaluate 22 models on the extended datasets, +seeing model performance dip 20-57% compared to the original smaller +benchmarks. We observe high levels of negation sensitivity in models like BERT +and ALBERT demonstrating that previous findings might have been skewed due to +smaller test sets. Finally, we observe that while GPT3 has generated all the +examples in ROLE-1500 is only able to solve 24.6% of them during probing. +" +Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation,Tao Fang,http://arxiv.org/pdf/2304.01746v1.pdf,2023-04-04,['cs.cl'],2304.01746v1.pdf," ChatGPT, a large-scale language model based on the advanced GPT-3.5 +architecture, has shown remarkable potential in various Natural Language +Processing (NLP) tasks. However, there is currently a dearth of comprehensive +study exploring its potential in the area of Grammatical Error Correction +(GEC). To showcase its capabilities in GEC, we design zero-shot +chain-of-thought (CoT) and few-shot CoT settings using in-context learning for +ChatGPT. Our evaluation involves assessing ChatGPT's performance on five +official test sets in three different languages, along with three +document-level GEC test sets in English. Our experimental results and human +evaluations demonstrate that ChatGPT has excellent error detection capabilities +and can freely correct errors to make the corrected sentences very fluent, +possibly due to its over-correction tendencies and not adhering to the +principle of minimal edits. Additionally, its performance in non-English and +low-resource settings highlights its potential in multilingual GEC tasks. +However, further analysis of various types of errors at the document-level has +shown that ChatGPT cannot effectively correct agreement, coreference, tense +errors across sentences, and cross-sentence boundary errors. +" +SegGPT: Segmenting Everything In Context,Xinlong Wang,http://arxiv.org/pdf/2304.03284v1.pdf,2023-04-06,['cs.cv'],2304.03284v1.pdf," We present SegGPT, a generalist model for segmenting everything in context. +We unify various segmentation tasks into a generalist in-context learning +framework that accommodates different kinds of segmentation data by +transforming them into the same format of images. The training of SegGPT is +formulated as an in-context coloring problem with random color mapping for each +data sample. The objective is to accomplish diverse tasks according to the +context, rather than relying on specific colors. After training, SegGPT can +perform arbitrary segmentation tasks in images or videos via in-context +inference, such as object instance, stuff, part, contour, and text. SegGPT is +evaluated on a broad range of tasks, including few-shot semantic segmentation, +video object segmentation, semantic segmentation, and panoptic segmentation. +Our results show strong capabilities in segmenting in-domain and out-of-domain +targets, either qualitatively or quantitatively. +" +Extractive Summarization via ChatGPT for Faithful Summary Generation,Haopeng Zhang,http://arxiv.org/pdf/2304.04193v2.pdf,2023-04-09,['cs.cl'],2304.04193v2.pdf," Extractive summarization is a crucial task in natural language processing +that aims to condense long documents into shorter versions by directly +extracting sentences. The recent introduction of large language models has +attracted significant interest in the NLP community due to its remarkable +performance on a wide range of downstream tasks. This paper first presents a +thorough evaluation of ChatGPT's performance on extractive summarization and +compares it with traditional fine-tuning methods on various benchmark datasets. +Our experimental analysis reveals that ChatGPT exhibits inferior extractive +summarization performance in terms of ROUGE scores compared to existing +supervised systems, while achieving higher performance based on LLM-based +evaluation metrics. In addition, we explore the effectiveness of in-context +learning and chain-of-thought reasoning for enhancing its performance. +Furthermore, we find that applying an extract-then-generate pipeline with +ChatGPT yields significant performance improvements over abstractive baselines +in terms of summary faithfulness. These observations highlight potential +directions for enhancing ChatGPT's capabilities in faithful summarization using +two-stage approaches. +" +Towards Robust Prompts on Vision-Language Models,Jindong Gu,http://arxiv.org/pdf/2304.08479v1.pdf,2023-04-17,['cs.cv'],2304.08479v1.pdf," With the advent of vision-language models (VLMs) that can perform in-context +and prompt-based learning, how can we design prompting approaches that robustly +generalize to distribution shift and can be used on novel classes outside the +support set of the prompts? In this work, we first define two types of +robustness to distribution shift on VLMs, namely, robustness on base classes +(the classes included in the support set of prompts) and robustness on novel +classes. Then, we study the robustness of existing in-context learning and +prompt learning approaches, where we find that prompt learning performs +robustly on test images from base classes, while it does not generalize well on +images from novel classes. We propose robust prompt learning by integrating +multiple-scale image features into the prompt, which improves both types of +robustness. Comprehensive experiments are conducted to study the defined +robustness on six benchmarks and show the effectiveness of our proposal. +" +A Latent Space Theory for Emergent Abilities in Large Language Models,Hui Jiang,http://arxiv.org/pdf/2304.09960v3.pdf,2023-04-19,"['cs.cl', 'cs.ai', 'cs.lg']",2304.09960v3.pdf," Languages are not created randomly but rather to communicate information. +There is a strong association between languages and their underlying meanings, +resulting in a sparse joint distribution that is heavily peaked according to +their correlations. Moreover, these peak values happen to match with the +marginal distribution of languages due to the sparsity. With the advent of LLMs +trained on big data and large models, we can now precisely assess the marginal +distribution of languages, providing a convenient means of exploring the sparse +structures in the joint distribution for effective inferences. In this paper, +we categorize languages as either unambiguous or {\epsilon}-ambiguous and +present quantitative results to demonstrate that the emergent abilities of +LLMs, such as language understanding, in-context learning, chain-of-thought +prompting, and effective instruction fine-tuning, can all be attributed to +Bayesian inference on the sparse joint distribution of languages. +" +Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation,Nan-Jiang Jiang,http://arxiv.org/pdf/2304.12443v1.pdf,2023-04-24,['cs.cl'],2304.12443v1.pdf," Human label variation (Plank 2022), or annotation disagreement, exists in +many natural language processing (NLP) tasks. To be robust and trusted, NLP +models need to identify such variation and be able to explain it. To this end, +we created the first ecologically valid explanation dataset with diverse +reasoning, LiveNLI. LiveNLI contains annotators' highlights and free-text +explanations for the label(s) of their choice for 122 English Natural Language +Inference items, each with at least 10 annotations. We used its explanations +for chain-of-thought prompting, and found there is still room for improvement +in GPT-3's ability to predict label distribution with in-context learning. +" +"Stance Detection With Supervised, Zero-Shot, and Few-Shot Applications",Michael Burnham,http://arxiv.org/pdf/2305.01723v1.pdf,2023-05-02,['cs.cl'],2305.01723v1.pdf," Stance detection is the identification of an author's beliefs about a subject +from a document. Researchers widely rely on sentiment analysis to accomplish +this. However, recent research has show that sentiment analysis is only loosely +correlated with stance, if at all. This paper advances methods in text analysis +by precisely defining the task of stance detection, providing a generalized +framework for the task, and then presenting three distinct approaches for +performing stance detection: supervised classification, zero-shot +classification with NLI classifiers, and in-context learning. In doing so, I +demonstrate how zero-shot and few-shot language classifiers can replace human +labelers for a variety of tasks and discuss how their application and +limitations differ from supervised classifiers. Finally, I demonstrate an +application of zero-shot stance detection by replicating Block Jr et al. +(2022). +" +WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models,John Giorgi,http://arxiv.org/pdf/2305.02220v2.pdf,2023-05-03,"['cs.cl', 'cs.ai', 'cs.lg']",2305.02220v2.pdf," This paper describes our submission to the MEDIQA-Chat 2023 shared task for +automatic clinical note generation from doctor-patient conversations. We report +results for two approaches: the first fine-tunes a pre-trained language model +(PLM) on the shared task data, and the second uses few-shot in-context learning +(ICL) with a large language model (LLM). Both achieve high performance as +measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and +first, respectively, of all submissions to the shared task. Expert human +scrutiny indicates that notes generated via the ICL-based approach with GPT-4 +are preferred about as often as human-written notes, making it a promising path +toward automated note generation from doctor-patient conversations. +" +Otter: A Multi-Modal Model with In-Context Instruction Tuning,Bo Li,http://arxiv.org/pdf/2305.03726v1.pdf,2023-05-05,"['cs.cv', 'cs.cl']",2305.03726v1.pdf," Large language models (LLMs) have demonstrated significant universal +capabilities as few/zero-shot learners in various tasks due to their +pre-training on vast amounts of text data, as exemplified by GPT-3, which +boosted to InstrctGPT and ChatGPT, effectively following natural language +instructions to accomplish real-world tasks. In this paper, we propose to +introduce instruction tuning into multi-modal models, motivated by the Flamingo +model's upstream interleaved format pretraining dataset. We adopt a similar +approach to construct our MultI-Modal In-Context Instruction Tuning (MIMIC-IT) +dataset. We then introduce Otter, a multi-modal model based on OpenFlamingo +(open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and +showcasing improved instruction-following ability and in-context learning. We +also optimize OpenFlamingo's implementation for researchers, democratizing the +required training resources from 1$\times$ A100 GPU to 4$\times$ RTX-3090 GPUs, +and integrate both OpenFlamingo and Otter into Huggingface Transformers for +more researchers to incorporate the models into their customized training and +inference pipelines. +" +How Good are Commercial Large Language Models on African Languages?,Jessica Ojo,http://arxiv.org/pdf/2305.06530v1.pdf,2023-05-11,"['cs.cl', 'cs.ai', 'cs.lg']",2305.06530v1.pdf," Recent advancements in Natural Language Processing (NLP) has led to the +proliferation of large pretrained language models. These models have been shown +to yield good performance, using in-context learning, even on unseen tasks and +languages. They have also been exposed as commercial APIs as a form of +language-model-as-a-service, with great adoption. However, their performance on +African languages is largely unknown. We present a preliminary analysis of +commercial large language models on two tasks (machine translation and text +classification) across eight African languages, spanning different language +families and geographical areas. Our results suggest that commercial language +models produce below-par performance on African languages. We also find that +they perform better on text classification than machine translation. In +general, our findings present a call-to-action to ensure African languages are +well represented in commercial large language models, given their growing +popularity. +" +Chain-of-Dictionary Prompting Elicits Translation in Large Language Models,Hongyuan Lu,http://arxiv.org/pdf/2305.06575v3.pdf,2023-05-11,['cs.cl'],2305.06575v3.pdf," Large language models (LLMs) have shown surprisingly good performance in +multilingual neural machine translation (MNMT) even when trained without +parallel data. Yet, despite the fact that the amount of training data is +gigantic, they still struggle with translating rare words, particularly for +low-resource languages. Even worse, it is usually unrealistic to retrieve +relevant demonstrations for in-context learning with low-resource languages on +LLMs, which restricts the practical use of LLMs for translation -- how should +we mitigate this problem? To this end, we present a novel method, CoD, which +augments LLMs with prior knowledge with the chains of multilingual dictionaries +for a subset of input words to elicit translation abilities for LLMs. Extensive +experiments indicate that augmenting ChatGPT with CoD elicits large gains by up +to 13x chrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in +Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the +importance of chaining the multilingual dictionaries, as well as the +superiority of CoD to few-shot demonstration for low-resource languages. +" +Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation,Jinglong Gao,http://arxiv.org/pdf/2305.07375v4.pdf,2023-05-12,"['cs.cl', 'cs.ai']",2305.07375v4.pdf," Causal reasoning ability is crucial for numerous NLP applications. Despite +the impressive emerging ability of ChatGPT in various NLP tasks, it is unclear +how well ChatGPT performs in causal reasoning. In this paper, we conduct the +first comprehensive evaluation of the ChatGPT's causal reasoning capabilities. +Experiments show that ChatGPT is not a good causal reasoner, but a good causal +explainer. Besides, ChatGPT has a serious hallucination on causal reasoning, +possibly due to the reporting biases between causal and non-causal +relationships in natural language, as well as ChatGPT's upgrading processes, +such as RLHF. The In-Context Learning (ICL) and Chain-of-Thought (CoT) +techniques can further exacerbate such causal hallucination. Additionally, the +causal reasoning ability of ChatGPT is sensitive to the words used to express +the causal concept in prompts, and close-ended prompts perform better than +open-ended prompts. For events in sentences, ChatGPT excels at capturing +explicit causality rather than implicit causality, and performs better in +sentences with lower event density and smaller lexical distance between events. +The code is available on https://github.com/ArrogantL/ChatGPT4CausalReasoning . +" +AutoTrial: Prompting Language Models for Clinical Trial Design,Zifeng Wang,http://arxiv.org/pdf/2305.11366v2.pdf,2023-05-19,['cs.cl'],2305.11366v2.pdf," Clinical trials are critical for drug development. Constructing the +appropriate eligibility criteria (i.e., the inclusion/exclusion criteria for +patient recruitment) is essential for the trial's success. Proper design of +clinical trial protocols should consider similar precedent trials and their +eligibility criteria to ensure sufficient patient coverage. In this paper, we +present a method named AutoTrial to aid the design of clinical eligibility +criteria using language models. It allows (1) controllable generation under +instructions via a hybrid of discrete and neural prompting, (2) scalable +knowledge incorporation via in-context learning, and (3) explicit reasoning +chains to provide rationales for understanding the outputs. Experiments on over +70K clinical trials verify that AutoTrial generates high-quality criteria texts +that are fluent and coherent and with high accuracy in capturing the relevant +clinical concepts to the target trial. It is noteworthy that our method, with a +much smaller parameter size, gains around 60% winning rate against the GPT-3.5 +baselines via human evaluations. +" +Cross-Lingual Supervision improves Large Language Models Pre-training,Andrea Schioppa,http://arxiv.org/pdf/2305.11778v1.pdf,2023-05-19,"['cs.cl', 'cs.lg']",2305.11778v1.pdf," The recent rapid progress in pre-training Large Language Models has relied on +using self-supervised language modeling objectives like next token prediction +or span corruption. On the other hand, Machine Translation Systems are mostly +trained using cross-lingual supervision that requires aligned data between +source and target languages. We demonstrate that pre-training Large Language +Models on a mixture of a self-supervised Language Modeling objective and the +supervised Machine Translation objective, therefore including cross-lingual +parallel data during pre-training, yields models with better in-context +learning abilities. As pre-training is a very resource-intensive process and a +grid search on the best mixing ratio between the two objectives is +prohibitively expensive, we propose a simple yet effective strategy to learn it +during pre-training. +" +"How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings",Shuaichen Chang,http://arxiv.org/pdf/2305.11853v2.pdf,2023-05-19,['cs.cl'],2305.11853v2.pdf," Large language models (LLMs) with in-context learning have demonstrated +remarkable capability in the text-to-SQL task. Previous research has prompted +LLMs with various demonstration-retrieval strategies and intermediate reasoning +steps to enhance the performance of LLMs. However, those works often employ +varied strategies when constructing the prompt text for text-to-SQL inputs, +such as databases and demonstration examples. This leads to a lack of +comparability in both the prompt constructions and their primary contributions. +Furthermore, selecting an effective prompt construction has emerged as a +persistent problem for future research. To address this limitation, we +comprehensively investigate the impact of prompt constructions across various +settings and provide insights for future work. +" +Fact-Checking Complex Claims with Program-Guided Reasoning,Liangming Pan,http://arxiv.org/pdf/2305.12744v1.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.12744v1.pdf," Fact-checking real-world claims often requires collecting multiple pieces of +evidence and applying complex multi-step reasoning. In this paper, we present +Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that +decomposes complex claims into simpler sub-tasks that can be solved using a +shared library of specialized functions. We first leverage the in-context +learning ability of large language models to generate reasoning programs to +guide the verification process. Afterward, we execute the program by delegating +each sub-task to the corresponding sub-task handler. This process makes our +model both explanatory and data-efficient, providing clear explanations of its +reasoning process and requiring minimal training data. We evaluate ProgramFC on +two challenging fact-checking datasets and show that it outperforms seven +fact-checking baselines across different settings of evidence availability, +with explicit output programs that benefit human debugging. Our codes and data +are publicly available at https://github.com/mbzuai-nlp/ProgramFC. +" +ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination,Dongfang Li,http://arxiv.org/pdf/2305.12945v2.pdf,2023-05-22,['cs.cl'],2305.12945v2.pdf," As ChatGPT and GPT-4 spearhead the development of Large Language Models +(LLMs), more researchers are investigating their performance across various +tasks. But more research needs to be done on the interpretability capabilities +of LLMs, that is, the ability to generate reasons after an answer has been +given. Existing explanation datasets are mostly English-language general +knowledge questions, which leads to insufficient thematic and linguistic +diversity. To address the language bias and lack of medical resources in +generating rationales QA datasets, we present ExplainCPE (over 7k instances), a +challenging medical benchmark in Simplified Chinese. We analyzed the errors of +ChatGPT and GPT-4, pointing out the limitations of current LLMs in +understanding text and computational reasoning. During the experiment, we also +found that different LLMs have different preferences for in-context learning. +ExplainCPE presents a significant challenge, but its potential for further +investigation is promising, and it can be used to evaluate the ability of a +model to generate explanations. AI safety and trustworthiness need more +attention, and this work makes the first step to explore the medical +interpretability of LLMs.The dataset is available at +https://github.com/HITsz-TMG/ExplainCPE. +" +MAILEX: Email Event and Argument Extraction,Saurabh Srivastava,http://arxiv.org/pdf/2305.13469v2.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.13469v2.pdf," In this work, we present the first dataset, MailEx, for performing event +extraction from conversational email threads. To this end, we first proposed a +new taxonomy covering 10 event types and 76 arguments in the email domain. Our +final dataset includes 1.5K email threads and ~4K emails, which are annotated +with totally ~8K event instances. To understand the task challenges, we +conducted a series of experiments comparing three types of approaches, i.e., +fine-tuned sequence labeling, fine-tuned generative extraction, and few-shot +in-context learning. Our results showed that the task of email event extraction +is far from being addressed, due to challenges lying in, e.g., extracting +non-continuous, shared trigger spans, extracting non-named entity arguments, +and modeling the email conversational history. Our work thus suggests more +future investigations in this domain-specific event extraction task. +" +Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding,Mutian He,http://arxiv.org/pdf/2305.13512v2.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.sd', 'eess.as']",2305.13512v2.pdf," Recently, large pretrained language models have demonstrated strong language +understanding capabilities. This is particularly reflected in their zero-shot +and in-context learning abilities on downstream tasks through prompting. To +assess their impact on spoken language understanding (SLU), we evaluate several +such models like ChatGPT and OPT of different sizes on multiple benchmarks. We +verify the emergent ability unique to the largest models as they can reach +intent classification accuracy close to that of supervised models with zero or +few shots on various languages given oracle transcripts. By contrast, the +results for smaller models fitting a single GPU fall far behind. We note that +the error cases often arise from the annotation scheme of the dataset; +responses from ChatGPT are still reasonable. We show, however, that the model +is worse at slot filling, and its performance is sensitive to ASR errors, +suggesting serious challenges for the application of those textual models on +SLU. +" +LogicLLM: Exploring Self-supervised Logic-enhanced Training for Large Language Models,Fangkai Jiao,http://arxiv.org/pdf/2305.13718v2.pdf,2023-05-23,['cs.cl'],2305.13718v2.pdf," Existing efforts to improve logical reasoning ability of language models have +predominantly relied on supervised fine-tuning, hindering generalization to new +domains and/or tasks. The development of Large Langauge Models (LLMs) has +demonstrated the capacity of compressing abundant knowledge into a single +proxy, enabling them to tackle multiple tasks effectively. Our preliminary +experiments, nevertheless, show that LLMs do not show capability on logical +reasoning. The performance of LLMs on logical reasoning benchmarks is far +behind the existing state-of-the-art baselines. In this paper, we make the +first attempt to investigate the feasibility of incorporating logical knowledge +through self-supervised post-training, and activating it via in-context +learning, which we termed as LogicLLM. Specifically, we devise an +auto-regressive objective variant of MERIt and integrate it with two LLM +series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to +13 billion. The results on two challenging logical reasoning benchmarks +demonstrate the effectiveness of LogicLLM. Besides, we conduct extensive +ablation studies to analyze the key factors in designing logic-oriented proxy +tasks. +" +Make a Choice! Knowledge Base Question Answering with In-Context Learning,Chuanyuan Tan,http://arxiv.org/pdf/2305.13972v1.pdf,2023-05-23,['cs.cl'],2305.13972v1.pdf," Question answering over knowledge bases (KBQA) aims to answer factoid +questions with a given knowledge base (KB). Due to the large scale of KB, +annotated data is impossible to cover all fact schemas in KB, which poses a +challenge to the generalization ability of methods that require a sufficient +amount of annotated data. Recently, LLMs have shown strong few-shot performance +in many NLP tasks. We expect LLM can help existing methods improve their +generalization ability, especially in low-resource situations. In this paper, +we present McL-KBQA, a framework that incorporates the few-shot ability of LLM +into the KBQA method via ICL-based multiple choice and then improves the +effectiveness of the QA tasks. Experimental results on two KBQA datasets +demonstrate the competitive performance of McL-KBQA with strong improvements in +generalization. We expect to explore a new way to QA tasks from KBQA in +conjunction with LLM, how to generate answers normatively and correctly with +strong generalization. +" +CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation,Aswanth Kumar,http://arxiv.org/pdf/2305.14105v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14105v2.pdf," Large language models have demonstrated the capability to perform on machine +translation when the input is prompted with a few examples (in-context +learning). Translation quality depends on various features of the selected +examples, such as their quality and relevance, but previous work has +predominantly focused on individual features in isolation. In this paper, we +propose a general framework for combining different features influencing +example selection. We learn a regression model, CTQ Scorer (Contextual +Translation Quality), that selects examples based on multiple features in order +to maximize the translation quality. On multiple language pairs and language +models, we show that CTQ Scorer helps significantly outperform random selection +as well as strong single-factor baselines reported in the literature. We also +see an improvement of over 2.5 COMET points on average with respect to a strong +BM25 retrieval-based baseline. +" +Empowering LLM-based Machine Translation with Cultural Awareness,Binwei Yao,http://arxiv.org/pdf/2305.14328v1.pdf,2023-05-23,['cs.cl'],2305.14328v1.pdf," Traditional neural machine translation (NMT) systems often fail to translate +sentences that contain culturally specific information. Most previous NMT +methods have incorporated external cultural knowledge during training, which +requires fine-tuning on low-frequency items specific to the culture. Recent +in-context learning utilizes lightweight prompts to guide large language models +(LLMs) to perform machine translation, however, whether such an approach works +in terms of injecting culture awareness into machine translation remains +unclear. To this end, we introduce a new data curation pipeline to construct a +culturally relevant parallel corpus, enriched with annotations of +cultural-specific entities. Additionally, we design simple but effective +prompting strategies to assist this LLM-based translation. Extensive +experiments show that our approaches can largely help incorporate cultural +knowledge into LLM-based machine translation, outperforming traditional NMT +systems in translating cultural-specific sentences. +" +Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models,Miaoran Li,http://arxiv.org/pdf/2305.14623v1.pdf,2023-05-24,['cs.cl'],2305.14623v1.pdf," Fact-checking is an essential task in NLP that is commonly utilized for +validating the factual accuracy of claims. Prior work has mainly focused on +fine-tuning pre-trained languages models on specific datasets, which can be +computationally intensive and time-consuming. With the rapid development of +large language models (LLMs), such as ChatGPT and GPT-3, researchers are now +exploring their in-context learning capabilities for a wide range of tasks. In +this paper, we aim to assess the capacity of LLMs for fact-checking by +introducing Self-Checker, a framework comprising a set of plug-and-play modules +that facilitate fact-checking by purely prompting LLMs in an almost zero-shot +setting. This framework provides a fast and efficient way to construct +fact-checking systems in low-resource environments. Empirical results +demonstrate the potential of Self-Checker in utilizing LLMs for fact-checking. +However, there is still significant room for improvement compared to SOTA +fine-tuned models, which suggests that LLM adoption could be a promising +approach for future fact-checking research. +" +ExpertPrompting: Instructing Large Language Models to be Distinguished Experts,Benfeng Xu,http://arxiv.org/pdf/2305.14688v1.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14688v1.pdf," The answering quality of an aligned large language model (LLM) can be +drastically improved if treated with proper crafting of prompts. In this paper, +we propose ExpertPrompting to elicit the potential of LLMs to answer as +distinguished experts. We first utilize In-Context Learning to automatically +synthesize detailed and customized descriptions of the expert identity for each +specific instruction, and then ask LLMs to provide answer conditioned on such +agent background. Based on this augmented prompting strategy, we produce a new +set of instruction-following data using GPT-3.5, and train a competitive +open-source chat assistant called ExpertLLaMA. We employ GPT4-based evaluation +to show that 1) the expert data is of significantly higher quality than vanilla +answers, and 2) ExpertLLaMA outperforms existing open-source opponents and +achieves 96\% of the original ChatGPT's capability. All data and the +ExpertLLaMA model will be made publicly available at +\url{https://github.com/OFA-Sys/ExpertLLaMA}. +" +Adapting Language Models to Compress Contexts,Alexis Chevalier,http://arxiv.org/pdf/2305.14788v2.pdf,2023-05-24,['cs.cl'],2305.14788v2.pdf," Transformer-based language models (LMs) are powerful and widely-applicable +tools, but their usefulness is constrained by a finite context window and the +expensive computational cost of processing long text documents. We propose to +adapt pre-trained LMs into AutoCompressors. These language models are capable +of compressing long contexts into compact summary vectors, which are then +accessible to the model as soft prompts. Summary vectors are trained with an +unsupervised objective, whereby long documents are processed in segments, and +summary vectors from all previous segments are used in language modeling. We +fine-tune OPT and Llama-2 models on sequences of up to 30,720 tokens and show +that AutoCompressors can utilize long contexts to improve perplexity. We +evaluate AutoCompressors on in-context learning by compressing task +demonstrations and find that summary vectors are good substitutes for +plain-text demonstrations, increasing accuracy while reducing inference costs. +Finally, we explore the benefits of pre-computing summary vectors for large +corpora by applying summary vectors to retrievalaugmented language modeling and +a passage re-ranking task. Overall, AutoCompressors emerge as a simple and +inexpensive solution to extend the context window of LMs while speeding up +inference over long contexts. +" +ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games,Ruoyao Wang,http://arxiv.org/pdf/2305.14879v2.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14879v2.pdf," In this work, we investigate the capacity of language models to generate +explicit, interpretable, and interactive world models of scientific and +common-sense reasoning tasks. We operationalize this as a task of generating +text games, expressed as hundreds of lines of Python code. To facilitate this +task, we introduce ByteSized32 (Code: github.com/cognitiveailab/BYTESIZED32), a +corpus of 32 reasoning-focused text games totaling 20k lines of Python code. We +empirically demonstrate that GPT-4 can use these games as templates for +single-shot in-context learning, successfully producing runnable games on +unseen topics in 28% of cases. When allowed to self-reflect on program errors, +game runnability substantially increases to 57%. While evaluating simulation +fidelity is labor-intensive, we introduce a suite of automated metrics to +assess game fidelity, technical validity, adherence to task specifications, and +winnability, showing a high degree of agreement with expert human ratings. We +pose this as a challenge task to spur further development at the juncture of +world modeling and code generation. +" +Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning,Tianqing Fang,http://arxiv.org/pdf/2305.14970v1.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14970v1.pdf," Event temporal reasoning aims at identifying the temporal relations between +two or more events. However, knowledge conflicts arise when there is a mismatch +between the actual temporal relations of events in the context and the prior +knowledge or biases learned by the model. We first systematically define +distinct kinds of bias in event temporal reasoning, which include event +relation prior bias, tense bias, narrative bias, and dependency bias, as +indicators to study knowledge conflicts. To mitigate such event-related +knowledge conflict, we introduce a Counterfactual Data Augmentation based +method that can be applied to both Pre-trained Language Models (PLMs) and Large +Language Models (LLMs) either as additional training data or demonstrations for +In-Context Learning. Experiments suggest the importance of mitigating knowledge +conflicts in event temporal reasoning tasks for reducing hallucination and +highlight the potential of counterfactual data augmentation for improving model +performance. +" +Boosting Cross-lingual Transferability in Multilingual Models via In-Context Learning,Sunkyoung Kim,http://arxiv.org/pdf/2305.15233v1.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.15233v1.pdf," Existing cross-lingual transfer (CLT) prompting methods are only concerned +with monolingual demonstration examples in the source language. In this paper, +we propose In-CLT, a novel cross-lingual transfer prompting method that +leverages both source and target languages to construct the demonstration +examples. We conduct comprehensive evaluations on multilingual benchmarks, +focusing on question answering tasks. Experiment results show that In-CLT +prompt not only improves multilingual models' cross-lingual transferability, +but also demonstrates remarkable unseen language generalization ability. In-CLT +prompting, in particular, improves model performance by 10 to 20\% points on +average when compared to prior cross-lingual transfer approaches. We also +observe the surprising performance gain on the other multilingual benchmarks, +especially in reasoning tasks. Furthermore, we investigate the relationship +between lexical similarity and pre-training corpora in terms of the +cross-lingual transfer gap. +" +A Mechanism for Solving Relational Tasks in Transformer Language Models,Jack Merullo,http://arxiv.org/pdf/2305.16130v2.pdf,2023-05-25,"['cs.cl', 'cs.lg']",2305.16130v2.pdf," A primary criticism towards language models (LMs) is their inscrutability. +This paper presents evidence that, despite their size and complexity, LMs +sometimes exploit a simple computational mechanism to solve one-to-one +relational tasks (e.g., capital_of(Poland)=Warsaw). We investigate a range of +language model sizes (from 124M parameters to 176B parameters) in an in-context +learning setting, and find that for a variety of tasks (involving capital +cities, upper-casing, and past-tensing) a key part of the mechanism reduces to +a simple linear update typically applied by the feedforward (FFN) networks. +These updates also tend to promote the output of the relation in a +content-independent way (e.g., encoding Poland:Warsaw::China:Beijing), +revealing a predictable pattern that these models take in solving these tasks. +We further show that this mechanism is specific to tasks that require retrieval +from pretraining memory, rather than retrieval from local context. Our results +contribute to a growing body of work on the mechanistic interpretability of +LLMs, and offer reason to be optimistic that, despite the massive and +non-linear nature of the models, the strategies they ultimately use to solve +tasks can sometimes reduce to familiar and even intuitive algorithms. +" +Large Language Models Are Partially Primed in Pronoun Interpretation,Suet-Ying Lam,http://arxiv.org/pdf/2305.16917v1.pdf,2023-05-26,['cs.cl'],2305.16917v1.pdf," While a large body of literature suggests that large language models (LLMs) +acquire rich linguistic representations, little is known about whether they +adapt to linguistic biases in a human-like way. The present study probes this +question by asking whether LLMs display human-like referential biases using +stimuli and procedures from real psycholinguistic experiments. Recent +psycholinguistic studies suggest that humans adapt their referential biases +with recent exposure to referential patterns; closely replicating three +relevant psycholinguistic experiments from Johnson & Arnold (2022) in an +in-context learning (ICL) framework, we found that InstructGPT adapts its +pronominal interpretations in response to the frequency of referential patterns +in the local discourse, though in a limited fashion: adaptation was only +observed relative to syntactic but not semantic biases. By contrast, FLAN-UL2 +fails to generate meaningful patterns. Our results provide further evidence +that contemporary LLMs discourse representations are sensitive to syntactic +patterns in the local context but less so to semantic patterns. Our data and +code are available at \url{https://github.com/zkx06111/llm_priming}. +" +A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks,Jacob Abernethy,http://arxiv.org/pdf/2305.17040v1.pdf,2023-05-26,"['cs.lg', 'cs.cl']",2305.17040v1.pdf," We study the phenomenon of \textit{in-context learning} (ICL) exhibited by +large language models, where they can adapt to a new learning task, given a +handful of labeled examples, without any explicit parameter optimization. Our +goal is to explain how a pre-trained transformer model is able to perform ICL +under reasonable assumptions on the pre-training process and the downstream +tasks. We posit a mechanism whereby a transformer can achieve the following: +(a) receive an i.i.d. sequence of examples which have been converted into a +prompt using potentially-ambiguous delimiters, (b) correctly segment the prompt +into examples and labels, (c) infer from the data a \textit{sparse linear +regressor} hypothesis, and finally (d) apply this hypothesis on the given test +example and return a predicted label. We establish that this entire procedure +is implementable using the transformer mechanism, and we give sample complexity +guarantees for this learning framework. Our empirical findings validate the +challenge of segmentation, and we show a correspondence between our posited +mechanisms and observed attention maps for step (c). +" +Augmenting Large Language Model Translators via Translation Memories,Yongyu Mu,http://arxiv.org/pdf/2305.17367v1.pdf,2023-05-27,['cs.cl'],2305.17367v1.pdf," Using translation memories (TMs) as prompts is a promising approach to +in-context learning of machine translation models. In this work, we take a step +towards prompting large language models (LLMs) with TMs and making them better +translators. We find that the ability of LLMs to ``understand'' prompts is +indeed helpful for making better use of TMs. Experiments show that the results +of a pre-trained LLM translator can be greatly improved by using high-quality +TM-based prompts. These results are even comparable to those of the +state-of-the-art NMT systems which have access to large-scale in-domain +bilingual data and are well tuned on the downstream tasks. +" +In-Context Analogical Reasoning with Pre-Trained Language Models,Xiaoyang Hu,http://arxiv.org/pdf/2305.17626v2.pdf,2023-05-28,"['cs.ai', 'cs.cl', 'cs.lg']",2305.17626v2.pdf," Analogical reasoning is a fundamental capacity of human cognition that allows +us to reason abstractly about novel situations by relating them to past +experiences. While it is thought to be essential for robust reasoning in AI +systems, conventional approaches require significant training and/or +hard-coding of domain knowledge to be applied to benchmark tasks. Inspired by +cognitive science research that has found connections between human language +and analogy-making, we explore the use of intuitive language-based abstractions +to support analogy in AI systems. Specifically, we apply large pre-trained +language models (PLMs) to visual Raven's Progressive Matrices (RPM), a common +relational reasoning test. By simply encoding the perceptual features of the +problem into language form, we find that PLMs exhibit a striking capacity for +zero-shot relational reasoning, exceeding human performance and nearing +supervised vision-based methods. We explore different encodings that vary the +level of abstraction over task features, finding that higher-level abstractions +further strengthen PLMs' analogical reasoning. Our detailed analysis reveals +insights on the role of model complexity, in-context learning, and prior +knowledge in solving RPM tasks. +" +Towards Explainable Conversational Recommender Systems,Shuyu Guo,http://arxiv.org/pdf/2305.18363v1.pdf,2023-05-27,"['cs.ir', 'cs.ai']",2305.18363v1.pdf," Explanations in conventional recommender systems have demonstrated benefits +in helping the user understand the rationality of the recommendations and +improving the system's efficiency, transparency, and trustworthiness. In the +conversational environment, multiple contextualized explanations need to be +generated, which poses further challenges for explanations. To better measure +explainability in conversational recommender systems (CRS), we propose ten +evaluation perspectives based on concepts from conventional recommender systems +together with the characteristics of CRS. We assess five existing CRS benchmark +datasets using these metrics and observe the necessity of improving the +explanation quality of CRS. To achieve this, we conduct manual and automatic +approaches to extend these dialogues and construct a new CRS dataset, namely +Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with +over 2,000 high-quality rewritten explanations. We compare two baseline +approaches to perform explanation generation based on E-ReDial. Experimental +results suggest that models trained on E-ReDial can significantly improve +explainability while introducing knowledge into the models can further improve +the performance. GPT-3 in the in-context learning setting can generate more +realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial +can better generate clear reasons for recommendations based on user +preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial. +" +Grammar Prompting for Domain-Specific Language Generation with Large Language Models,Bailin Wang,http://arxiv.org/pdf/2305.19234v3.pdf,2023-05-30,"['cs.cl', 'cs.ai']",2305.19234v3.pdf," Large language models (LLMs) can learn to perform a wide range of natural +language tasks from just a handful of in-context examples. However, for +generating strings from highly structured languages (e.g., semantic parsing to +complex domain-specific languages), it is challenging for the LLM to generalize +from just a few exemplars. We propose \emph{grammar prompting}, a simple +approach to enable LLMs to use external knowledge and domain-specific +constraints, expressed through a grammar in Backus--Naur Form (BNF), during +in-context learning. Grammar prompting augments each demonstration example with +a specialized grammar that is minimally sufficient for generating the +particular output example, where the specialized grammar is a subset of the +full DSL grammar. For inference, the LLM first predicts a BNF grammar given a +test input, and then generates the output according to the rules of the +grammar. Experiments demonstrate that grammar prompting can enable LLMs to +perform competitively on a diverse set of DSL generation tasks, including +semantic parsing (SMCalFlow, Overnight, GeoQuery), PDDL planning, and +SMILES-based molecule generation. +" +Contextual Vision Transformers for Robust Representation Learning,Yujia Bao,http://arxiv.org/pdf/2305.19402v2.pdf,2023-05-30,"['cs.cv', 'cs.ai', 'cs.cl']",2305.19402v2.pdf," We introduce Contextual Vision Transformers (ContextViT), a method designed +to generate robust image representations for datasets experiencing shifts in +latent factors across various groups. Derived from the concept of in-context +learning, ContextViT incorporates an additional context token to encapsulate +group-specific information. This integration allows the model to adjust the +image representation in accordance with the group-specific context. +Specifically, for a given input image, ContextViT maps images with identical +group membership into this context token, which is appended to the input image +tokens. Additionally, we introduce a context inference network to predict such +tokens on-the-fly, given a batch of samples from the group. This enables +ContextViT to adapt to new testing distributions during inference time. We +demonstrate the efficacy of ContextViT across a wide range of applications. In +supervised fine-tuning, we show that augmenting pre-trained ViTs with our +proposed context conditioning mechanism results in consistent improvements in +out-of-distribution generalization on iWildCam and FMoW. We also investigate +self-supervised representation learning with ContextViT. Our experiments on the +Camelyon17 pathology imaging benchmark and the JUMP-CP microscopy imaging +benchmark demonstrate that ContextViT excels in learning stable image +featurizations amidst distribution shift, consistently outperforming its ViT +counterpart. +" +Self-Verification Improves Few-Shot Clinical Information Extraction,Zelalem Gero,http://arxiv.org/pdf/2306.00024v1.pdf,2023-05-30,"['cs.cl', 'cs.lg']",2306.00024v1.pdf," Extracting patient information from unstructured text is a critical task in +health decision-support and clinical research. Large language models (LLMs) +have shown the potential to accelerate clinical curation via few-shot +in-context learning, in contrast to supervised learning which requires much +more costly human annotations. However, despite drastic advances in modern LLMs +such as GPT-4, they still struggle with issues regarding accuracy and +interpretability, especially in mission-critical domains such as health. Here, +we explore a general mitigation framework using self-verification, which +leverages the LLM to provide provenance for its own extraction and check its +own outputs. This is made possible by the asymmetry between verification and +generation, where the latter is often much easier than the former. Experimental +results show that our method consistently improves accuracy for various LLMs in +standard clinical information extraction tasks. Additionally, self-verification +yields interpretations in the form of a short text span corresponding to each +output, which makes it very efficient for human experts to audit the results, +paving the way towards trustworthy extraction of clinical information in +resource-constrained scenarios. To facilitate future research in this +direction, we release our code and prompts. +" +ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?,Michael Heck,http://arxiv.org/pdf/2306.01386v1.pdf,2023-06-02,"['cs.cl', 'cs.ai']",2306.01386v1.pdf," Recent research on dialogue state tracking (DST) focuses on methods that +allow few- and zero-shot transfer to new domains or schemas. However, +performance gains heavily depend on aggressive data augmentation and +fine-tuning of ever larger language model based architectures. In contrast, +general purpose language models, trained on large amounts of diverse data, hold +the promise of solving any kind of task without task-specific training. We +present preliminary experimental results on the ChatGPT research preview, +showing that ChatGPT achieves state-of-the-art performance in zero-shot DST. +Despite our findings, we argue that properties inherent to general purpose +models limit their ability to replace specialized systems. We further theorize +that the in-context learning capabilities of such models will likely become +powerful tools to support the development of dedicated and dynamic dialogue +state trackers. +" +Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models,Fengzhu Zeng,http://arxiv.org/pdf/2306.02569v1.pdf,2023-06-05,['cs.cl'],2306.02569v1.pdf," Few-shot or zero-shot fact verification only relies on a few or no labeled +training examples. In this paper, we propose a novel method called ProToCo, to +\underline{Pro}mpt pre-trained language models (PLMs) \underline{To} be +\underline{Co}nsistent, for improving the factuality assessment capability of +PLMs in the few-shot and zero-shot settings. Given a claim-evidence pair, +ProToCo generates multiple variants of the claim with different relations and +frames a simple consistency mechanism as constraints for making compatible +predictions across these variants. We update PLMs by using parameter-efficient +fine-tuning (PEFT), leading to more accurate predictions in few-shot and +zero-shot fact verification tasks. Our experiments on three public verification +datasets show that ProToCo significantly outperforms state-of-the-art few-shot +fact verification baselines. With a small number of unlabeled instances, +ProToCo also outperforms the strong zero-shot learner T0 on zero-shot +verification. Compared to large PLMs using in-context learning (ICL) method, +ProToCo outperforms OPT-30B and the Self-Consistency-enabled OPT-6.7B model in +both few- and zero-shot settings. +" +STEPS: A Benchmark for Order Reasoning in Sequential Tasks,Weizhi Wang,http://arxiv.org/pdf/2306.04441v1.pdf,2023-06-07,['cs.cl'],2306.04441v1.pdf," Various human activities can be abstracted into a sequence of actions in +natural text, i.e. cooking, repairing, manufacturing, etc. Such action +sequences heavily depend on the executing order, while disorder in action +sequences leads to failure of further task execution by robots or AI agents. +Therefore, to verify the order reasoning capability of current neural models in +sequential tasks, we propose a challenging benchmark , named STEPS. STEPS +involves two subtask settings, focusing on determining the rationality of given +next step in recipes and selecting the reasonable step from the multi-choice +question, respectively. We describe the data construction and task +formulations, and benchmark most of significant Large Language Models (LLMs). +The experimental results demonstrate 1) The commonsense reasoning of action +orders in sequential tasks are challenging to resolve via zero-shot prompting +or few-shot in-context learning for LLMs; 2) Prompting method still +significantly lags behind tuning-based method on STEPS. +" +Modular Visual Question Answering via Code Generation,Sanjay Subramanian,http://arxiv.org/pdf/2306.05392v1.pdf,2023-06-08,['cs.cl'],2306.05392v1.pdf," We present a framework that formulates visual question answering as modular +code generation. In contrast to prior work on modular approaches to VQA, our +approach requires no additional training and relies on pre-trained language +models (LMs), visual models pre-trained on image-caption pairs, and fifty VQA +examples used for in-context learning. The generated Python programs invoke and +compose the outputs of the visual models using arithmetic and conditional +logic. Our approach improves accuracy on the COVR dataset by at least 3% and on +the GQA dataset by roughly 2% compared to the few-shot baseline that does not +employ code generation. +" +Measuring and Modifying Factual Knowledge in Large Language Models,Pouya Pezeshkpour,http://arxiv.org/pdf/2306.06264v1.pdf,2023-06-09,"['cs.cl', 'cs.lg']",2306.06264v1.pdf," Large Language Models (LLMs) store an extensive amount of factual knowledge +obtained from vast collections of text. To effectively utilize these models for +downstream tasks, it is crucial to have reliable methods for measuring their +knowledge. However, existing approaches for knowledge measurement have certain +limitations, and despite recent efforts, they fail to provide accurate +measurements and the necessary insights for modifying the knowledge within +LLMs. In this work, we employ information theory-based measurements to provide +a framework estimating the factual knowledge contained within large language +models. More specifically, we measure knowledge by analyzing the LLM's +prediction probability distribution before and after instilling the target +knowledge, employing metrics such as entropy and KL-divergence. Introducing our +metrics, we first assess their accuracy in comparison to previous ranking-based +methods, surpassing them by over $35\%$ in a synthetic experiment. Then, we +explore two prominent methods of knowledge instillation, discovering that LLMs +exhibit limitations in capturing new knowledge under specific circumstances for +one of these methods. Lastly, we demonstrate the applicability of our methods +in extracting unlearned and mislearned facts in LLMs through their application +to in-context learning. We make code and data for all methods and experiments +in this paper publicly available. +" +A Survey on Multimodal Large Language Models,Shukang Yin,http://arxiv.org/pdf/2306.13549v1.pdf,2023-06-23,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2306.13549v1.pdf," Multimodal Large Language Model (MLLM) recently has been a new rising +research hotspot, which uses powerful Large Language Models (LLMs) as a brain +to perform multimodal tasks. The surprising emergent capabilities of MLLM, such +as writing stories based on images and OCR-free math reasoning, are rare in +traditional methods, suggesting a potential path to artificial general +intelligence. In this paper, we aim to trace and summarize the recent progress +of MLLM. First of all, we present the formulation of MLLM and delineate its +related concepts. Then, we discuss the key techniques and applications, +including Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning +(M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning +(LAVR). Finally, we discuss existing challenges and point out promising +research directions. In light of the fact that the era of MLLM has only just +begun, we will keep updating this survey and hope it can inspire more research. +An associated GitHub link collecting the latest papers is available at +https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models. +" +Potential Benefits of Employing Large Language Models in Research in Moral Education and Development,Hyemin Han,http://arxiv.org/pdf/2306.13805v2.pdf,2023-06-23,"['cs.cy', 'cs.ai']",2306.13805v2.pdf," Recently, computer scientists have developed large language models (LLMs) by +training prediction models with large-scale language corpora and human +reinforcements. The LLMs have become one promising way to implement artificial +intelligence with accuracy in various fields. Interestingly, recent LLMs +possess emergent functional features that emulate sophisticated human +cognition, especially in-context learning and the chain of thought, which were +unavailable in previous prediction models. In this paper, I will examine how +LLMs might contribute to moral education and development research. To achieve +this goal, I will review the most recently published conference papers and +ArXiv preprints to overview the novel functional features implemented in LLMs. +I also intend to conduct brief experiments with ChatGPT to investigate how LLMs +behave while addressing ethical dilemmas and external feedback. The results +suggest that LLMs might be capable of solving dilemmas based on reasoning and +revising their reasoning process with external input. Furthermore, a +preliminary experimental result from the moral exemplar test may demonstrate +that exemplary stories can elicit moral elevation in LLMs as do they among +human participants. I will discuss the potential implications of LLMs on +research on moral education and development with the results. +" +DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios,Vinicius G. Goecks,http://arxiv.org/pdf/2306.17271v1.pdf,2023-06-29,"['cs.lg', 'i.2.7; j.7; k.4.0']",2306.17271v1.pdf," The development of plans of action in disaster response scenarios is a +time-consuming process. Large Language Models (LLMs) offer a powerful solution +to expedite this process through in-context learning. This study presents +DisasterResponseGPT, an algorithm that leverages LLMs to generate valid plans +of action quickly by incorporating disaster response and planning guidelines in +the initial prompt. In DisasterResponseGPT, users input the scenario +description and receive a plan of action as output. The proposed method +generates multiple plans within seconds, which can be further refined following +the user's feedback. Preliminary results indicate that the plans of action +developed by DisasterResponseGPT are comparable to human-generated ones while +offering greater ease of modification in real-time. This approach has the +potential to revolutionize disaster response operations by enabling rapid +updates and adjustments during the plan's execution. +" +Meta-Reasoning: Semantics-Symbol Deconstruction For Large Language Models,Yiming Wang,http://arxiv.org/pdf/2306.17820v2.pdf,2023-06-30,['cs.cl'],2306.17820v2.pdf," Neural-symbolic methods have shown their effectiveness in enhancing the +reasoning abilities of large language models (LLMs). However, existing methods +primarily rely on mapping natural languages to more syntactically complete +formal languages (e.g., Python and SQL). Those approaches necessitate that +reasoning tasks be convertible into programs, which cater more to the computer +execution mindset and deviate from human reasoning habits. To expand the +real-world applicability and flexibility of symbolic methods, we propose +Meta-Reasoning from the scope of linguistics itself. This method empowers LLMs +to deconstruct questions and effectively capture more generalized knowledge +autonomously. We find that Meta-Reasoning achieves improved in-context learning +efficiency, reasoning accuracy, and output stability in six arithmetic and +symbolic reasoning tasks. In particular, when applied to symbolic reasoning +tasks such as Tracking Shuffled Objects, GPT-3 (text-davinci-002) surpasses the +few-shot Chain-of-Thought prompting approach (+37.7%), with 99% accuracy after +a single demonstration of Meta-Reasoning. +" +Assessing the efficacy of large language models in generating accurate teacher responses,Yann Hicke,http://arxiv.org/pdf/2307.04274v1.pdf,2023-07-09,"['cs.cl', 'cs.lg']",2307.04274v1.pdf," (Tack et al., 2023) organized the shared task hosted by the 18th Workshop on +Innovative Use of NLP for Building Educational Applications on generation of +teacher language in educational dialogues. Following the structure of the +shared task, in this study, we attempt to assess the generative abilities of +large language models in providing informative and helpful insights to +students, thereby simulating the role of a knowledgeable teacher. To this end, +we present an extensive evaluation of several benchmarking generative models, +including GPT-4 (few-shot, in-context learning), fine-tuned GPT-2, and +fine-tuned DialoGPT. Additionally, to optimize for pedagogical quality, we +fine-tuned the Flan-T5 model using reinforcement learning. Our experimental +findings on the Teacher-Student Chatroom Corpus subset indicate the efficacy of +GPT-4 over other fine-tuned models, measured using BERTScore and DialogRPT. + We hypothesize that several dataset characteristics, including sampling, +representativeness, and dialog completeness, pose significant challenges to +fine-tuning, thus contributing to the poor generalizability of the fine-tuned +models. Finally, we note the need for these generative models to be evaluated +with a metric that relies not only on dialog coherence and matched language +modeling distribution but also on the model's ability to showcase pedagogical +skills. +" +Unsupervised Calibration through Prior Adaptation for Text Classification using Large Language Models,Lautaro Estienne,http://arxiv.org/pdf/2307.06713v3.pdf,2023-07-13,"['cs.cl', 'cs.lg']",2307.06713v3.pdf," A wide variety of natural language tasks are currently being addressed with +large-scale language models (LLMs). These models are usually trained with a +very large amount of unsupervised text data and adapted to perform a downstream +natural language task using methods like fine-tuning, calibration or in-context +learning. In this work, we propose an approach to adapt the prior class +distribution to perform text classification tasks without the need for labelled +samples and only few in-domain sample queries. The proposed approach treats the +LLM as a black box, adding a stage where the model posteriors are calibrated to +the task. Results show that these methods outperform the un-adapted model for +different number of training shots in the prompt and a previous approach were +calibration is performed without using any adaptation data. +" +Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation,Yahui Fu,http://arxiv.org/pdf/2308.00085v2.pdf,2023-07-28,"['cs.cl', 'cs.ai']",2308.00085v2.pdf," Recent approaches to empathetic response generation try to incorporate +commonsense knowledge or reasoning about the causes of emotions to better +understand the user's experiences and feelings. However, these approaches +mainly focus on understanding the causalities of context from the user's +perspective, ignoring the system's perspective. In this paper, we propose a +commonsense-based causality explanation approach for diverse empathetic +response generation that considers both the user's perspective (user's desires +and reactions) and the system's perspective (system's intentions and +reactions). We enhance ChatGPT's ability to reason for the system's perspective +by integrating in-context learning with commonsense knowledge. Then, we +integrate the commonsense-based causality explanation with both ChatGPT and a +T5-based model. Experimental evaluations demonstrate that our method +outperforms other comparable methods on both automatic and human evaluations. +" +Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models,Zheyu Zhang,http://arxiv.org/pdf/2308.01684v2.pdf,2023-08-03,['cs.cl'],2308.01684v2.pdf," Large Language Models (LLMs) demonstrate remarkable performance on a variety +of natural language understanding (NLU) tasks, primarily due to their +in-context learning ability. This ability could be applied to building babylike +models, i.e. models at small scales, improving training efficiency. In this +paper, we propose a ""CoThought"" pipeline, which efficiently trains smaller +""baby"" language models (BabyLMs) by leveraging the Chain of Thought prompting +of LLMs. Our pipeline restructures a dataset of less than 100M in size using +GPT-3.5-turbo, transforming it into task-oriented, human-readable texts that +are comparable to the school texts for language learners. The BabyLM is then +pretrained on this restructured dataset in a RoBERTa fashion. In evaluations +across 4 benchmarks, our BabyLM outperforms the vanilla RoBERTa in 10 +linguistic, NLU, and question-answering tasks by more than 3 points, showing a +superior ability to extract contextual information. These results suggest that +compact LMs pretrained on small, LLM-restructured data can better understand +tasks and achieve improved performance. +" +FLIRT: Feedback Loop In-context Red Teaming,Ninareh Mehrabi,http://arxiv.org/pdf/2308.04265v1.pdf,2023-08-08,['cs.ai'],2308.04265v1.pdf," Warning: this paper contains content that may be inappropriate or offensive. + As generative models become available for public use in various applications, +testing and analyzing vulnerabilities of these models has become a priority. +Here we propose an automatic red teaming framework that evaluates a given model +and exposes its vulnerabilities against unsafe and inappropriate content +generation. Our framework uses in-context learning in a feedback loop to red +team models and trigger them into unsafe content generation. We propose +different in-context attack strategies to automatically learn effective and +diverse adversarial prompts for text-to-image models. Our experiments +demonstrate that compared to baseline approaches, our proposed strategy is +significantly more effective in exposing vulnerabilities in Stable Diffusion +(SD) model, even when the latter is enhanced with safety features. Furthermore, +we demonstrate that the proposed framework is effective for red teaming +text-to-text models, resulting in significantly higher toxic response +generation rate compared to previously reported numbers. +" +JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models,Peike Li,http://arxiv.org/pdf/2308.04729v1.pdf,2023-08-09,"['cs.sd', 'cs.ai', 'cs.lg', 'cs.mm', 'eess.as']",2308.04729v1.pdf," Music generation has attracted growing interest with the advancement of deep +generative models. However, generating music conditioned on textual +descriptions, known as text-to-music, remains challenging due to the complexity +of musical structures and high sampling rate requirements. Despite the task's +significance, prevailing generative models exhibit limitations in music +quality, computational efficiency, and generalization. This paper introduces +JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a +diffusion model incorporating both autoregressive and non-autoregressive +training. Through in-context learning, JEN-1 performs various generation tasks +including text-guided music generation, music inpainting, and continuation. +Evaluations demonstrate JEN-1's superior performance over state-of-the-art +methods in text-music alignment and music quality while maintaining +computational efficiency. Our demos are available at +http://futureverse.com/research/jen/demos/jen1 +" +Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models,Bilgehan Sel,http://arxiv.org/pdf/2308.10379v2.pdf,2023-08-20,"['cs.cl', 'cs.ai']",2308.10379v2.pdf," Current literature, aiming to surpass the ""Chain-of-Thought"" approach, often +resorts to an external modus operandi involving halting, modifying, and then +resuming the generation process to boost Large Language Models' (LLMs) +reasoning capacities. This mode escalates the number of query requests, leading +to increased costs, memory, and computational overheads. Addressing this, we +propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through +algorithmic reasoning pathways, pioneering a new mode of in-context learning. +By employing algorithmic examples, we exploit the innate recurrence dynamics of +LLMs, expanding their idea exploration with merely one or a few queries. Our +technique outperforms earlier single-query methods and stands on par with a +recent multi-query strategy that employs an extensive tree search algorithm. +Intriguingly, our results suggest that instructing an LLM using an algorithm +can lead to performance surpassing that of the algorithm itself, hinting at +LLM's inherent ability to weave its intuition into optimized searches. We probe +into the underpinnings of our method's efficacy and its nuances in application. +" +Building Emotional Support Chatbots in the Era of LLMs,Zhonghua Zheng,http://arxiv.org/pdf/2308.11584v1.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.11584v1.pdf," The integration of emotional support into various conversational scenarios +presents profound societal benefits, such as social interactions, mental health +counseling, and customer service. However, there are unsolved challenges that +hinder real-world applications in this field, including limited data +availability and the absence of well-accepted model training paradigms. This +work endeavors to navigate these challenges by harnessing the capabilities of +Large Language Models (LLMs). We introduce an innovative methodology that +synthesizes human insights with the computational prowess of LLMs to curate an +extensive emotional support dialogue dataset. Our approach is initiated with a +meticulously designed set of dialogues spanning diverse scenarios as generative +seeds. By utilizing the in-context learning potential of ChatGPT, we +recursively generate an ExTensible Emotional Support dialogue dataset, named +ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, +examining the impact of diverse training strategies, ultimately yielding an LLM +meticulously optimized for emotional support interactions. An exhaustive +assessment of the resultant model showcases its proficiency in offering +emotional support, marking a pivotal step in the realm of emotional support +bots and paving the way for subsequent research and implementations. +" +Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning,Jiasheng Ye,http://arxiv.org/pdf/2308.12219v2.pdf,2023-08-23,"['cs.cl', 'cs.ai', 'cs.lg']",2308.12219v2.pdf," The recent surge of generative AI has been fueled by the generative power of +diffusion probabilistic models and the scalable capabilities of large language +models. Despite their potential, it remains elusive whether diffusion language +models can solve general language tasks comparable to their autoregressive +counterparts. This paper demonstrates that scaling diffusion models w.r.t. +data, sizes, and tasks can effectively make them strong language learners. We +build competent diffusion language models at scale by first acquiring knowledge +from massive data via masked language modeling pretraining thanks to their +intrinsic connections. We then reprogram pretrained masked language models into +diffusion language models via diffusive adaptation, wherein task-specific +finetuning and instruction finetuning are explored to unlock their versatility +in solving general language tasks. Experiments show that scaling diffusion +language models consistently improves performance across downstream language +tasks. We further discover that instruction finetuning can elicit zero-shot and +few-shot in-context learning abilities that help tackle many unseen tasks by +following natural language instructions, and show promise in advanced and +challenging abilities such as reasoning. +" +Large Language Model as Autonomous Decision Maker,Yining Ye,http://arxiv.org/pdf/2308.12519v1.pdf,2023-08-24,['cs.cl'],2308.12519v1.pdf," While large language models (LLMs) exhibit impressive language understanding +and in-context learning abilities, their decision-making ability still heavily +relies on the guidance of task-specific expert knowledge when solving +real-world tasks. To unleash the potential of LLMs as autonomous decision +makers, this paper presents an approach JuDec to endow LLMs with the +self-judgment ability, enabling LLMs to achieve autonomous judgment and +exploration for decision making. Specifically, in JuDec, Elo-based +Self-Judgment Mechanism is designed to assign Elo scores to decision steps to +judge their values and utilities via pairwise comparisons between two solutions +and then guide the decision-searching process toward the optimal solution +accordingly. Experimental results on the ToolBench dataset demonstrate JuDec's +superiority over baselines, achieving over 10% improvement in Pass Rate on +diverse tasks. It offers higher-quality solutions and reduces costs (ChatGPT +API calls), highlighting its effectiveness and efficiency. +" +Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance,Lefteris Loukas,http://arxiv.org/pdf/2308.14634v1.pdf,2023-08-28,"['cs.cl', 'cs.ai', 'cs.lg', 'q-fin.cp']",2308.14634v1.pdf," We propose the use of conversational GPT models for easy and quick few-shot +text classification in the financial domain using the Banking77 dataset. Our +approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes +the technical expertise required and eliminates the need for expensive GPU +computing while yielding quick and accurate results. Additionally, we fine-tune +other pre-trained, masked language models with SetFit, a recent contrastive +learning technique, to achieve state-of-the-art results both in full-data and +few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can +outperform fine-tuned, non-generative models even with fewer examples. However, +subscription fees associated with these solutions may be considered costly for +small organizations. Lastly, we find that generative models perform better on +the given task when shown representative samples selected by a human expert +rather than when shown random ones. We conclude that a) our proposed methods +offer a practical solution for few-shot tasks in datasets with limited label +availability, and b) our state-of-the-art results can inspire future work in +the area. +" +Gender-specific Machine Translation with Large Language Models,Eduardo Sánchez,http://arxiv.org/pdf/2309.03175v1.pdf,2023-09-06,['cs.cl'],2309.03175v1.pdf," Decoder-only Large Language Models (LLMs) have demonstrated potential in +machine translation (MT), albeit with performance slightly lagging behind +traditional encoder-decoder Neural Machine Translation (NMT) systems. However, +LLMs offer a unique advantage: the ability to control the properties of the +output through prompts. In this study, we harness this flexibility to explore +LLaMa's capability to produce gender-specific translations for languages with +grammatical gender. Our results indicate that LLaMa can generate +gender-specific translations with competitive accuracy and gender bias +mitigation when compared to NLLB, a state-of-the-art multilingual NMT system. +Furthermore, our experiments reveal that LLaMa's translations are robust, +showing significant performance drops when evaluated against opposite-gender +references in gender-ambiguous datasets but maintaining consistency in less +ambiguous contexts. This research provides insights into the potential and +challenges of using LLMs for gender-specific translations and highlights the +importance of in-context learning to elicit new tasks in LLMs. +" +Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty,Chen Ling,http://arxiv.org/pdf/2309.03433v1.pdf,2023-09-07,['cs.cl'],2309.03433v1.pdf," Open Information Extraction (OIE) task aims at extracting structured facts +from unstructured text, typically in the form of (subject, relation, object) +triples. Despite the potential of large language models (LLMs) like ChatGPT as +a general task solver, they lag behind state-of-the-art (supervised) methods in +OIE tasks due to two key issues. First, LLMs struggle to distinguish irrelevant +context from relevant relations and generate structured output due to the +restrictions on fine-tuning the model. Second, LLMs generates responses +autoregressively based on probability, which makes the predicted relations lack +confidence. In this paper, we assess the capabilities of LLMs in improving the +OIE task. Particularly, we propose various in-context learning strategies to +enhance LLM's instruction-following ability and a demonstration uncertainty +quantification module to enhance the confidence of the generated relations. Our +experiments on three OIE benchmark datasets show that our approach holds its +own against established supervised methods, both quantitatively and +qualitatively. +" +EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets,Hongyuan Lu,http://arxiv.org/pdf/2309.04725v1.pdf,2023-09-09,['cs.cl'],2309.04725v1.pdf," Large language models (LLMs) have shown promising performance on various NLP +tasks via task prompting. And their performance can be further improved by +appending task demonstrations to the head of the prompt. And usually, a better +performance can be achieved with more demonstrations. However, asking the users +to write the demonstrations can be cumbersome. As a simple yet cost-effective +workaround, this paper proposes a novel method called EPA (\textbf{E}asy +\textbf{P}rompt \textbf{A}ugmentation)\footnote{While this paper considers +augmenting prompts via demonstrations, we name it EPA as the name EDA is +already taken by a well-known NLP method \citep{wei-zou-2019-eda}.} that +effectively minimizes user efforts in writing demonstrations while improving +the model performance at the same time. EPA achieves these goals by +automatically augmenting the demonstrations with multiple sources/targets, +where each of them paraphrases each other. This is well motivated as augmenting +data via paraphrasing effectively improves neural language models. EPA thus +employs paraphrasing as an augmentation method for in-context learning. +Extensive experiments indicate that EPA effectively improves both NLU and NLG +tasks, covering from natural language inference to machine translation in +translating tens of languages.\footnote{Code and data will be released upon +publication.} +" +CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation,Chao-Wei Huang,http://arxiv.org/pdf/2309.06748v1.pdf,2023-09-13,"['cs.cl', 'cs.ir']",2309.06748v1.pdf," Conversational search provides a natural interface for information retrieval +(IR). Recent approaches have demonstrated promising results in applying dense +retrieval to conversational IR. However, training dense retrievers requires +large amounts of in-domain paired data. This hinders the development of +conversational dense retrievers, as abundant in-domain conversations are +expensive to collect. In this paper, we propose CONVERSER, a framework for +training conversational dense retrievers with at most 6 examples of in-domain +dialogues. Specifically, we utilize the in-context learning capability of large +language models to generate conversational queries given a passage in the +retrieval corpus. Experimental results on conversational retrieval benchmarks +OR-QuAC and TREC CAsT 19 show that the proposed CONVERSER achieves comparable +performance to fully-supervised models, demonstrating the effectiveness of our +proposed framework in few-shot conversational dense retrieval. All source code +and generated datasets are available at https://github.com/MiuLab/CONVERSER +" +Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer,Yongqi Wang,http://arxiv.org/pdf/2309.07566v1.pdf,2023-09-14,"['cs.sd', 'cs.ai', 'eess.as']",2309.07566v1.pdf," Direct speech-to-speech translation (S2ST) with discrete self-supervised +representations has achieved remarkable accuracy, but is unable to preserve the +speaker timbre of the source speech during translation. Meanwhile, the scarcity +of high-quality speaker-parallel data poses a challenge for learning style +transfer between source and target speech. We propose an S2ST framework with an +acoustic language model based on discrete units from a self-supervised model +and a neural codec for style transfer. The acoustic language model leverages +self-supervised in-context learning, acquiring the ability for style transfer +without relying on any speaker-parallel data, thereby overcoming the issue of +data scarcity. By using extensive training data, our model achieves zero-shot +cross-lingual style transfer on previously unseen source languages. Experiments +show that our model generates translated speeches with high fidelity and style +similarity. Audio samples are available at http://stylelm.github.io/ . +" +"Bridging Topic, Domain, and Language Shifts: An Evaluation of Comprehensive Out-of-Distribution Scenarios",Andreas Waldis,http://arxiv.org/pdf/2309.08316v1.pdf,2023-09-15,['cs.cl'],2309.08316v1.pdf," Language models (LMs) excel in in-distribution (ID) scenarios where train and +test data are independent and identically distributed. However, their +performance often degrades in real-world applications like argument mining. +Such degradation happens when new topics emerge, or other text domains and +languages become relevant. To assess LMs' generalization abilities in such +out-of-distribution (OOD) scenarios, we simulate such distribution shifts by +deliberately withholding specific instances for testing, as from the social +media domain or the topic Solar Energy. + Unlike prior studies focusing on specific shifts and metrics in isolation, we +comprehensively analyze OOD generalization. We define three metrics to pinpoint +generalization flaws and propose eleven classification tasks covering topic, +domain, and language shifts. Overall, we find superior performance of +prompt-based fine-tuning, notably when train and test splits primarily differ +semantically. Simultaneously, in-context learning is more effective than +prompt-based or vanilla fine-tuning for tasks when training data embodies heavy +discrepancies in label distribution compared to testing data. This reveals a +crucial drawback of gradient-based learning: it biases LMs regarding such +structural obstacles. +" +Neural Machine Translation Models Can Learn to be Few-shot Learners,Raphael Reinauer,http://arxiv.org/pdf/2309.08590v1.pdf,2023-09-15,['cs.cl'],2309.08590v1.pdf," The emergent ability of Large Language Models to use a small number of +examples to learn to perform in novel domains and tasks, also called in-context +learning (ICL). In this work, we show that a much smaller model can be trained +to perform ICL by fine-tuning towards a specialized training objective, +exemplified on the task of domain adaptation for neural machine translation. +With this capacity for ICL, the model can take advantage of relevant few-shot +examples to adapt its output towards the domain. We compare the quality of this +domain adaptation to traditional supervised techniques and ICL with a +40B-parameter Large Language Model. Our approach allows efficient batch +inference on a mix of domains and outperforms state-of-the-art baselines in +terms of both translation quality and immediate adaptation rate, i.e. the +ability to reproduce a specific term after being shown a single example. +" +Few-Shot Adaptation for Parsing Contextual Utterances with LLMs,Kevin Lin,http://arxiv.org/pdf/2309.10168v1.pdf,2023-09-18,['cs.cl'],2309.10168v1.pdf," We evaluate the ability of semantic parsers based on large language models +(LLMs) to handle contextual utterances. In real-world settings, there typically +exists only a limited number of annotated contextual utterances due to +annotation cost, resulting in an imbalance compared to non-contextual +utterances. Therefore, parsers must adapt to contextual utterances with a few +training examples. We examine four major paradigms for doing so in +conversational semantic parsing i.e., Parse-with-Utterance-History, +Parse-with-Reference-Program, Parse-then-Resolve, and Rewrite-then-Parse. To +facilitate such cross-paradigm comparisons, we construct +SMCalFlow-EventQueries, a subset of contextual examples from SMCalFlow with +additional annotations. Experiments with in-context learning and fine-tuning +suggest that Rewrite-then-Parse is the most promising paradigm when +holistically considering parsing accuracy, annotation cost, and error types. +" +Toward Unified Controllable Text Generation via Regular Expression Instruction,Xin Zheng,http://arxiv.org/pdf/2309.10447v2.pdf,2023-09-19,"['cs.cl', 'cs.ai']",2309.10447v2.pdf," Controllable text generation is a fundamental aspect of natural language +generation, with numerous methods proposed for different constraint types. +However, these approaches often require significant architectural or decoding +modifications, making them challenging to apply to additional constraints or +resolve different constraint combinations. To address this, our paper +introduces Regular Expression Instruction (REI), which utilizes an +instruction-based mechanism to fully exploit regular expressions' advantages to +uniformly model diverse constraints. Specifically, our REI supports all popular +fine-grained controllable generation constraints, i.e., lexical, positional, +and length, as well as their complex combinations, via regular expression-style +instructions. Our method only requires fine-tuning on medium-scale language +models or few-shot, in-context learning on large language models, and requires +no further adjustment when applied to various constraint combinations. +Experiments demonstrate that our straightforward approach yields high success +rates and adaptability to various constraints while maintaining competitiveness +in automatic metrics and outperforming most previous baselines. +" +Language Modeling Is Compression,Grégoire Delétang,http://arxiv.org/pdf/2309.10668v1.pdf,2023-09-19,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.it', 'math.it']",2309.10668v1.pdf," It has long been established that predictive models can be transformed into +lossless compressors and vice versa. Incidentally, in recent years, the machine +learning community has focused on training increasingly large and powerful +self-supervised (language) models. Since these large language models exhibit +impressive predictive capabilities, they are well-positioned to be strong +compressors. In this work, we advocate for viewing the prediction problem +through the lens of compression and evaluate the compression capabilities of +large (foundation) models. We show that large language models are powerful +general-purpose predictors and that the compression viewpoint provides novel +insights into scaling laws, tokenization, and in-context learning. For example, +Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to +43.4% and LibriSpeech samples to 16.4% of their raw size, beating +domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. +Finally, we show that the prediction-compression equivalence allows us to use +any compressor (like gzip) to build a conditional generative model. +" +Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation,Hyelin Nam,http://arxiv.org/pdf/2309.11127v1.pdf,2023-09-20,"['eess.sp', 'cs.ai', 'cs.cl']",2309.11127v1.pdf," By integrating recent advances in large language models (LLMs) and generative +models into the emerging semantic communication (SC) paradigm, in this article +we put forward to a novel framework of language-oriented semantic communication +(LSC). In LSC, machines communicate using human language messages that can be +interpreted and manipulated via natural language processing (NLP) techniques +for SC efficiency. To demonstrate LSC's potential, we introduce three +innovative algorithms: 1) semantic source coding (SSC) which compresses a text +prompt into its key head words capturing the prompt's syntactic essence while +maintaining their appearance order to keep the prompt's context; 2) semantic +channel coding (SCC) that improves robustness against errors by substituting +head words with their lenghthier synonyms; and 3) semantic knowledge +distillation (SKD) that produces listener-customized prompts via in-context +learning the listener's language style. In a communication task for progressive +text-to-image generation, the proposed methods achieve higher perceptual +similarities with fewer transmissions while enhancing robustness in noisy +communication channels. +" +Towards Effective Disambiguation for Machine Translation with Large Language Models,Vivek Iyer,http://arxiv.org/pdf/2309.11668v2.pdf,2023-09-20,['cs.cl'],2309.11668v2.pdf," Resolving semantic ambiguity has long been recognised as a central challenge +in the field of Machine Translation. Recent work on benchmarking translation +performance on ambiguous sentences has exposed the limitations of conventional +Neural Machine Translation (NMT) systems, which fail to handle many such cases. +Large language models (LLMs) have emerged as a promising alternative, +demonstrating comparable performance to traditional NMT models while +introducing new paradigms for controlling the target outputs. In this paper, we +study the capabilities of LLMs to translate ""ambiguous sentences"" - i.e. those +containing highly polysemous words and/or rare word senses. We also propose two +ways to improve their disambiguation capabilities, through a) in-context +learning and b) fine-tuning on carefully curated ambiguous datasets. +Experiments show that our methods can match or outperform state-of-the-art +systems such as DeepL and NLLB in four out of five language directions. Our +research provides valuable insights into effectively adapting LLMs to become +better disambiguators during Machine Translation. We release our curated +disambiguation corpora and resources at +https://data.statmt.org/ambiguous-europarl. +" +In-context Interference in Chat-based Large Language Models,Eric Nuertey Coleman,http://arxiv.org/pdf/2309.12727v1.pdf,2023-09-22,"['cs.ai', 'cs.cl']",2309.12727v1.pdf," Large language models (LLMs) have had a huge impact on society due to their +impressive capabilities and vast knowledge of the world. Various applications +and tools have been created that allow users to interact with these models in a +black-box scenario. However, one limitation of this scenario is that users +cannot modify the internal knowledge of the model, and the only way to add or +modify internal knowledge is by explicitly mentioning it to the model during +the current interaction. This learning process is called in-context training, +and it refers to training that is confined to the user's current session or +context. In-context learning has significant applications, but also has +limitations that are seldom studied. In this paper, we present a study that +shows how the model can suffer from interference between information that +continually flows in the context, causing it to forget previously learned +knowledge, which can reduce the model's performance. Along with showing the +problem, we propose an evaluation benchmark based on the bAbI dataset. +" +Affect Recognition in Conversations Using Large Language Models,Shutong Feng,http://arxiv.org/pdf/2309.12881v1.pdf,2023-09-22,['cs.cl'],2309.12881v1.pdf," Affect recognition, encompassing emotions, moods, and feelings, plays a +pivotal role in human communication. In the realm of conversational artificial +intelligence (AI), the ability to discern and respond to human affective cues +is a critical factor for creating engaging and empathetic interactions. This +study delves into the capacity of large language models (LLMs) to recognise +human affect in conversations, with a focus on both open-domain chit-chat +dialogues and task-oriented dialogues. Leveraging three diverse datasets, +namely IEMOCAP, EmoWOZ, and DAIC-WOZ, covering a spectrum of dialogues from +casual conversations to clinical interviews, we evaluated and compared LLMs' +performance in affect recognition. Our investigation explores the zero-shot and +few-shot capabilities of LLMs through in-context learning (ICL) as well as +their model capacities through task-specific fine-tuning. Additionally, this +study takes into account the potential impact of automatic speech recognition +(ASR) errors on LLM predictions. With this work, we aim to shed light on the +extent to which LLMs can replicate human-like affect recognition capabilities +in conversations. +" +Calibrating LLM-Based Evaluator,Yuxuan Liu,http://arxiv.org/pdf/2309.13308v1.pdf,2023-09-23,['cs.cl'],2309.13308v1.pdf," Recent advancements in large language models (LLMs) on language modeling and +emergent capabilities make them a promising reference-free evaluator of natural +language generation quality, and a competent alternative to human evaluation. +However, hindered by the closed-source or high computational demand to host and +tune, there is a lack of practice to further calibrate an off-the-shelf +LLM-based evaluator towards better human alignment. In this work, we propose +AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate +and align an LLM-based evaluator toward human preference. Instead of explicitly +modeling human preferences, we first implicitly encompass them within a set of +human labels. Then, an initial set of scoring criteria is drafted by the +language model itself, leveraging in-context learning on different few-shot +examples. To further calibrate this set of criteria, we select the best +performers and re-draft them with self-refinement. Our experiments on multiple +text quality evaluation datasets illustrate a significant improvement in +correlation with expert evaluation through calibration. Our comprehensive +qualitative analysis conveys insightful intuitions and observations on the +essence of effective scoring criteria. +" +MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases,Yucheng Shi,http://arxiv.org/pdf/2309.16035v1.pdf,2023-09-27,"['cs.cl', 'cs.ai']",2309.16035v1.pdf," Large Language Models (LLMs), although powerful in general domains, often +perform poorly on domain-specific tasks like medical question answering (QA). +Moreover, they tend to function as ""black-boxes,"" making it challenging to +modify their behavior. Addressing this, our study delves into model editing +utilizing in-context learning, aiming to improve LLM responses without the need +for fine-tuning or retraining. Specifically, we propose a comprehensive +retrieval strategy to extract medical facts from an external knowledge base, +and then we incorporate them into the query prompt for the LLM. Focusing on +medical QA using the MedQA-SMILE dataset, we evaluate the impact of different +retrieval models and the number of facts provided to the LLM. Notably, our +edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%. +This work underscores the potential of model editing to enhance LLM +performance, offering a practical approach to mitigate the challenges of +black-box LLMs. +" +A Prefrontal Cortex-inspired Architecture for Planning in Large Language Models,Taylor Webb,http://arxiv.org/pdf/2310.00194v1.pdf,2023-09-30,"['cs.ai', 'cs.ne']",2310.00194v1.pdf," Large language models (LLMs) demonstrate impressive performance on a wide +variety of tasks, but they often struggle with tasks that require multi-step +reasoning or goal-directed planning. To address this, we take inspiration from +the human brain, in which planning is accomplished via the recurrent +interaction of specialized modules in the prefrontal cortex (PFC). These +modules perform functions such as conflict monitoring, state prediction, state +evaluation, task decomposition, and task coordination. We find that LLMs are +sometimes capable of carrying out these functions in isolation, but struggle to +autonomously coordinate them in the service of a goal. Therefore, we propose a +black box architecture with multiple LLM-based (GPT-4) modules. The +architecture improves planning through the interaction of specialized +PFC-inspired modules that break down a larger problem into multiple brief +automated calls to the LLM. We evaluate the combined architecture on two +challenging planning tasks -- graph traversal and Tower of Hanoi -- finding +that it yields significant improvements over standard LLM methods (e.g., +zero-shot prompting or in-context learning). These results demonstrate the +benefit of utilizing knowledge from cognitive neuroscience to improve planning +in LLMs. +" +Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method,Xuan Zhang,http://arxiv.org/pdf/2310.00305v1.pdf,2023-09-30,['cs.cl'],2310.00305v1.pdf," While large pre-trained language models (LLMs) have shown their impressive +capabilities in various NLP tasks, they are still under-explored in the +misinformation domain. In this paper, we examine LLMs with in-context learning +(ICL) for news claim verification, and find that only with 4-shot demonstration +examples, the performance of several prompting methods can be comparable with +previous supervised models. To further boost performance, we introduce a +Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to +separate a claim into several subclaims and then verify each of them via +multiple questions-answering steps progressively. Experiment results on two +public misinformation datasets show that HiSS prompting outperforms +state-of-the-art fully-supervised approach and strong few-shot ICL-enabled +baselines. +" +Text Data Augmentation in Low-Resource Settings via Fine-Tuning of Large Language Models,Jean Kaddour,http://arxiv.org/pdf/2310.01119v1.pdf,2023-10-02,"['cs.cl', 'cs.lg']",2310.01119v1.pdf," The in-context learning ability of large language models (LLMs) enables them +to generalize to novel downstream tasks with relatively few labeled examples. +However, they require enormous computational resources to be deployed. +Alternatively, smaller models can solve specific tasks if fine-tuned with +enough labeled examples. These examples, however, are expensive to obtain. In +pursuit of the best of both worlds, we study the annotation and generation of +fine-tuning training data via fine-tuned teacher LLMs to improve the downstream +performance of much smaller models. In four text classification and two text +generation tasks, we find that both data generation and annotation dramatically +improve the respective downstream model's performance, occasionally +necessitating only a minor fraction of the original training dataset. +" +Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations,Yongshuo Zong,http://arxiv.org/pdf/2310.01651v1.pdf,2023-10-02,['cs.lg'],2310.01651v1.pdf," Large language and vision-language models are rapidly being deployed in +practice thanks to their impressive capabilities in instruction following, +in-context learning, and so on. This raises an urgent need to carefully analyse +their robustness so that stakeholders can understand if and when such models +are trustworthy enough to be relied upon in any given application. In this +paper, we highlight a specific vulnerability in popular models, namely +permutation sensitivity in multiple-choice question answering (MCQA). +Specifically, we show empirically that popular models are vulnerable to +adversarial permutation in answer sets for multiple-choice prompting, which is +surprising as models should ideally be as invariant to prompt permutation as +humans are. These vulnerabilities persist across various model sizes, and exist +in very recent language and vision-language models. Code is available at +\url{https://github.com/ys-zong/FoolyourVLLMs}. +" +Improving Automatic VQA Evaluation Using Large Language Models,Oscar Mañas,http://arxiv.org/pdf/2310.02567v1.pdf,2023-10-04,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2310.02567v1.pdf," 8 years after the visual question answering (VQA) task was proposed, accuracy +remains the primary metric for automatic evaluation. VQA Accuracy has been +effective so far in the IID evaluation setting. However, our community is +undergoing a shift towards open-ended generative models and OOD evaluation. In +this new paradigm, the existing VQA Accuracy metric is overly stringent and +underestimates the performance of VQA systems. Thus, there is a need to develop +more robust automatic VQA metrics that serve as a proxy for human judgment. In +this work, we propose to leverage the in-context learning capabilities of +instruction-tuned large language models (LLMs) to build a better VQA metric. We +formulate VQA evaluation as an answer-rating task where the LLM is instructed +to score the accuracy of a candidate answer given a set of reference answers. +We demonstrate the proposed metric better correlates with human judgment +compared to existing metrics across several VQA models and benchmarks. We hope +wide adoption of our metric will contribute to better estimating the research +progress on the VQA task. +" +A Language-Agent Approach to Formal Theorem-Proving,Amitayush Thakur,http://arxiv.org/pdf/2310.04353v1.pdf,2023-10-06,"['cs.lg', 'cs.ai', 'cs.lo', 'cs.pl']",2310.04353v1.pdf," Language agents, which use a large language model (LLM) capable of in-context +learning to interact with an external environment, have recently emerged as a +promising approach to control tasks. We present the first language-agent +approach to formal theorem-proving. Our method, COPRA, uses a high-capacity, +black-box LLM (GPT-4) as part of a policy for a stateful backtracking search. +During the search, the policy can select proof tactics and retrieve lemmas and +definitions from an external database. Each selected tactic is executed in the +underlying proof framework, and the execution feedback is used to build the +prompt for the next policy invocation. The search also tracks selected +information from its history and uses it to reduce hallucinations and +unnecessary LLM queries. + We evaluate COPRA on the miniF2F benchmark for Lean and a set of Coq tasks +from the Compcert project. On these benchmarks, COPRA is significantly better +than one-shot invocations of GPT-4, as well as state-of-the-art models +fine-tuned on proof data, at finding correct proofs quickly. +" +Guideline Learning for In-context Information Extraction,Chaoxu Pang,http://arxiv.org/pdf/2310.05066v2.pdf,2023-10-08,"['cs.cl', 'cs.lg']",2310.05066v2.pdf," Large language models (LLMs) can perform a new task by merely conditioning on +task instructions and a few input-output examples, without optimizing any +parameters. This is called In-Context Learning (ICL). In-context Information +Extraction (IE) has recently garnered attention in the research community. +However, the performance of In-context IE generally lags behind the +state-of-the-art supervised expert models. We highlight a key reason for this +shortfall: underspecified task description. The limited-length context +struggles to thoroughly express the intricate IE task instructions and various +edge cases, leading to misalignment in task comprehension with humans. In this +paper, we propose a Guideline Learning (GL) framework for In-context IE which +reflectively learns and follows guidelines. During the learning phrase, GL +automatically synthesizes a set of guidelines based on a few error cases, and +during inference, GL retrieves helpful guidelines for better ICL. Moreover, we +propose a self-consistency-based active learning method to enhance the +efficiency of GL. Experiments on event extraction and relation extraction show +that GL can significantly improve the performance of in-context IE. +" +Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements,Yushan Qian,http://arxiv.org/pdf/2310.05140v1.pdf,2023-10-08,"['cs.cl', 'cs.ai']",2310.05140v1.pdf," Empathetic dialogue is an indispensable part of building harmonious social +relationships and contributes to the development of a helpful AI. Previous +approaches are mainly based on fine small-scale language models. With the +advent of ChatGPT, the application effect of large language models (LLMs) in +this field has attracted great attention. This work empirically investigates +the performance of LLMs in generating empathetic responses and proposes three +improvement methods of semantically similar in-context learning, two-stage +interactive generation, and combination with the knowledge base. Extensive +experiments show that LLMs can significantly benefit from our proposed methods +and is able to achieve state-of-the-art performance in both automatic and human +evaluations. Additionally, we explore the possibility of GPT-4 simulating human +evaluators. +" +LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models,Huiqiang Jiang,http://arxiv.org/pdf/2310.05736v1.pdf,2023-10-09,"['cs.cl', 'cs.lg']",2310.05736v1.pdf," Large language models (LLMs) have been applied in various applications due to +their astonishing capabilities. With advancements in technologies such as +chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed +to LLMs are becoming increasingly lengthy, even exceeding tens of thousands of +tokens. To accelerate model inference and reduce cost, this paper presents +LLMLingua, a coarse-to-fine prompt compression method that involves a budget +controller to maintain semantic integrity under high compression ratios, a +token-level iterative compression algorithm to better model the interdependence +between compressed contents, and an instruction tuning based method for +distribution alignment between language models. We conduct experiments and +analysis over four datasets from different scenarios, i.e., GSM8K, BBH, +ShareGPT, and Arxiv-March23; showing that the proposed approach yields +state-of-the-art performance and allows for up to 20x compression with little +performance loss. Our code is available at https://aka.ms/LLMLingua. +" +Selective Demonstrations for Cross-domain Text-to-SQL,Shuaichen Chang,http://arxiv.org/pdf/2310.06302v1.pdf,2023-10-10,['cs.cl'],2310.06302v1.pdf," Large language models (LLMs) with in-context learning have demonstrated +impressive generalization capabilities in the cross-domain text-to-SQL task, +without the use of in-domain annotations. However, incorporating in-domain +demonstration examples has been found to greatly enhance LLMs' performance. In +this paper, we delve into the key factors within in-domain examples that +contribute to the improvement and explore whether we can harness these benefits +without relying on in-domain annotations. Based on our findings, we propose a +demonstration selection framework ODIS which utilizes both out-of-domain +examples and synthetically generated in-domain examples to construct +demonstrations. By retrieving demonstrations from hybrid sources, ODIS +leverages the advantages of both, showcasing its effectiveness compared to +baseline methods that rely on a single data source. Furthermore, ODIS +outperforms state-of-the-art approaches on two cross-domain text-to-SQL +datasets, with improvements of 1.1 and 11.8 points in execution accuracy, +respectively. +" +Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations,Zeming Wei,http://arxiv.org/pdf/2310.06387v1.pdf,2023-10-10,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cr']",2310.06387v1.pdf," Large Language Models (LLMs) have shown remarkable success in various tasks, +but concerns about their safety and the potential for generating malicious +content have emerged. In this paper, we explore the power of In-Context +Learning (ICL) in manipulating the alignment ability of LLMs. We find that by +providing just few in-context demonstrations without fine-tuning, LLMs can be +manipulated to increase or decrease the probability of jailbreaking, i.e. +answering malicious prompts. Based on these observations, we propose In-Context +Attack (ICA) and In-Context Defense (ICD) methods for jailbreaking and guarding +aligned language model purposes. ICA crafts malicious contexts to guide models +in generating harmful outputs, while ICD enhances model robustness by +demonstrations of rejecting to answer harmful prompts. Our experiments show the +effectiveness of ICA and ICD in increasing or reducing the success rate of +adversarial jailbreaking attacks. Overall, we shed light on the potential of +ICL to influence LLM behavior and provide a new perspective for enhancing the +safety and alignment of LLMs. +" +Humans and language models diverge when predicting repeating text,Aditya R. Vaidya,http://arxiv.org/pdf/2310.06408v2.pdf,2023-10-10,['cs.cl'],2310.06408v2.pdf," Language models that are trained on the next-word prediction task have been +shown to accurately model human behavior in word prediction and reading speed. +In contrast with these findings, we present a scenario in which the performance +of humans and LMs diverges. We collected a dataset of human next-word +predictions for five stimuli that are formed by repeating spans of text. Human +and GPT-2 LM predictions are strongly aligned in the first presentation of a +text span, but their performance quickly diverges when memory (or in-context +learning) begins to play a role. We traced the cause of this divergence to +specific attention heads in a middle layer. Adding a power-law recency bias to +these attention heads yielded a model that performs much more similarly to +humans. We hope that this scenario will spur future work in bringing LMs closer +to human behavior. +" +The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment Quadruples: A Comparative Analysis,Xiancai Xu,http://arxiv.org/pdf/2310.06502v1.pdf,2023-10-10,['cs.cl'],2310.06502v1.pdf," Recently, ChatGPT has attracted great attention from both industry and +academia due to its surprising abilities in natural language understanding and +generation. We are particularly curious about whether it can achieve promising +performance on one of the most complex tasks in aspect-based sentiment +analysis, i.e., extracting aspect-category-opinion-sentiment quadruples from +texts. To this end, in this paper we develop a specialized prompt template that +enables ChatGPT to effectively tackle this complex quadruple extraction task. +Further, we propose a selection method on few-shot examples to fully exploit +the in-context learning ability of ChatGPT and uplift its effectiveness on this +complex task. Finally, we provide a comparative evaluation on ChatGPT against +existing state-of-the-art quadruple extraction models based on four public +datasets and highlight some important findings regarding the capability +boundaries of ChatGPT in the quadruple extraction. +" +AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents,Jake Grigsby,http://arxiv.org/pdf/2310.09971v2.pdf,2023-10-15,['cs.lg'],2310.09971v2.pdf," We introduce AMAGO, an in-context Reinforcement Learning (RL) agent that uses +sequence models to tackle the challenges of generalization, long-term memory, +and meta-learning. Recent works have shown that off-policy learning can make +in-context RL with recurrent policies viable. Nonetheless, these approaches +require extensive tuning and limit scalability by creating key bottlenecks in +agents' memory capacity, planning horizon, and model size. AMAGO revisits and +redesigns the off-policy in-context approach to successfully train +long-sequence Transformers over entire rollouts in parallel with end-to-end RL. +Our agent is uniquely scalable and applicable to a wide range of problems. We +demonstrate its strong performance empirically in meta-RL and long-term memory +domains. AMAGO's focus on sparse rewards and off-policy data also allows +in-context learning to extend to goal-conditioned problems with challenging +exploration. When combined with a novel hindsight relabeling scheme, AMAGO can +solve a previously difficult category of open-world domains, where agents +complete many possible instructions in procedurally generated environments. We +evaluate our agent on three goal-conditioned domains and study how its +individual improvements connect to create a generalist policy. +" +A Search for Prompts: Generating Structured Answers from Contracts,Adam Roegiest,http://arxiv.org/pdf/2310.10141v1.pdf,2023-10-16,['cs.cv'],2310.10141v1.pdf," In many legal processes being able to action on the concrete implication of a +legal question can be valuable to automating human review or signalling certain +conditions (e.g., alerts around automatic renewal). To support such tasks, we +present a form of legal question answering that seeks to return one (or more) +fixed answers for a question about a contract clause. After showing that +unstructured generative question answering can have questionable outcomes for +such a task, we discuss our exploration methodology for legal question +answering prompts using OpenAI's \textit{GPT-3.5-Turbo} and provide a summary +of insights. + Using insights gleaned from our qualitative experiences, we compare our +proposed template prompts against a common semantic matching approach and find +that our prompt templates are far more accurate despite being less reliable in +the exact response return. With some additional tweaks to prompts and the use +of in-context learning, we are able to further improve the performance of our +proposed strategy while maximizing the reliability of responses as best we can. +" +Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT,Xiaoshuai Song,http://arxiv.org/pdf/2310.10176v1.pdf,2023-10-16,"['cs.cl', 'cs.ai', 'cs.lg']",2310.10176v1.pdf," The tasks of out-of-domain (OOD) intent discovery and generalized intent +discovery (GID) aim to extend a closed intent classifier to open-world intent +sets, which is crucial to task-oriented dialogue (TOD) systems. Previous +methods address them by fine-tuning discriminative models. Recently, although +some studies have been exploring the application of large language models +(LLMs) represented by ChatGPT to various downstream tasks, it is still unclear +for the ability of ChatGPT to discover and incrementally extent OOD intents. In +this paper, we comprehensively evaluate ChatGPT on OOD intent discovery and +GID, and then outline the strengths and weaknesses of ChatGPT. Overall, ChatGPT +exhibits consistent advantages under zero-shot settings, but is still at a +disadvantage compared to fine-tuned models. More deeply, through a series of +analytical experiments, we summarize and discuss the challenges faced by LLMs +including clustering, domain-specific understanding, and cross-domain +in-context learning scenarios. Finally, we provide empirical guidance for +future directions to address these challenges. +" +MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations,Heyuan Yao,http://arxiv.org/pdf/2310.10198v2.pdf,2023-10-16,"['cs.cv', 'cs.gr']",2310.10198v2.pdf," In this work, we present MoConVQ, a novel unified framework for physics-based +motion control leveraging scalable discrete representations. Building upon +vector quantized variational autoencoders (VQ-VAE) and model-based +reinforcement learning, our approach effectively learns motion embeddings from +a large, unstructured dataset spanning tens of hours of motion examples. The +resultant motion representation not only captures diverse motion skills but +also offers a robust and intuitive interface for various applications. We +demonstrate the versatility of MoConVQ through several applications: universal +tracking control from various motion sources, interactive character control +with latent motion representations using supervised learning, physics-based +motion generation from natural language descriptions using the GPT framework, +and, most interestingly, seamless integration with large language models (LLMs) +with in-context learning to tackle complex and abstract tasks. +" +Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking,Yuxiang Wu,http://arxiv.org/pdf/2310.10520v2.pdf,2023-10-16,"['cs.cl', 'cs.ai', 'cs.lg']",2310.10520v2.pdf," Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring +and annotating task-oriented dialogues, which can be time consuming and costly. +However, DST extends beyond simple slot-filling and requires effective updating +strategies for tracking dialogue state as conversations progress. In this +paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to +introduce additional intricate updating strategies in zero-shot DST. Our +approach reformulates the DST task by leveraging powerful Large Language Models +(LLMs) and translating the original dialogue text to JSON through semantic +parsing as an intermediate state. We also design a novel framework that +includes more modules to ensure the effectiveness of updating strategies in the +text-to-JSON process. Experimental results demonstrate that our approach +outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant +improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to +existing ICL methods. +" +Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment,Ji Qi,http://arxiv.org/pdf/2310.10590v1.pdf,2023-10-16,['cs.cl'],2310.10590v1.pdf," Open Information Extraction (OIE) aims to extract objective structured +knowledge from natural texts, which has attracted growing attention to build +dedicated models with human experience. As the large language models (LLMs) +have exhibited remarkable in-context learning capabilities, a question arises +as to whether the task of OIE can be effectively tackled with this paradigm? In +this paper, we explore solving the OIE problem by constructing an appropriate +reasoning environment for LLMs. Specifically, we first propose a method to +effectively estimate the discrepancy of syntactic distribution between a LLM +and test samples, which can serve as correlation evidence for preparing +positive demonstrations. Upon the evidence, we introduce a simple yet effective +mechanism to establish the reasoning environment for LLMs on specific tasks. +Without bells and whistles, experimental results on the standard CaRB benchmark +demonstrate that our $6$-shot approach outperforms state-of-the-art supervised +method, achieving an $55.3$ $F_1$ score. Further experiments on TACRED and +ACE05 show that our method can naturally generalize to other information +extraction tasks, resulting in improvements of $5.7$ and $6.8$ $F_1$ scores, +respectively. +" +Exploring Automatic Evaluation Methods based on a Decoder-based LLM for Text Generation,Tomohito Kasahara,http://arxiv.org/pdf/2310.11026v1.pdf,2023-10-17,['cs.cl'],2310.11026v1.pdf," Automatic evaluation of text generation is essential for improving the +accuracy of generation tasks. In light of the current trend towards +increasingly larger decoder-based language models, we investigate automatic +evaluation methods based on such models for text generation. This paper +compares various methods, including tuning with encoder-based models and large +language models under equal conditions, on two different tasks, machine +translation evaluation and semantic textual similarity, in two languages, +Japanese and English. Experimental results show that compared to the tuned +encoder-based models, the tuned decoder-based models perform poorly. The +analysis of the causes for this suggests that the decoder-based models focus on +surface word sequences and do not capture meaning. It is also revealed that +in-context learning of very large decoder-based models such as ChatGPT makes it +difficult to identify fine-grained semantic differences. +" +Learning from Red Teaming: Gender Bias Provocation and Mitigation in Large Language Models,Hsuan Su,http://arxiv.org/pdf/2310.11079v1.pdf,2023-10-17,"['cs.cl', 'cs.ai']",2310.11079v1.pdf," Recently, researchers have made considerable improvements in dialogue systems +with the progress of large language models (LLMs) such as ChatGPT and GPT-4. +These LLM-based chatbots encode the potential biases while retaining +disparities that can harm humans during interactions. The traditional biases +investigation methods often rely on human-written test cases. However, these +test cases are usually expensive and limited. In this work, we propose a +first-of-its-kind method that automatically generates test cases to detect +LLMs' potential gender bias. We apply our method to three well-known LLMs and +find that the generated test cases effectively identify the presence of biases. +To address the biases identified, we propose a mitigation strategy that uses +the generated test cases as demonstrations for in-context learning to +circumvent the need for parameter fine-tuning. The experimental results show +that LLMs generate fairer responses with the proposed approach. +" +Evaluating LLMs for Privilege-Escalation Scenarios,Andreas Happe,http://arxiv.org/pdf/2310.11409v2.pdf,2023-10-17,"['cs.cr', 'cs.ai']",2310.11409v2.pdf," Penetration testing, an essential component of cybersecurity, allows +organizations to proactively identify and remediate vulnerabilities in their +systems, thus bolstering their defense mechanisms against potential +cyberattacks. One recent advancement in the realm of penetration testing is the +utilization of Language Models (LLMs). We explore the intersection of LLMs and +penetration testing to gain insight into their capabilities and challenges in +the context of privilige escalation. We create an automated Linux +privilege-escalation benchmark utilizing local virtual machines. We introduce +an LLM-guided privilege-escalation tool designed for evaluating different LLMs +and prompt strategies against our benchmark. We analyze the impact of different +prompt designs, the benefits of in-context learning, and the advantages of +offering high-level guidance to LLMs. We discuss challenging areas for LLMs, +including maintaining focus during testing, coping with errors, and finally +comparing them with both stochastic parrots as well as with human hackers. +" +Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly,Sheng Lu,http://arxiv.org/pdf/2310.12300v1.pdf,2023-10-18,['cs.cl'],2310.12300v1.pdf," In-context learning (ICL) is a new learning paradigm that has gained +popularity along with the development of large language models. In this work, +we adapt a recently proposed hardness metric, pointwise $\mathcal{V}$-usable +information (PVI), to an in-context version (in-context PVI). Compared to the +original PVI, in-context PVI is more efficient in that it requires only a few +exemplars and does not require fine-tuning. We conducted a comprehensive +empirical analysis to evaluate the reliability of in-context PVI. Our findings +indicate that in-context PVI estimates exhibit similar characteristics to the +original PVI. Specific to the in-context setting, we show that in-context PVI +estimates remain consistent across different exemplar selections and numbers of +shots. The variance of in-context PVI estimates across different exemplar +selections is insignificant, which suggests that in-context PVI are stable. +Furthermore, we demonstrate how in-context PVI can be employed to identify +challenging instances. Our work highlights the potential of in-context PVI and +provides new insights into the capabilities of ICL. +" +Attack Prompt Generation for Red Teaming and Defending Large Language Models,Boyi Deng,http://arxiv.org/pdf/2310.12505v1.pdf,2023-10-19,"['cs.cl', 'cs.cr', 'cs.lg']",2310.12505v1.pdf," Large language models (LLMs) are susceptible to red teaming attacks, which +can induce LLMs to generate harmful content. Previous research constructs +attack prompts via manual or automatic methods, which have their own +limitations on construction cost and quality. To address these issues, we +propose an integrated approach that combines manual and automatic methods to +economically generate high-quality attack prompts. Specifically, considering +the impressive capabilities of newly emerged LLMs, we propose an attack +framework to instruct LLMs to mimic human-generated prompts through in-context +learning. Furthermore, we propose a defense framework that fine-tunes victim +LLMs through iterative interactions with the attack framework to enhance their +safety against red teaming attacks. Extensive experiments on different LLMs +validate the effectiveness of our proposed attack and defense frameworks. +Additionally, we release a series of attack prompts datasets named SAP with +varying sizes, facilitating the safety evaluation and enhancement of more LLMs. +Our code and dataset is available on https://github.com/Aatrox103/SAP . +" +Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization,Ningyu Xu,http://arxiv.org/pdf/2310.12794v1.pdf,2023-10-19,['cs.cl'],2310.12794v1.pdf," Large language models (LLMs) have exhibited considerable cross-lingual +generalization abilities, whereby they implicitly transfer knowledge across +languages. However, the transfer is not equally successful for all languages, +especially for low-resource ones, which poses an ongoing challenge. It is +unclear whether we have reached the limits of implicit cross-lingual +generalization and if explicit knowledge transfer is viable. In this paper, we +investigate the potential for explicitly aligning conceptual correspondence +between languages to enhance cross-lingual generalization. Using the syntactic +aspect of language as a testbed, our analyses of 43 languages reveal a high +degree of alignability among the spaces of structural concepts within each +language for both encoder-only and decoder-only LLMs. We then propose a +meta-learning-based method to learn to align conceptual spaces of different +languages, which facilitates zero-shot and few-shot generalization in concept +classification and also offers insights into the cross-lingual in-context +learning phenomenon. Experiments on syntactic analysis tasks show that our +approach achieves competitive results with state-of-the-art methods and narrows +the performance gap between languages, particularly benefiting those with +limited resources. +" +Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning,Lucas Weber,http://arxiv.org/pdf/2310.13486v1.pdf,2023-10-20,"['cs.cl', 'cs.ai']",2310.13486v1.pdf," Finding the best way of adapting pre-trained language models to a task is a +big challenge in current NLP. Just like the previous generation of task-tuned +models (TT), models that are adapted to tasks via in-context-learning (ICL) are +robust in some setups but not in others. Here, we present a detailed analysis +of which design choices cause instabilities and inconsistencies in LLM +predictions. First, we show how spurious correlations between input +distributions and labels -- a known issue in TT models -- form only a minor +problem for prompted models. Then, we engage in a systematic, holistic +evaluation of different factors that have been found to influence predictions +in a prompting setup. We test all possible combinations of a range of factors +on both vanilla and instruction-tuned (IT) LLMs of different scale and +statistically analyse the results to show which factors are the most +influential, interactive or stable. Our results show which factors can be used +without precautions and which should be avoided or handled with care in most +settings. +" +A Simple Baseline for Knowledge-Based Visual Question Answering,Alexandros Xenos,http://arxiv.org/pdf/2310.13570v2.pdf,2023-10-20,['cs.cv'],2310.13570v2.pdf," This paper is on the problem of Knowledge-Based Visual Question Answering +(KB-VQA). Recent works have emphasized the significance of incorporating both +explicit (through external databases) and implicit (through LLMs) knowledge to +answer questions requiring external knowledge effectively. A common limitation +of such approaches is that they consist of relatively complicated pipelines and +often heavily rely on accessing GPT-3 API. Our main contribution in this paper +is to propose a much simpler and readily reproducible pipeline which, in a +nutshell, is based on efficient in-context learning by prompting LLaMA (1 and +2) using question-informative captions as contextual information. Contrary to +recent approaches, our method is training-free, does not require access to +external databases or APIs, and yet achieves state-of-the-art accuracy on the +OK-VQA and A-OK-VQA datasets. Finally, we perform several ablation studies to +understand important aspects of our method. Our code is publicly available at +https://github.com/alexandrosXe/ASimple-Baseline-For-Knowledge-Based-VQA +" +An In-Context Schema Understanding Method for Knowledge Base Question Answering,Yantao Liu,http://arxiv.org/pdf/2310.14174v1.pdf,2023-10-22,['cs.cl'],2310.14174v1.pdf," The Knowledge Base Question Answering (KBQA) task aims to answer natural +language questions based on a given knowledge base. As a kind of common method +for this task, semantic parsing-based ones first convert natural language +questions to logical forms (e.g., SPARQL queries) and then execute them on +knowledge bases to get answers. Recently, Large Language Models (LLMs) have +shown strong abilities in language understanding and may be adopted as semantic +parsers in such kinds of methods. However, in doing so, a great challenge for +LLMs is to understand the schema of knowledge bases. Therefore, in this paper, +we propose an In-Context Schema Understanding (ICSU) method for facilitating +LLMs to be used as a semantic parser in KBQA. Specifically, ICSU adopts the +In-context Learning mechanism to instruct LLMs to generate SPARQL queries with +examples. In order to retrieve appropriate examples from annotated +question-query pairs, which contain comprehensive schema information related to +questions, ICSU explores four different retrieval strategies. Experimental +results on the largest KBQA benchmark, KQA Pro, show that ICSU with all these +strategies outperforms that with a random retrieval strategy significantly +(from 12\% to 78.76\% in accuracy). +" +From Chaos to Clarity: Claim Normalization to Empower Fact-Checking,Megha Sundriyal,http://arxiv.org/pdf/2310.14338v1.pdf,2023-10-22,"['cs.cl', 'cs.ai']",2310.14338v1.pdf," With the proliferation of social media platforms, users are exposed to vast +information, including posts containing misleading claims. However, the +pervasive noise inherent in these posts presents a challenge in identifying +precise and prominent claims that require verification. Extracting the core +assertions from such posts is arduous and time-consuming. We introduce a novel +task called Claim Normalization (aka ClaimNorm) that aims to decompose complex +and noisy social media posts into more straightforward and understandable +forms, termed normalized claims. We propose CACN, a pioneering approach that +leverages chain-of-thought and claim check-worthiness estimation, mimicking +human reasoning processes, to comprehend intricate claims. Moreover, we +capitalize on large language models' powerful in-context learning abilities to +provide guidance and improve the claim normalization process. To evaluate the +effectiveness of our proposed model, we meticulously compile a comprehensive +real-world dataset, CLAN, comprising more than 6k instances of social media +posts alongside their respective normalized claims. Experimentation +demonstrates that CACN outperforms several baselines across various evaluation +measures. A rigorous error analysis validates CACN's capabilities and pitfalls. +" +Retrieval-Augmented Chain-of-Thought in Semi-structured Domains,Vaibhav Mavi,http://arxiv.org/pdf/2310.14435v1.pdf,2023-10-22,"['cs.cl', 'cs.ai']",2310.14435v1.pdf," Applying existing question answering (QA) systems to specialized domains like +law and finance presents challenges that necessitate domain expertise. Although +large language models (LLMs) have shown impressive language comprehension and +in-context learning capabilities, their inability to handle very long +inputs/contexts is well known. Tasks specific to these domains need significant +background knowledge, leading to contexts that can often exceed the maximum +length that existing LLMs can process. This study explores leveraging the +semi-structured nature of legal and financial data to efficiently retrieve +relevant context, enabling the use of LLMs for domain-specialized QA. The +resulting system outperforms contemporary models and also provides useful +explanations for the answers, encouraging the integration of LLMs into legal +and financial NLP systems for future research. +" +Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings,Parker Seegmiller,http://arxiv.org/pdf/2310.15010v1.pdf,2023-10-23,['cs.cl'],2310.15010v1.pdf," The popularity of transformer-based text embeddings calls for better +statistical tools for measuring distributions of such embeddings. One such tool +would be a method for ranking texts within a corpus by centrality, i.e. +assigning each text a number signifying how representative that text is of the +corpus as a whole. However, an intrinsic center-outward ordering of +high-dimensional text representations is not trivial. A statistical depth is a +function for ranking k-dimensional objects by measuring centrality with respect +to some observed k-dimensional distribution. We adopt a statistical depth to +measure distributions of transformer-based text embeddings, transformer-based +text embedding (TTE) depth, and introduce the practical use of this depth for +both modeling and distributional inference in NLP pipelines. We first define +TTE depth and an associated rank sum test for determining whether two corpora +differ significantly in embedding space. We then use TTE depth for the task of +in-context learning prompt selection, showing that this approach reliably +improves performance over statistical baseline approaches across six text +classification tasks. Finally, we use TTE depth and the associated rank sum +test to characterize the distributions of synthesized and human-generated +corpora, showing that five recent synthetic data augmentation processes cause a +measurable distributional shift away from associated human-generated text. +" +Meta- (out-of-context) learning in neural networks,Dmitrii Krasheninnikov,http://arxiv.org/pdf/2310.15047v2.pdf,2023-10-23,"['cs.lg', 'cs.ai']",2310.15047v2.pdf," Brown et al. (2020) famously introduced the phenomenon of in-context learning +in large language models (LLMs). We establish the existence of a phenomenon we +call meta-out-of-context learning (meta-OCL) via carefully designed synthetic +experiments with LLMs. Our results suggest that meta-OCL leads LLMs to more +readily ""internalize"" the semantic content of text that is, or appears to be, +broadly useful (such as true statements, or text from authoritative sources) +and use it in appropriate circumstances. We further demonstrate meta-OCL in a +synthetic computer vision setting, and propose two hypotheses for the emergence +of meta-OCL: one relying on the way models store knowledge in their parameters, +and another suggesting that the implicit gradient alignment bias of +gradient-descent-based optimizers may be responsible. Finally, we reflect on +what our results might imply about capabilities of future AI systems, and +discuss potential risks. Our code can be found at +https://github.com/krasheninnikov/internalization. +" +The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models,Xinyi Chen,http://arxiv.org/pdf/2310.15061v1.pdf,2023-10-23,"['cs.cl', 'cs.ai', 'cs.cv']",2310.15061v1.pdf," Despite the impressive performance achieved by pre-trained +language-and-vision models in downstream tasks, it remains an open question +whether this reflects a proper understanding of image-text interaction. In this +work, we explore to what extent they handle basic linguistic constructions -- +active-passive voice, coordination, and relative clauses -- that even preschool +children can typically master. We present BLA, a novel, automatically +constructed benchmark to evaluate multimodal models on these Basic Language +Abilities. We show that different types of Transformer-based systems, such as +CLIP, ViLBERT, and BLIP2, generally struggle with BLA in a zero-shot setting, +in line with previous findings. Our experiments, in particular, show that most +of the tested models only marginally benefit when fine-tuned or prompted with +construction-specific samples. Yet, the generative BLIP2 shows promising +trends, especially in an in-context learning setting. This opens the door to +using BLA not only as an evaluation benchmark but also to improve models' basic +language abilities. +" +LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis,Shih-Chieh Dai,http://arxiv.org/pdf/2310.15100v1.pdf,2023-10-23,['cs.cl'],2310.15100v1.pdf," Thematic analysis (TA) has been widely used for analyzing qualitative data in +many disciplines and fields. To ensure reliable analysis, the same piece of +data is typically assigned to at least two human coders. Moreover, to produce +meaningful and useful analysis, human coders develop and deepen their data +interpretation and coding over multiple iterations, making TA labor-intensive +and time-consuming. Recently the emerging field of large language models (LLMs) +research has shown that LLMs have the potential replicate human-like behavior +in various tasks: in particular, LLMs outperform crowd workers on +text-annotation tasks, suggesting an opportunity to leverage LLMs on TA. We +propose a human-LLM collaboration framework (i.e., LLM-in-the-loop) to conduct +TA with in-context learning (ICL). This framework provides the prompt to frame +discussions with a LLM (e.g., GPT-3.5) to generate the final codebook for TA. +We demonstrate the utility of this framework using survey datasets on the +aspects of the music listening experience and the usage of a password manager. +Results of the two case studies show that the proposed framework yields similar +coding quality to that of human coders but reduces TA's labor and time demands. +" +UI Layout Generation with LLMs Guided by UI Grammar,Yuwen Lu,http://arxiv.org/pdf/2310.15455v1.pdf,2023-10-24,"['cs.hc', 'cs.ai']",2310.15455v1.pdf," The recent advances in Large Language Models (LLMs) have stimulated interest +among researchers and industry professionals, particularly in their application +to tasks concerning mobile user interfaces (UIs). This position paper +investigates the use of LLMs for UI layout generation. Central to our +exploration is the introduction of UI grammar -- a novel approach we proposed +to represent the hierarchical structure inherent in UI screens. The aim of this +approach is to guide the generative capacities of LLMs more effectively and +improve the explainability and controllability of the process. Initial +experiments conducted with GPT-4 showed the promising capability of LLMs to +produce high-quality user interfaces via in-context learning. Furthermore, our +preliminary comparative study suggested the potential of the grammar-based +approach in improving the quality of generative results in specific aspects. +" +POE: Process of Elimination for Multiple Choice Reasoning,Chenkai Ma,http://arxiv.org/pdf/2310.15575v1.pdf,2023-10-24,['cs.cl'],2310.15575v1.pdf," Language models (LMs) are capable of conducting in-context learning for +multiple choice reasoning tasks, but the options in these tasks are treated +equally. As humans often first eliminate wrong options before picking the final +correct answer, we argue a similar two-step strategy can make LMs better at +these tasks. To this end, we present the Process of Elimination (POE), a +two-step scoring method. In the first step, POE scores each option, and +eliminates seemingly wrong options. In the second step, POE masks these wrong +options, and makes the final prediction from the remaining options. Zero-shot +experiments on 8 reasoning tasks illustrate the effectiveness of POE, and a +following analysis finds our method to be especially performant on logical +reasoning tasks. We further analyze the effect of masks, and show that POE +applies to few-shot settings and large language models (LLMs) like ChatGPT. +" +WebWISE: Web Interface Control and Sequential Exploration with Large Language Models,Heyi Tao,http://arxiv.org/pdf/2310.16042v2.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.16042v2.pdf," The paper investigates using a Large Language Model (LLM) to automatically +perform web software tasks using click, scroll, and text input operations. +Previous approaches, such as reinforcement learning (RL) or imitation learning, +are inefficient to train and task-specific. Our method uses filtered Document +Object Model (DOM) elements as observations and performs tasks step-by-step, +sequentially generating small programs based on the current observations. We +use in-context learning, either benefiting from a single manually provided +example, or an automatically generated example based on a successful zero-shot +trial. We evaluate the proposed method on the MiniWob++ benchmark. With only +one in-context example, our WebWISE method achieves similar or better +performance than other methods that require many demonstrations or trials. +" +From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning,Zheyuan Zhang,http://arxiv.org/pdf/2310.18364v1.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.18364v1.pdf," Pre-trained language models (PLMs) have shown impressive performance in +various language tasks. However, they are prone to spurious correlations, and +often generate illusory information. In real-world applications, PLMs should +justify decisions with formalized, coherent reasoning chains, but this +challenge remains under-explored. Cognitive psychology theorizes that humans +are capable of utilizing fast and intuitive heuristic thinking to make +decisions based on past experience, then rationalizing the decisions through +slower and deliberative analytic reasoning. We incorporate these interlinked +dual processes in fine-tuning and in-context learning with PLMs, applying them +to two language understanding tasks that require coherent physical commonsense +reasoning. We show that our proposed Heuristic-Analytic Reasoning (HAR) +strategies drastically improve the coherence of rationalizations for model +decisions, yielding state-of-the-art results on Tiered Reasoning for Intuitive +Physics (TRIP). We also find that this improved coherence is a direct result of +more faithful attention to relevant language context in each step of reasoning. +Our findings suggest that human-like reasoning strategies can effectively +improve the coherence and reliability of PLM reasoning. +" +The Mystery and Fascination of LLMs: A Comprehensive Survey on the Interpretation and Analysis of Emergent Abilities,Yuxiang Zhou,http://arxiv.org/pdf/2311.00237v1.pdf,2023-11-01,['cs.cl'],2311.00237v1.pdf," Understanding emergent abilities, such as in-context learning (ICL) and +chain-of-thought (CoT) prompting in large language models (LLMs), is of utmost +importance. This importance stems not only from the better utilization of these +capabilities across various tasks, but also from the proactive identification +and mitigation of potential risks, including concerns of truthfulness, bias, +and toxicity, that may arise alongside these capabilities. In this paper, we +present a thorough survey on the interpretation and analysis of emergent +abilities of LLMs. First, we provide a concise introduction to the background +and definition of emergent abilities. Then, we give an overview of advancements +from two perspectives: 1) a macro perspective, emphasizing studies on the +mechanistic interpretability and delving into the mathematical foundations +behind emergent abilities; and 2) a micro-perspective, concerning studies that +focus on empirical interpretability by examining factors associated with these +abilities. We conclude by highlighting the challenges encountered and +suggesting potential avenues for future research. We believe that our work +establishes the basis for further exploration into the interpretation of +emergent abilities. +" +Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles,Weiting Tan,http://arxiv.org/pdf/2311.02310v1.pdf,2023-11-04,['cs.cl'],2311.02310v1.pdf," Large language models trained primarily in a monolingual setting have +demonstrated their ability to generalize to machine translation using zero- and +few-shot examples with in-context learning. However, even though zero-shot +translations are relatively good, there remains a discernible gap comparing +their performance with the few-shot setting. In this paper, we investigate the +factors contributing to this gap and find that this gap can largely be closed +(for about 70%) by matching the writing styles of the target corpus. +Additionally, we explore potential approaches to enhance zero-shot baselines +without the need for parallel demonstration examples, providing valuable +insights into how these methods contribute to improving translation metrics. +" +Instructed Language Models with Retrievers Are Powerful Entity Linkers,Zilin Xiao,http://arxiv.org/pdf/2311.03250v1.pdf,2023-11-06,"['cs.cl', 'cs.ai']",2311.03250v1.pdf," Generative approaches powered by large language models (LLMs) have +demonstrated emergent abilities in tasks that require complex reasoning +abilities. Yet the generative nature still makes the generated content suffer +from hallucinations, thus unsuitable for entity-centric tasks like entity +linking (EL) requiring precise entity predictions over a large knowledge base. +We present Instructed Generative Entity Linker (INSGENEL), the first approach +that enables casual language models to perform entity linking over knowledge +bases. Several methods to equip language models with EL capability were +proposed in this work, including (i) a sequence-to-sequence training EL +objective with instruction-tuning, (ii) a novel generative EL framework based +on a light-weight potential mention retriever that frees the model from heavy +and non-parallelizable decoding, achieving 4$\times$ speedup without compromise +on linking metrics. INSGENEL outperforms previous generative alternatives with ++6.8 F1 points gain on average, also with a huge advantage in training data +efficiency and training compute consumption. In addition, our skillfully +engineered in-context learning (ICL) framework for EL still lags behind +INSGENEL significantly, reaffirming that the EL task remains a persistent +hurdle for general LLMs. +" +Meta-learning via Language Model In-context Tuning,Yanda Chen,http://arxiv.org/pdf/2110.07814v2.pdf,2021-10-15,"['cs.cl', 'cs.lg']",2110.07814v2.pdf," The goal of meta-learning is to learn to adapt to a new task with only a few +labeled examples. To tackle this problem in NLP, we propose $\textit{in-context +tuning}$, which recasts adaptation and prediction as a simple sequence +prediction problem: to form the input sequence, we concatenate the task +instruction, the labeled examples, and the target input to predict; to +meta-train the model to learn from in-context examples, we fine-tune a +pre-trained language model (LM) to predict the target label from the input +sequences on a collection of tasks. + We benchmark our method on two collections of text classification tasks: LAMA +and BinaryClfs. Compared to first-order MAML which adapts the model with +gradient descent, our method better leverages the inductive bias of LMs to +perform pattern matching, and outperforms MAML by an absolute $6\%$ AUC ROC +score on BinaryClfs, with increasing advantage w.r.t. model size. Compared to +non-fine-tuned in-context learning (i.e. prompting a raw LM), in-context tuning +directly learns to learn from in-context examples. On BinaryClfs, in-context +tuning improves the average AUC-ROC score by an absolute $10\%$, and reduces +the variance with respect to example ordering by 6x and example choices by 2x. +" +Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER,Dong-Ho Lee,http://arxiv.org/pdf/2110.08454v3.pdf,2021-10-16,['cs.cl'],2110.08454v3.pdf," Recent advances in prompt-based learning have shown strong results on +few-shot text classification by using cloze-style templates. Similar attempts +have been made on named entity recognition (NER) which manually design +templates to predict entity types for every text span in a sentence. However, +such methods may suffer from error propagation induced by entity span +detection, high cost due to enumeration of all possible text spans, and +omission of inter-dependencies among token labels in a sentence. Here we +present a simple demonstration-based learning method for NER, which lets the +input be prefaced by task demonstrations for in-context learning. We perform a +systematic study on demonstration strategy regarding what to include (entity +examples, with or without surrounding context), how to select the examples, and +what templates to use. Results on in-domain learning and domain adaptation show +that the model's performance in low-resource settings can be largely improved +with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train +instances). We also find that good demonstration can save many labeled examples +and consistency in demonstration contributes to better performance. +" +GLaM: Efficient Scaling of Language Models with Mixture-of-Experts,Nan Du,http://arxiv.org/pdf/2112.06905v2.pdf,2021-12-13,['cs.cl'],2112.06905v2.pdf," Scaling language models with more data, compute and parameters has driven +significant progress in natural language processing. For example, thanks to +scaling, GPT-3 was able to achieve strong results on in-context learning tasks. +However, training these large dense models requires significant amounts of +computing resources. In this paper, we propose and develop a family of language +models named GLaM (Generalist Language Model), which uses a sparsely activated +mixture-of-experts architecture to scale the model capacity while also +incurring substantially less training cost compared to dense variants. The +largest GLaM has 1.2 trillion parameters, which is approximately 7x larger than +GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half +of the computation flops for inference, while still achieving better overall +zero-shot and one-shot performance across 29 NLP tasks. +" +Can language models learn from explanations in context?,Andrew K. Lampinen,http://arxiv.org/pdf/2204.02329v4.pdf,2022-04-05,"['cs.cl', 'cs.ai', 'cs.lg']",2204.02329v4.pdf," Language Models (LMs) can perform new tasks by adapting to a few in-context +examples. For humans, explanations that connect examples to task principles can +improve learning. We therefore investigate whether explanations of few-shot +examples can help LMs. We annotate questions from 40 challenging tasks with +answer explanations, and various matched control explanations. We evaluate how +different types of explanations, instructions, and controls affect zero- and +few-shot performance. We analyze these results using statistical multilevel +modeling techniques that account for the nested dependencies among conditions, +tasks, prompts, and models. We find that explanations can improve performance +-- even without tuning. Furthermore, explanations hand-tuned for performance on +a small validation set offer substantially larger benefits, and building a +prompt by selecting examples and explanations together substantially improves +performance over selecting examples alone. Finally, even untuned explanations +outperform carefully matched controls, suggesting that the benefits are due to +the link between an example and its explanation, rather than lower-level +features. However, only large models benefit. In summary, explanations can +support the in-context learning of large LMs on challenging tasks. +" +Automatic Short Math Answer Grading via In-context Meta-learning,Mengxue Zhang,http://arxiv.org/pdf/2205.15219v3.pdf,2022-05-30,"['cs.cl', 'cs.lg']",2205.15219v3.pdf," Automatic short answer grading is an important research direction in the +exploration of how to use artificial intelligence (AI)-based tools to improve +education. Current state-of-the-art approaches use neural language models to +create vectorized representations of students responses, followed by +classifiers to predict the score. However, these approaches have several key +limitations, including i) they use pre-trained language models that are not +well-adapted to educational subject domains and/or student-generated text and +ii) they almost always train one model per question, ignoring the linkage +across a question and result in a significant model storage problem due to the +size of advanced language models. In this paper, we study the problem of +automatic short answer grading for students' responses to math questions and +propose a novel framework for this task. First, we use MathBERT, a variant of +the popular language model BERT adapted to mathematical content, as our base +model and fine-tune it for the downstream task of student response grading. +Second, we use an in-context learning approach that provides scoring examples +as input to the language model to provide additional context information and +promote generalization to previously unseen questions. We evaluate our +framework on a real-world dataset of student responses to open-ended math +questions and show that our framework (often significantly) outperforms +existing approaches, especially for new questions that are not seen during +training. +" +ThinkSum: Probabilistic reasoning over sets using large language models,Batu Ozturkler,http://arxiv.org/pdf/2210.01293v2.pdf,2022-10-04,['cs.cl'],2210.01293v2.pdf," Large language models (LLMs) have a substantial capacity for high-level +analogical reasoning: reproducing patterns in linear text that occur in their +training data (zero-shot evaluation) or in the provided context (few-shot +in-context learning). However, recent studies show that even the more advanced +LLMs fail in scenarios that require reasoning over multiple objects or facts +and making sequences of logical deductions. We propose a two-stage +probabilistic inference paradigm, ThinkSum, which reasons over sets of objects +or facts in a structured manner. In the first stage (Think - retrieval of +associations), a LLM is queried in parallel over a set of phrases extracted +from the prompt or an auxiliary model call. In the second stage (Sum - +probabilistic inference or reasoning), the results of these queries are +aggregated to make the final prediction. We demonstrate the possibilities and +advantages of ThinkSum on the BIG-bench suite of LLM evaluation tasks, +achieving improvements over the state of the art using GPT-family models on +thirteen difficult tasks, often with far smaller model variants. We also +compare and contrast ThinkSum with other proposed modifications to direct +prompting of LLMs, such as variants of chain-of-thought prompting. Our results +suggest that because the probabilistic inference in ThinkSum is performed +outside of calls to the LLM, ThinkSum is less sensitive to prompt design, +yields more interpretable predictions, and can be flexibly combined with latent +variable models to extract structured knowledge from LLMs. Overall, our +proposed paradigm represents a promising approach for enhancing the reasoning +capabilities of LLMs. +" +Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model,Jacob Eisenstein,http://arxiv.org/pdf/2210.02498v2.pdf,2022-10-05,"['cs.cl', 'cs.lg']",2210.02498v2.pdf," Explainable question answering systems should produce not only accurate +answers but also rationales that justify their reasoning and allow humans to +check their work. But what sorts of rationales are useful and how can we train +systems to produce them? We propose a new style of rationale for open-book +question answering, called \emph{markup-and-mask}, which combines aspects of +extractive and free-text explanations. In the markup phase, the passage is +augmented with free-text markup that enables each sentence to stand on its own +outside the discourse context. In the masking phase, a sub-span of the +marked-up passage is selected. To train a system to produce markup-and-mask +rationales without annotations, we leverage in-context learning. Specifically, +we generate silver annotated data by sending a series of prompts to a frozen +pretrained language model, which acts as a teacher. We then fine-tune a smaller +student model by training on the subset of rationales that led to correct +answers. The student is ""honest"" in the sense that it is a pipeline: the +rationale acts as a bottleneck between the passage and the answer, while the +""untrusted"" teacher operates under no such constraints. Thus, we offer a new +way to build trustworthy pipeline systems from a combination of end-task +annotations and frozen pretrained language models. +" +Large Language Models can Implement Policy Iteration,Ethan Brooks,http://arxiv.org/pdf/2210.03821v2.pdf,2022-10-07,['cs.lg'],2210.03821v2.pdf," This work presents In-Context Policy Iteration, an algorithm for performing +Reinforcement Learning (RL), in-context, using foundation models. While the +application of foundation models to RL has received considerable attention, +most approaches rely on either (1) the curation of expert demonstrations +(either through manual design or task-specific pretraining) or (2) adaptation +to the task of interest using gradient methods (either fine-tuning or training +of adapter layers). Both of these techniques have drawbacks. Collecting +demonstrations is labor-intensive, and algorithms that rely on them do not +outperform the experts from which the demonstrations were derived. All gradient +techniques are inherently slow, sacrificing the ""few-shot"" quality that made +in-context learning attractive to begin with. In this work, we present an +algorithm, ICPI, that learns to perform RL tasks without expert demonstrations +or gradients. Instead we present a policy-iteration method in which the prompt +content is the entire locus of learning. ICPI iteratively updates the contents +of the prompt from which it derives its policy through trial-and-error +interaction with an RL environment. In order to eliminate the role of +in-weights learning (on which approaches like Decision Transformer rely +heavily), we demonstrate our algorithm using Codex, a language model with no +prior knowledge of the domains on which we evaluate it. +" +Transformers generalize differently from information stored in context vs in weights,Stephanie C. Y. Chan,http://arxiv.org/pdf/2210.05675v2.pdf,2022-10-11,"['cs.cl', 'cs.ai', 'cs.lg']",2210.05675v2.pdf," Transformer models can use two fundamentally different kinds of information: +information stored in weights during training, and information provided +``in-context'' at inference time. In this work, we show that transformers +exhibit different inductive biases in how they represent and generalize from +the information in these two sources. In particular, we characterize whether +they generalize via parsimonious rules (rule-based generalization) or via +direct comparison with observed examples (exemplar-based generalization). This +is of important practical consequence, as it informs whether to encode +information in weights or in context, depending on how we want models to use +that information. In transformers trained on controlled stimuli, we find that +generalization from weights is more rule-based whereas generalization from +context is largely exemplar-based. In contrast, we find that in transformers +pre-trained on natural language, in-context learning is significantly +rule-based, with larger models showing more rule-basedness. We hypothesise that +rule-based generalization from in-context information might be an emergent +consequence of large-scale training on language, which has sparse rule-like +structure. Using controlled stimuli, we verify that transformers pretrained on +data containing sparse rule-like structure exhibit more rule-based +generalization. +" +Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters,Nuo Chen,http://arxiv.org/pdf/2211.06869v4.pdf,2022-11-13,"['cs.cl', 'cs.ai']",2211.06869v4.pdf," In recent years, Dialogue-style Large Language Models (LLMs) such as ChatGPT +and GPT4 have demonstrated immense potential in constructing open-domain +dialogue agents. However, aligning these agents with specific characters or +individuals remains a considerable challenge due to the complexities of +character representation and the lack of comprehensive annotations. In this +paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to +advance the study of dialogue agents and character alignment. The dataset +encompasses all dialogue sessions (in both English and Chinese) from the Harry +Potter series and is annotated with vital background information, including +dialogue scenes, speakers, character relationships, and attributes. These +extensive annotations may empower LLMs to unlock character-driven dialogue +capabilities. Furthermore, it can serve as a universal benchmark for evaluating +how well can a LLM aligning with a specific character. We benchmark LLMs on HPD +using both fine-tuning and in-context learning settings. Evaluation results +reveal that although there is substantial room for improvement in generating +high-quality, character-aligned responses, the proposed dataset is valuable in +guiding models toward responses that better align with the character of Harry +Potter. +" +Retrieval-Augmented Multimodal Language Modeling,Michihiro Yasunaga,http://arxiv.org/pdf/2211.12561v2.pdf,2022-11-22,"['cs.cv', 'cs.cl', 'cs.lg']",2211.12561v2.pdf," Recent multimodal models such as DALL-E and CM3 have achieved remarkable +progress in text-to-image and image-to-text generation. However, these models +store all learned knowledge (e.g., the appearance of the Eiffel Tower) in the +model parameters, requiring increasingly larger models and training data to +capture more knowledge. To integrate knowledge in a more scalable and modular +way, we propose a retrieval-augmented multimodal model, which enables a base +multimodal model (generator) to refer to relevant text and images fetched by a +retriever from external memory (e.g., documents on the web). Specifically, for +the retriever, we use a pretrained CLIP, and for the generator, we train a CM3 +Transformer on the LAION dataset. Our resulting model, named +Retrieval-Augmented CM3 (RA-CM3), is the first multimodal model that can +retrieve and generate both text and images. We show that RA-CM3 significantly +outperforms baseline multimodal models such as DALL-E and CM3 on both image and +caption generation tasks (12 FID and 17 CIDEr improvements on MS-COCO), while +requiring much less compute for training (<30% of DALL-E). Moreover, we show +that RA-CM3 exhibits novel capabilities, such as faithful image generation and +multimodal in-context learning (e.g., image generation from demonstrations). +" +"Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models",Vikas Raunak,http://arxiv.org/pdf/2212.00006v1.pdf,2022-11-19,"['cs.hc', 'cs.cl', 'cs.cv', 'cs.cy']",2212.00006v1.pdf," In this work, we present some recommendations on the evaluation of +state-of-the-art generative models for constrained generation tasks. The +progress on generative models has been rapid in recent years. These large-scale +models have had three impacts: firstly, the fluency of generation in both +language and vision modalities has rendered common average-case evaluation +metrics much less useful in diagnosing system errors. Secondly, the same +substrate models now form the basis of a number of applications, driven both by +the utility of their representations as well as phenomena such as in-context +learning, which raise the abstraction level of interacting with such models. +Thirdly, the user expectations around these models and their feted public +releases have made the technical challenge of out of domain generalization much +less excusable in practice. Subsequently, our evaluation methodologies haven't +adapted to these changes. More concretely, while the associated utility and +methods of interacting with generative models have expanded, a similar +expansion has not been observed in their evaluation practices. In this paper, +we argue that the scale of generative models could be exploited to raise the +abstraction level at which evaluation itself is conducted and provide +recommendations for the same. Our recommendations are based on leveraging +specifications as a powerful instrument to evaluate generation quality and are +readily applicable to a variety of tasks. +" +Language model acceptability judgements are not always robust to context,Koustuv Sinha,http://arxiv.org/pdf/2212.08979v1.pdf,2022-12-18,"['cs.cl', 'cs.lg']",2212.08979v1.pdf," Targeted syntactic evaluations of language models ask whether models show +stable preferences for syntactically acceptable content over minimal-pair +unacceptable inputs. Most targeted syntactic evaluation datasets ask models to +make these judgements with just a single context-free sentence as input. This +does not match language models' training regime, in which input sentences are +always highly contextualized by the surrounding corpus. This mismatch raises an +important question: how robust are models' syntactic judgements in different +contexts? In this paper, we investigate the stability of language models' +performance on targeted syntactic evaluations as we vary properties of the +input context: the length of the context, the types of syntactic phenomena it +contains, and whether or not there are violations of grammaticality. We find +that model judgements are generally robust when placed in randomly sampled +linguistic contexts. However, they are substantially unstable for contexts +containing syntactic structures matching those in the critical test content. +Among all tested models (GPT-2 and five variants of OPT), we significantly +improve models' judgements by providing contexts with matching syntactic +structures, and conversely significantly worsen them using unacceptable +contexts with matching but violated syntactic structures. This effect is +amplified by the length of the context, except for unrelated inputs. We show +that these changes in model performance are not explainable by simple features +matching the context and the test inputs, such as lexical overlap and +dependency overlap. This sensitivity to highly specific syntactic features of +the context can only be explained by the models' implicit in-context learning +abilities. +" +Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?,Ajay Patel,http://arxiv.org/pdf/2212.08986v2.pdf,2022-12-18,['cs.cl'],2212.08986v2.pdf," Authorship style transfer involves altering text to match the style of a +target author whilst preserving the original meaning. Existing unsupervised +approaches like STRAP have largely focused on style transfer to target authors +with many examples of their writing style in books, speeches, or other +published works. This high-resource training data requirement (often greater +than 100,000 words) makes these approaches primarily useful for style transfer +to published authors, politicians, or other well-known figures and authorship +styles, while style transfer to non-famous authors has not been well-studied. +We introduce the \textit{low-resource authorship style transfer} task, a more +challenging class of authorship style transfer where only a limited amount of +text in the target author's style may exist. In our experiments, we +specifically choose source and target authors from Reddit and style transfer +their Reddit posts, limiting ourselves to just 16 posts (on average ~500 words) +of the target author's style. Style transfer accuracy is typically measured by +how often a classifier or human judge will classify an output as written by the +target author. Recent authorship representations models excel at authorship +identification even with just a few writing samples, making automatic +evaluation of this task possible for the first time through evaluation metrics +we propose. Our results establish an in-context learning technique we develop +as the strongest baseline, though we find current approaches do not yet achieve +mastery of this challenging task. We release our data and implementations to +encourage further investigation. +" +Training Trajectories of Language Models Across Scales,Mengzhou Xia,http://arxiv.org/pdf/2212.09803v3.pdf,2022-12-19,"['cs.cl', 'cs.ai', 'cs.lg']",2212.09803v3.pdf," Scaling up language models has led to unprecedented performance gains, but +little is understood about how the training dynamics change as models get +larger. How do language models of different sizes learn during pre-training? +Why do larger language models demonstrate more desirable behaviors? In this +paper, we analyze the intermediate training checkpoints of differently sized +OPT models (Zhang et al.,2022)--from 125M to 175B parameters--on next-token +prediction, sequence-level generation, and downstream tasks. We find that 1) at +a given perplexity and independent of model sizes, a similar subset of training +tokens see the most significant reduction in loss, with the rest stagnating or +showing double-descent behavior; 2) early in training, all models learn to +reduce the perplexity of grammatical sequences that contain hallucinations, +with small models halting at this suboptimal distribution and larger ones +eventually learning to assign these sequences lower probabilities; 3) +perplexity is a strong predictor of in-context learning performance on 74 +multiple-choice tasks from BIG-Bench, and this holds independent of the model +size. Together, these results show that perplexity is more predictive of model +behaviors than model size or training computation. +" +Dialog2API: Task-Oriented Dialogue with API Description and Example Programs,Raphael Shu,http://arxiv.org/pdf/2212.09946v1.pdf,2022-12-20,['cs.cl'],2212.09946v1.pdf," Functionality and dialogue experience are two important factors of +task-oriented dialogue systems. Conventional approaches with closed schema +(e.g., conversational semantic parsing) often fail as both the functionality +and dialogue experience are strongly constrained by the underlying schema. We +introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly +expand the functionality and provide seamless dialogue experience. The +conversational model interacts with the environment by generating and executing +programs triggering a set of pre-defined APIs. The model also manages the +dialogue policy and interact with the user through generating appropriate +natural language responses. By allowing generating free-form programs, +Dialog2API supports composite goals by combining different APIs, whereas +unrestricted program revision provides natural and robust dialogue experience. +To facilitate Dialog2API, the core model is provided with API documents, an +execution environment and optionally some example dialogues annotated with +programs. We propose an approach tailored for the Dialog2API, where the +dialogue states are represented by a stack of programs, with most recently +mentioned program on the top of the stack. Dialog2API can work with many +application scenarios such as software automation and customer service. In this +paper, we construct a dataset for AWS S3 APIs and present evaluation results of +in-context learning baselines. +" +HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation,Hamish Ivison,http://arxiv.org/pdf/2212.10315v2.pdf,2022-12-20,['cs.cl'],2212.10315v2.pdf," Recent NLP models have shown the remarkable ability to effectively generalise +`zero-shot' to new tasks using only natural language instructions as guidance. +However, many of these approaches suffer from high computational costs due to +their reliance on concatenating lengthy instructions with every input example, +resulting in costly reprocessing of the instruction. To avoid this, we +introduce Hypernetworks for INstruction Tuning (HINT), which convert task +instructions and examples into parameter-efficient modules inserted into an +underlying model using a pretrained text encoder, eliminating the need to +include instructions in the model input. The hypernetwork in HINT also produces +an encoded instruction, which we concatenate with encoded inputs during +decoding to further improve performance. HINT models outperform strong +state-of-the-art baselines by over 10% when controlling for compute (measured +in FLOPs). By converting instructions into modules, HINT models can effectively +disregard the length of instructions and few-shot example inputs in terms of +compute usage. As a result, HINT can enhance its performance by up to 25% by +incorporating additional few-shot data, while utilizing only up to 5% more +compute. This combines the strengths of parameter-efficient fine-tuning and +in-context learning. +" +Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners,Hyunsoo Cho,http://arxiv.org/pdf/2212.10873v3.pdf,2022-12-21,"['cs.cl', 'cs.lg']",2212.10873v3.pdf," Through in-context learning (ICL), large-scale language models are effective +few-shot learners without additional model fine-tuning. However, the ICL +performance does not scale well with the number of available training samples +as it is limited by the inherent input length constraint of the underlying +language model. Meanwhile, many studies have revealed that language models are +also powerful feature extractors, allowing them to be utilized in a black-box +manner and enabling the linear probing paradigm, where lightweight +discriminators are trained on top of the pre-extracted input representations. +This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear +probing and ICL, which leverages the best of both worlds. PALP inherits the +scalability of linear probing and the capability of enforcing language models +to derive more meaningful representations via tailoring input into a more +conceivable form. Throughout in-depth investigations on various datasets, we +verified that PALP significantly enhances the input representations closing the +gap between ICL in the data-hungry scenario and fine-tuning in the +data-abundant scenario with little training overhead, potentially making PALP a +strong alternative in a black-box scenario. +" +Parallel Context Windows for Large Language Models,Nir Ratner,http://arxiv.org/pdf/2212.10947v3.pdf,2022-12-21,['cs.cl'],2212.10947v3.pdf," When applied to processing long text, Large Language Models (LLMs) are +limited by their context window. Existing efforts to address this limitation +involve training specialized architectures, and cannot be easily applied to +off-the-shelf LLMs. We present Parallel Context Windows (PCW), a method that +alleviates the context window restriction for any off-the-shelf LLM without +further training. The key to the approach is to carve a long context into +chunks (``windows''), restrict the attention mechanism to apply only within +each window, and re-use the positional embeddings across the windows. Our main +results test the PCW approach on in-context learning with models that range in +size between 750 million and 178 billion parameters, and show substantial +improvements for tasks with diverse input and output spaces. We show additional +benefits in other settings where long context windows may be beneficial: +multi-hop questions and retrieval-augmented question answering with multiple +retrieved documents. Our results highlight Parallel Context Windows as a +promising method for applying off-the-shelf LLMs in a range of settings that +require long text sequences. We make our code publicly available at +https://github.com/ai21labs/parallel-context-windows. +" +Collaborating with language models for embodied reasoning,Ishita Dasgupta,http://arxiv.org/pdf/2302.00763v1.pdf,2023-02-01,"['cs.lg', 'cs.ai', 'cs.cl']",2302.00763v1.pdf," Reasoning in a complex and ambiguous environment is a key goal for +Reinforcement Learning (RL) agents. While some sophisticated RL agents can +successfully solve difficult tasks, they require a large amount of training +data and often struggle to generalize to new unseen environments and new tasks. +On the other hand, Large Scale Language Models (LSLMs) have exhibited strong +reasoning ability and the ability to to adapt to new tasks through in-context +learning. However, LSLMs do not inherently have the ability to interrogate or +intervene on the environment. In this work, we investigate how to combine these +complementary abilities in a single system consisting of three parts: a +Planner, an Actor, and a Reporter. The Planner is a pre-trained language model +that can issue commands to a simple embodied agent (the Actor), while the +Reporter communicates with the Planner to inform its next command. We present a +set of tasks that require reasoning, test this system's ability to generalize +zero-shot and investigate failure cases, and demonstrate how components of this +system can be trained with reinforcement-learning to improve performance. +" +Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning,Angela Ramirez,http://arxiv.org/pdf/2302.03848v1.pdf,2023-02-08,['cs.cl'],2302.03848v1.pdf," Prompt-based or in-context learning has achieved high zero-shot performance +on many natural language generation (NLG) tasks. Here we explore the +performance of prompt-based learning for simultaneously controlling the +personality and the semantic accuracy of an NLG for task-oriented dialogue. We +experiment with prompt-based learning on the PERSONAGE restaurant +recommendation corpus to generate semantically and stylistically-controlled +text for 5 different Big-5 personality types: agreeable, disagreeable, +conscientious, unconscientious, and extravert. We test two different classes of +discrete prompts to generate utterances for a particular personality style: (1) +prompts that demonstrate generating directly from a meaning representation that +includes a personality specification; and (2) prompts that rely on first +converting the meaning representation to a textual pseudo-reference, and then +using the pseudo-reference in a textual style transfer (TST) prompt. In each +case, we show that we can vastly improve performance by over-generating outputs +and ranking them, testing several ranking functions based on automatic metrics +for semantic accuracy, personality-match, and fluency. We also test whether NLG +personality demonstrations from the restaurant domain can be used with meaning +representations for the video game domain to generate personality stylized +utterances about video games. Our findings show that the TST prompts produces +the highest semantic accuracy (78.46% for restaurants and 87.6% for video +games) and personality accuracy (100% for restaurants and 97% for video games). +Our results on transferring personality style to video game utterances are +surprisingly good. To our knowledge, there is no previous work testing the +application of prompt-based learning to simultaneously controlling both style +and semantic accuracy in NLG. +" +Distinguishability Calibration to In-Context Learning,Hongjing Li,http://arxiv.org/pdf/2302.06198v3.pdf,2023-02-13,['cs.cl'],2302.06198v3.pdf," Recent years have witnessed increasing interests in prompt-based learning in +which models can be trained on only a few annotated instances, making them +suitable in low-resource settings. When using prompt-based learning for text +classification, the goal is to use a pre-trained language model (PLM) to +predict a missing token in a pre-defined template given an input text, which +can be mapped to a class label. However, PLMs built on the transformer +architecture tend to generate similar output embeddings, making it difficult to +discriminate between different class labels. The problem is further exacerbated +when dealing with classification tasks involving many fine-grained class +labels. In this work, we alleviate this information diffusion issue, i.e., +different tokens share a large proportion of similar information after going +through stacked multiple self-attention layers in a transformer, by proposing a +calibration method built on feature transformations through rotation and +scaling to map a PLM-encoded embedding into a new metric space to guarantee the +distinguishability of the resulting embeddings. Furthermore, we take the +advantage of hyperbolic embeddings to capture the hierarchical relations among +fine-grained class-associated token embedding by a coarse-to-fine metric +learning strategy to enhance the distinguishability of the learned output +embeddings. Extensive experiments on the three datasets under various settings +demonstrate the effectiveness of our approach. Our code can be found at +https://github.com/donttal/TARA. +" +Do We Still Need Clinical Language Models?,Eric Lehman,http://arxiv.org/pdf/2302.08091v1.pdf,2023-02-16,['cs.cl'],2302.08091v1.pdf," Although recent advances in scaling large language models (LLMs) have +resulted in improvements on many NLP tasks, it remains unclear whether these +models trained primarily with general web text are the right tool in highly +specialized, safety critical domains such as clinical text. Recent results have +suggested that LLMs encode a surprising amount of medical knowledge. This +raises an important question regarding the utility of smaller domain-specific +language models. With the success of general-domain LLMs, is there still a need +for specialized clinical models? To investigate this question, we conduct an +extensive empirical analysis of 12 language models, ranging from 220M to 175B +parameters, measuring their performance on 3 different clinical tasks that test +their ability to parse and reason over electronic health records. As part of +our experiments, we train T5-Base and T5-Large models from scratch on clinical +notes from MIMIC III and IV to directly investigate the efficiency of clinical +tokens. We show that relatively small specialized clinical models substantially +outperform all in-context learning approaches, even when finetuned on limited +annotated data. Further, we find that pretraining on clinical tokens allows for +smaller, more parameter-efficient models that either match or outperform much +larger language models trained on general text. We release the code and the +models used under the PhysioNet Credentialed Health Data license and data use +agreement. +" +eP-ALM: Efficient Perceptual Augmentation of Language Models,Mustafa Shukor,http://arxiv.org/pdf/2303.11403v4.pdf,2023-03-20,"['cs.cv', 'cs.cl', 'cs.lg']",2303.11403v4.pdf," Large Language Models (LLMs) have so far impressed the world, with +unprecedented capabilities that emerge in models at large scales. On the vision +side, transformer models (i.e., ViT) are following the same trend, achieving +the best performance on challenging benchmarks. With the abundance of such +unimodal models, a natural question arises; do we need also to follow this +trend to tackle multimodal tasks? In this work, we propose to rather direct +effort to efficient adaptations of existing models, and propose to augment +Language Models with perception. Existing approaches for adapting pretrained +models for vision-language tasks still rely on several key components that +hinder their efficiency. In particular, they still train a large number of +parameters, rely on large multimodal pretraining, use encoders (e.g., CLIP) +trained on huge image-text datasets, and add significant inference overhead. In +addition, most of these approaches have focused on Zero-Shot and In Context +Learning, with little to no effort on direct finetuning. We investigate the +minimal computational effort needed to adapt unimodal models for multimodal +tasks and propose a new challenging setup, alongside different approaches, that +efficiently adapts unimodal pretrained models. We show that by freezing more +than 99% of total parameters, training only one linear projection layer, and +prepending only one trainable token, our approach (dubbed eP-ALM) significantly +outperforms other baselines on VQA and Captioning across Image, Video, and +Audio modalities, following the proposed setup. The code is available here: +https://github.com/mshukor/eP-ALM. +" +Towards Making the Most of ChatGPT for Machine Translation,Keqin Peng,http://arxiv.org/pdf/2303.13780v4.pdf,2023-03-24,['cs.cl'],2303.13780v4.pdf," ChatGPT shows remarkable capabilities for machine translation (MT). Several +prior studies have shown that it achieves comparable results to commercial +systems for high-resource languages, but lags behind in complex tasks, e.g., +low-resource and distant-language-pairs translation. However, they usually +adopt simple prompts which can not fully elicit the capability of ChatGPT. In +this paper, we aim to further mine ChatGPT's translation ability by revisiting +several aspects: temperature, task information, and domain information, and +correspondingly propose an optimal temperature setting and two (simple but +effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts +(DSP). We show that: 1) The performance of ChatGPT depends largely on +temperature, and a lower temperature usually can achieve better performance; 2) +Emphasizing the task information can further improve ChatGPT's performance, +particularly in complex MT tasks; 3) Introducing domain information can elicit +ChatGPT's generalization ability and improve its performance in the specific +domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT +tasks, which can be partially addressed by our proposed prompts but still need +to be highlighted for the MT/NLP community. We also explore the effects of +advanced in-context learning strategies and find a (negative but interesting) +observation: the powerful chain-of-thought prompt leads to word-by-word +translation behavior, thus bringing significant translation degradation. +" +$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference,Benfeng Xu,http://arxiv.org/pdf/2303.13824v1.pdf,2023-03-24,"['cs.cl', 'cs.ai']",2303.13824v1.pdf," In-Context Learning (ICL), which formulates target tasks as prompt completion +conditioned on in-context demonstrations, has become the prevailing utilization +of LLMs. In this paper, we first disclose an actual predicament for this +typical usage that it can not scale up with training data due to context length +restriction. Besides, existing works have shown that ICL also suffers from +various biases and requires delicate calibration treatment. To address both +challenges, we advocate a simple and effective solution, $k$NN Prompting, which +first queries LLM with training data for distributed representations, then +predicts test instances by simply referring to nearest neighbors. We conduct +comprehensive experiments to demonstrate its two-fold superiority: 1) +Calibration-Free: $k$NN Prompting does not directly align LLM output +distribution with task-specific label space, instead leverages such +distribution to align test and training instances. It significantly outperforms +state-of-the-art calibration-based methods under comparable few-shot scenario. +2) Beyond-Context: $k$NN Prompting can further scale up effectively with as +many training data as are available, continually bringing substantial +improvements. The scaling trend holds across 10 orders of magnitude ranging +from 2 shots to 1024 shots as well as different LLMs scales ranging from 0.8B +to 30B. It successfully bridges data scaling into model scaling, and brings new +potentials for the gradient-free paradigm of LLM deployment. Code is publicly +available. +" +Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System,Yunfan Gao,http://arxiv.org/pdf/2303.14524v2.pdf,2023-03-25,"['cs.ir', 'cs.cl', 'cs.lg']",2303.14524v2.pdf," Large language models (LLMs) have demonstrated their significant potential to +be applied for addressing various application tasks. However, traditional +recommender systems continue to face great challenges such as poor +interactivity and explainability, which actually also hinder their broad +deployment in real-world systems. To address these limitations, this paper +proposes a novel paradigm called Chat-Rec (ChatGPT Augmented Recommender +System) that innovatively augments LLMs for building conversational recommender +systems by converting user profiles and historical interactions into prompts. +Chat-Rec is demonstrated to be effective in learning user preferences and +establishing connections between users and products through in-context +learning, which also makes the recommendation process more interactive and +explainable. What's more, within the Chat-Rec framework, user's preferences can +transfer to different products for cross-domain recommendations, and +prompt-based injection of information into LLMs can also handle the cold-start +scenarios with new items. In our experiments, Chat-Rec effectively improve the +results of top-k recommendations and performs better in zero-shot rating +prediction task. Chat-Rec offers a novel approach to improving recommender +systems and presents new practical scenarios for the implementation of AIGC (AI +generated content) in recommender system studies. +" +What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?,Shuzheng Gao,http://arxiv.org/pdf/2304.07575v2.pdf,2023-04-15,['cs.se'],2304.07575v2.pdf," Pre-trained models of source code have gained widespread popularity in many +code intelligence tasks. Recently, with the scaling of the model and corpus +size, large language models have shown the ability of in-context learning +(ICL). ICL employs task instructions and a few examples as demonstrations, and +then inputs the demonstrations to the language models for making predictions. +This new learning paradigm is training-free and has shown impressive +performance in various natural language processing and code intelligence tasks. +However, the performance of ICL heavily relies on the quality of +demonstrations, e.g., the selected examples. It is important to systematically +investigate how to construct a good demonstration for code-related tasks. In +this paper, we empirically explore the impact of three key factors on the +performance of ICL in code intelligence tasks: the selection, order, and number +of demonstration examples. We conduct extensive experiments on three code +intelligence tasks including code summarization, bug fixing, and program +synthesis. Our experimental results demonstrate that all the above three +factors dramatically impact the performance of ICL in code intelligence tasks. +Additionally, we summarize our findings and provide takeaway suggestions on how +to construct effective demonstrations, taking into account these three +perspectives. We also show that a carefully-designed demonstration based on our +findings can lead to substantial improvements over widely-used demonstration +construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, +175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, +respectively +" +Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference for Mobile AIGC Services,Minrui Xu,http://arxiv.org/pdf/2304.08782v2.pdf,2023-04-18,['cs.ni'],2304.08782v2.pdf," Aiming at achieving artificial general intelligence (AGI) for Metaverse, +pretrained foundation models (PFMs), e.g., generative pretrained transformers +(GPTs), can effectively provide various AI services, such as autonomous +driving, digital twins, and AI-generated content (AIGC) for extended reality. +With the advantages of low latency and privacy-preserving, serving PFMs of +mobile AI services in edge intelligence is a viable solution for caching and +executing PFMs on edge servers with limited computing resources and GPU memory. +However, PFMs typically consist of billions of parameters that are computation +and memory-intensive for edge servers during loading and execution. In this +article, we investigate edge PFM serving problems for mobile AIGC services of +Metaverse. First, we introduce the fundamentals of PFMs and discuss their +characteristic fine-tuning and inference methods in edge intelligence. Then, we +propose a novel framework of joint model caching and inference for managing +models and allocating resources to satisfy users' requests efficiently. +Furthermore, considering the in-context learning ability of PFMs, we propose a +new metric to evaluate the freshness and relevance between examples in +demonstrations and executing tasks, namely the Age of Context (AoC). Finally, +we propose a least context algorithm for managing cached models at edge servers +by balancing the tradeoff among latency, energy consumption, and accuracy. +" +Controlled Text Generation with Natural Language Instructions,Wangchunshu Zhou,http://arxiv.org/pdf/2304.14293v2.pdf,2023-04-27,"['cs.cl', 'cs.ai', 'cs.lg']",2304.14293v2.pdf," Large language models generate fluent texts and can follow natural language +instructions to solve a wide range of tasks without task-specific training. +Nevertheless, it is notoriously difficult to control their generation to +satisfy the various constraints required by different applications. In this +work, we present InstructCTG, a controlled text generation framework that +incorporates different constraints by conditioning on natural language +descriptions and demonstrations of the constraints. In particular, we first +extract the underlying constraints of natural texts through a combination of +off-the-shelf NLP tools and simple heuristics. We then verbalize the +constraints into natural language instructions to form weakly supervised +training data. By prepending natural language descriptions of the constraints +and a few demonstrations, we fine-tune a pre-trained language model to +incorporate various types of constraints. Compared to existing search-based or +score-based methods, InstructCTG is more flexible to different constraint types +and has a much smaller impact on the generation quality and speed because it +does not modify the decoding procedure. Additionally, InstructCTG allows the +model to adapt to new constraints without re-training through the use of +few-shot task generalization and in-context learning abilities of +instruction-tuned language models. +" +TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation,Keqin Bao,http://arxiv.org/pdf/2305.00447v3.pdf,2023-04-30,['cs.ir'],2305.00447v3.pdf," Large Language Models (LLMs) have demonstrated remarkable performance across +diverse domains, thereby prompting researchers to explore their potential for +use in recommendation systems. Initial attempts have leveraged the exceptional +capabilities of LLMs, such as rich knowledge and strong generalization through +In-context Learning, which involves phrasing the recommendation task as +prompts. Nevertheless, the performance of LLMs in recommendation tasks remains +suboptimal due to a substantial disparity between the training tasks for LLMs +and recommendation tasks, as well as inadequate recommendation data during +pre-training. To bridge the gap, we consider building a Large Recommendation +Language Model by tunning LLMs with recommendation data. To this end, we +propose an efficient and effective Tuning framework for Aligning LLMs with +Recommendation, namely TALLRec. We have demonstrated that the proposed TALLRec +framework can significantly enhance the recommendation capabilities of LLMs in +the movie and book domains, even with a limited dataset of fewer than 100 +samples. Additionally, the proposed framework is highly efficient and can be +executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM +exhibits robust cross-domain generalization. Our code and data are available at +https://github.com/SAI990323/TALLRec. +" +Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction,Ashish Sharma,http://arxiv.org/pdf/2305.02466v1.pdf,2023-05-04,"['cs.cl', 'cs.hc', 'cs.si']",2305.02466v1.pdf," A proven therapeutic technique to overcome negative thoughts is to replace +them with a more hopeful ""reframed thought."" Although therapy can help people +practice and learn this Cognitive Reframing of Negative Thoughts, clinician +shortages and mental health stigma commonly limit people's access to therapy. +In this paper, we conduct a human-centered study of how language models may +assist people in reframing negative thoughts. Based on psychology literature, +we define a framework of seven linguistic attributes that can be used to +reframe a thought. We develop automated metrics to measure these attributes and +validate them with expert judgements from mental health practitioners. We +collect a dataset of 600 situations, thoughts and reframes from practitioners +and use it to train a retrieval-enhanced in-context learning model that +effectively generates reframed thoughts and controls their linguistic +attributes. To investigate what constitutes a ""high-quality"" reframe, we +conduct an IRB-approved randomized field study on a large mental health website +with over 2,000 participants. Amongst other findings, we show that people +prefer highly empathic or specific reframes, as opposed to reframes that are +overly positive. Our findings provide key implications for the use of LMs to +assist people in overcoming negative thoughts. +" +Using ChatGPT for Entity Matching,Ralph Peeters,http://arxiv.org/pdf/2305.03423v2.pdf,2023-05-05,['cs.cl'],2305.03423v2.pdf," Entity Matching is the task of deciding if two entity descriptions refer to +the same real-world entity. State-of-the-art entity matching methods often rely +on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks +of using these models for entity matching are that (i) the models require +significant amounts of fine-tuning data for reaching a good performance and +(ii) the fine-tuned models are not robust concerning out-of-distribution +entities. In this paper, we investigate using ChatGPT for entity matching as a +more robust, training data-efficient alternative to traditional Transformer +models. We perform experiments along three dimensions: (i) general prompt +design, (ii) in-context learning, and (iii) provision of higher-level matching +knowledge. We show that ChatGPT is competitive with a fine-tuned RoBERTa model, +reaching a zero-shot performance of 82.35% F1 on a challenging matching task on +which RoBERTa requires 2000 training examples for reaching a similar +performance. Adding in-context demonstrations to the prompts further improves +the F1 by up to 7.85% when using similarity-based example selection. Always +using the same set of 10 handpicked demonstrations leads to an improvement of +4.92% over the zero-shot performance. Finally, we show that ChatGPT can also be +guided by adding higher-level matching knowledge in the form of rules to the +prompts. Providing matching rules leads to similar performance gains as +providing in-context demonstrations. +" +Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment,Eshaan Tanwar,http://arxiv.org/pdf/2305.05940v3.pdf,2023-05-10,['cs.cl'],2305.05940v3.pdf," In-context learning (ICL) unfolds as large language models become capable of +inferring test labels conditioned on a few labeled samples without any gradient +update. ICL-enabled large language models provide a promising step forward +toward bypassing recurrent annotation costs in a low-resource setting. Yet, +only a handful of past studies have explored ICL in a cross-lingual setting, in +which the need for transferring label-knowledge from a high-resource language +to a low-resource one is immensely crucial. To bridge the gap, we provide the +first in-depth analysis of ICL for cross-lingual text classification. We find +that the prevalent mode of selecting random input-label pairs to construct the +prompt-context is severely limited in the case of cross-lingual ICL, primarily +due to the lack of alignment in the input as well as the output spaces. To +mitigate this, we propose a novel prompt construction strategy -- Cross-lingual +In-context Source-Target Alignment (X-InSTA). With an injected coherence in the +semantics of the input examples and a task-based alignment across the source +and target languages, X-InSTA is able to outperform random prompt selection by +a large margin across three different tasks using 44 different cross-lingual +pairs. +" +Can Language Models Solve Graph Problems in Natural Language?,Heng Wang,http://arxiv.org/pdf/2305.10037v2.pdf,2023-05-17,"['cs.cl', 'cs.ai']",2305.10037v2.pdf," Large language models (LLMs) are increasingly adopted for a variety of tasks +with implicit graphical structures, such as planning in robotics, multi-hop +question answering or knowledge probing, structured commonsense reasoning, and +more. While LLMs have advanced the state-of-the-art on these tasks with +structure implications, whether LLMs could explicitly process textual +descriptions of graphs and structures, map them to grounded conceptual spaces, +and perform structured operations remains underexplored. To this end, we +propose NLGraph (Natural Language Graph), a comprehensive benchmark of +graph-based problem solving designed in natural language. NLGraph contains +29,370 problems, covering eight graph reasoning tasks with varying complexity +from simple tasks such as connectivity and shortest path up to complex problems +such as maximum flow and simulating graph neural networks. We evaluate LLMs +(GPT-3/4) with various prompting approaches on the NLGraph benchmark and find +that 1) language models do demonstrate preliminary graph reasoning abilities, +2) the benefit of advanced prompting and in-context learning diminishes on more +complex graph problems, while 3) LLMs are also (un)surprisingly brittle in the +face of spurious correlations in graph and problem settings. We then propose +Build-a-Graph Prompting and Algorithmic Prompting, two instruction-based +approaches to enhance LLMs in solving natural language graph problems. +Build-a-Graph and Algorithmic prompting improve the performance of LLMs on +NLGraph by 3.07% to 16.85% across multiple tasks and settings, while how to +solve the most complicated graph reasoning tasks in our setup with language +models remains an open research question. The NLGraph benchmark and evaluation +code are available at https://github.com/Arthur-Heng/NLGraph. +" +Joint Foundation Model Caching and Inference of Generative AI Services for Edge Intelligence,Minrui Xu,http://arxiv.org/pdf/2305.12130v1.pdf,2023-05-20,['cs.ni'],2305.12130v1.pdf," With the rapid development of artificial general intelligence (AGI), various +multimedia services based on pretrained foundation models (PFMs) need to be +effectively deployed. With edge servers that have cloud-level computing power, +edge intelligence can extend the capabilities of AGI to mobile edge networks. +However, compared with cloud data centers, resource-limited edge servers can +only cache and execute a small number of PFMs, which typically consist of +billions of parameters and require intensive computing power and GPU memory +during inference. To address this challenge, in this paper, we propose a joint +foundation model caching and inference framework that aims to balance the +tradeoff among inference latency, accuracy, and resource consumption by +managing cached PFMs and user requests efficiently during the provisioning of +generative AI services. Specifically, considering the in-context learning +ability of PFMs, a new metric named the Age of Context (AoC), is proposed to +model the freshness and relevance between examples in past demonstrations and +current service requests. Based on the AoC, we propose a least context caching +algorithm to manage cached PFMs at edge servers with historical prompts and +inference results. The numerical results demonstrate that the proposed +algorithm can reduce system costs compared with existing baselines by +effectively utilizing contextual information. +" +Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies,Linyong Nan,http://arxiv.org/pdf/2305.12586v1.pdf,2023-05-21,['cs.cl'],2305.12586v1.pdf," In-context learning (ICL) has emerged as a new approach to various natural +language processing tasks, utilizing large language models (LLMs) to make +predictions based on context that has been supplemented with a few examples or +task-specific instructions. In this paper, we aim to extend this method to +question answering tasks that utilize structured knowledge sources, and improve +Text-to-SQL systems by exploring various prompt design strategies for employing +LLMs. We conduct a systematic investigation into different demonstration +selection methods and optimal instruction formats for prompting LLMs in the +Text-to-SQL task. Our approach involves leveraging the syntactic structure of +an example's SQL query to retrieve demonstrations, and we demonstrate that +pursuing both diversity and similarity in demonstration selection leads to +enhanced performance. Furthermore, we show that LLMs benefit from +database-related knowledge augmentations. Our most effective strategy +outperforms the state-of-the-art system by 2.5 points (Execution Accuracy) and +the best fine-tuned system by 5.1 points on the Spider dataset. These results +highlight the effectiveness of our approach in adapting LLMs to the Text-to-SQL +task, and we present an analysis of the factors contributing to the success of +our strategy. +" +Exploring Chain-of-Thought Style Prompting for Text-to-SQL,Chang-You Tai,http://arxiv.org/pdf/2305.14215v2.pdf,2023-05-23,['cs.cl'],2305.14215v2.pdf," In-context learning with large language models (LLMs) has recently caught +increasing attention due to its superior few-shot performance on various tasks. +However, its performance on text-to-SQL parsing still has much room for +improvement. In this paper, we hypothesize that a crucial aspect of LLMs to +improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we +systematically study how to enhance LLMs' reasoning ability through chain of +thought (CoT) style prompting, including the original chain-of-thought +prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023). +Our experiments demonstrate that iterative prompting as in Zhou et al. (2023) +may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps +tends to have more error propagation issues. Based on these findings, we +propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 +and 6.5 point absolute gains on the Spider development set and the Spider +Realistic set, respectively, compared to the standard prompting method without +reasoning steps; 2.4 and 1.5 point absolute gains, compared to the +least-to-most prompting method. +" +Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment,Sky CH-Wang,http://arxiv.org/pdf/2305.14492v2.pdf,2023-05-23,['cs.cl'],2305.14492v2.pdf," Designing systems that can reason across cultures requires that they are +grounded in the norms of the contexts in which they operate. However, current +research on developing computational models of social norms has primarily +focused on American society. Here, we propose a novel approach to discover and +compare descriptive social norms across Chinese and American cultures. We +demonstrate our approach by leveraging discussions on a Chinese Q&A platform +(Zhihu) and the existing SocialChemistry dataset as proxies for contrasting +cultural axes, align social situations cross-culturally, and extract social +norms from texts using in-context learning. Embedding Chain-of-Thought +prompting in a human-AI collaborative framework, we build a high-quality +dataset of 3,069 social norms aligned with social situations across Chinese and +American cultures alongside corresponding free-text explanations. To test the +ability of models to reason about social norms across cultures, we introduce +the task of explainable social norm entailment, showing that existing models +under 3B parameters have significant room for improvement in both automatic and +human evaluation. Further analysis of cross-cultural norm differences based on +our dataset shows empirical alignment with the social orientations framework, +revealing several situational and descriptive nuances in norms across these +cultures. +" +Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy,Sarah Wiegreffe,http://arxiv.org/pdf/2305.14596v2.pdf,2023-05-24,"['cs.cl', 'cs.lg']",2305.14596v2.pdf," When pretrained language models (LMs) are applied to discriminative tasks +such as multiple-choice questions, they place probability mass on vocabulary +tokens that aren't among the given answer choices. Spreading probability mass +across multiple surface forms with identical meaning (such as ""bath"" and +""bathtub"") is thought to cause an underestimation of a model's true +performance, referred to as the ""surface form competition"" (SFC) hypothesis. +This has motivated the introduction of various probability normalization +methods. However, many core questions remain unanswered. How do we measure SFC? +Are there direct ways of reducing it, and does doing so improve task +performance? + We propose a mathematical formalism for SFC which allows us to quantify and +bound its impact for the first time. We identify a simple method for reducing +it -- namely, increasing probability mass on the given answer choices by a) +including them in the prompt and b) using in-context learning with even just +one example. We show this method eliminates the impact of SFC in the majority +of instances. Our experiments on three diverse datasets and six LMs reveal +several additional surprising findings. For example, both normalization and +prompting methods for reducing SFC can be ineffective or even detrimental to +task performance for some LMs. We conclude with practical insights for +effectively prompting LMs for multiple-choice tasks. +" +Universal Self-Adaptive Prompting,Xingchen Wan,http://arxiv.org/pdf/2305.14926v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14926v2.pdf," A hallmark of modern large language models (LLMs) is their impressive general +zero-shot and few-shot abilities, often elicited through in-context learning +(ICL) via prompting. However, while highly coveted and being the most general, +zero-shot performances in LLMs are still typically weaker due to the lack of +guidance and the difficulty of applying existing automatic prompt design +methods in general tasks when ground-truth labels are unavailable. In this +study, we address this by presenting Universal Self-Adaptive Prompting (USP), +an automatic prompt design approach specifically tailored for zero-shot +learning (while compatible with few-shot). Requiring only a small amount of +unlabeled data and an inference-only LLM, USP is highly versatile: to achieve +universal prompting, USP categorizes a possible NLP task into one of the three +possible task types and then uses a corresponding selector to select the most +suitable queries and zero-shot model-generated responses as +pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a +fully automated way. We evaluate USP with PaLM and PaLM 2 models and +demonstrate performances that are considerably stronger than standard zero-shot +baselines and often comparable to or even superior to few-shot baselines across +more than 40 natural language understanding, natural language generation, and +reasoning tasks. +" +Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization,Aman Priyanshu,http://arxiv.org/pdf/2305.15008v1.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.cy']",2305.15008v1.pdf," LLM-powered chatbots are becoming widely adopted in applications such as +healthcare, personal assistants, industry hiring decisions, etc. In many of +these cases, chatbots are fed sensitive, personal information in their prompts, +as samples for in-context learning, retrieved records from a database, or as +part of the conversation. The information provided in the prompt could directly +appear in the output, which might have privacy ramifications if there is +sensitive information there. As such, in this paper, we aim to understand the +input copying and regurgitation capabilities of these models during inference +and how they can be directly instructed to limit this copying by complying with +regulations such as HIPAA and GDPR, based on their internal knowledge of them. +More specifically, we find that when ChatGPT is prompted to summarize cover +letters of a 100 candidates, it would retain personally identifiable +information (PII) verbatim in 57.4% of cases, and we find this retention to be +non-uniform between different subgroups of people, based on attributes such as +gender identity. We then probe ChatGPT's perception of privacy-related policies +and privatization mechanisms by directly instructing it to provide compliant +outputs and observe a significant omission of PII from output. +" +Fine-Tuning Language Models with Just Forward Passes,Sadhika Malladi,http://arxiv.org/pdf/2305.17333v2.pdf,2023-05-27,"['cs.lg', 'cs.cl']",2305.17333v2.pdf," Fine-tuning language models (LMs) has yielded success on diverse downstream +tasks, but as LMs grow in size, backpropagation requires a prohibitively large +amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients +using only two forward passes but are theorized to be catastrophically slow for +optimizing large models. In this work, we propose a memory-efficient +zerothorder optimizer (MeZO), adapting the classical ZO-SGD method to operate +in-place, thereby fine-tuning LMs with the same memory footprint as inference. +For example, with a single A100 80GB GPU, MeZO can train a 30-billion parameter +model, whereas fine-tuning with backpropagation can train only a 2.7B LM with +the same budget. We conduct comprehensive experiments across model types +(masked and autoregressive LMs), model scales (up to 66B), and downstream tasks +(classification, multiple-choice, and generation). Our results demonstrate that +(1) MeZO significantly outperforms in-context learning and linear probing; (2) +MeZO achieves comparable performance to fine-tuning with backpropagation across +multiple tasks, with up to 12x memory reduction and up to 2x GPU-hour reduction +in our implementation; (3) MeZO is compatible with both full-parameter and +parameter-efficient tuning techniques such as LoRA and prefix tuning; (4) MeZO +can effectively optimize non-differentiable objectives (e.g., maximizing +accuracy or F1). We support our empirical findings with theoretical insights, +highlighting how adequate pre-training and task prompts enable MeZO to +fine-tune huge models, despite classical ZO analyses suggesting otherwise. +" +Do Large Language Models Know What They Don't Know?,Zhangyue Yin,http://arxiv.org/pdf/2305.18153v2.pdf,2023-05-29,['cs.cl'],2305.18153v2.pdf," Large language models (LLMs) have a wealth of knowledge that allows them to +excel in various Natural Language Processing (NLP) tasks. Current research +focuses on enhancing their performance within their existing knowledge. Despite +their vast knowledge, LLMs are still limited by the amount of information they +can accommodate and comprehend. Therefore, the ability to understand their own +limitations on the unknows, referred to as self-knowledge, is of paramount +importance. This study aims to evaluate LLMs' self-knowledge by assessing their +ability to identify unanswerable or unknowable questions. We introduce an +automated methodology to detect uncertainty in the responses of these models, +providing a novel measure of their self-knowledge. We further introduce a +unique dataset, SelfAware, consisting of unanswerable questions from five +diverse categories and their answerable counterparts. Our extensive analysis, +involving 20 LLMs including GPT-3, InstructGPT, and LLaMA, discovering an +intrinsic capacity for self-knowledge within these models. Moreover, we +demonstrate that in-context learning and instruction tuning can further enhance +this self-knowledge. Despite this promising insight, our findings also +highlight a considerable gap between the capabilities of these models and human +proficiency in recognizing the limits of their knowledge. +" +Improving CLIP Training with Language Rewrites,Lijie Fan,http://arxiv.org/pdf/2305.20088v2.pdf,2023-05-31,"['cs.cv', 'cs.cl', 'cs.lg']",2305.20088v2.pdf," Contrastive Language-Image Pre-training (CLIP) stands as one of the most +effective and scalable methods for training transferable vision models using +paired image and text data. CLIP models are trained using contrastive loss, +which typically relies on data augmentations to prevent overfitting and +shortcuts. However, in the CLIP training paradigm, data augmentations are +exclusively applied to image inputs, while language inputs remain unchanged +throughout the entire training process, limiting the exposure of diverse texts +to the same image. In this paper, we introduce Language augmented CLIP +(LaCLIP), a simple yet highly effective approach to enhance CLIP training +through language rewrites. Leveraging the in-context learning capability of +large language models, we rewrite the text descriptions associated with each +image. These rewritten texts exhibit diversity in sentence structure and +vocabulary while preserving the original key concepts and meanings. During +training, LaCLIP randomly selects either the original texts or the rewritten +versions as text augmentations for each image. Extensive experiments on CC3M, +CC12M, RedCaps and LAION-400M datasets show that CLIP pre-training with +language rewrites significantly improves the transfer performance without +computation or memory overhead during training. Specifically for ImageNet +zero-shot accuracy, LaCLIP outperforms CLIP by 8.2% on CC12M and 2.4% on +LAION-400M. Code is available at https://github.com/LijieFan/LaCLIP. +" +SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL,Ruoxi Sun,http://arxiv.org/pdf/2306.00739v3.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.db']",2306.00739v3.pdf," One impressive emergent capability of large language models (LLMs) is +generation of code, including Structured Query Language (SQL) for databases. +For the task of converting natural language text to SQL queries, Text-to-SQL, +adaptation of LLMs is of paramount importance, both in in-context learning and +fine-tuning settings, depending on the amount of adaptation data used. In this +paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on +PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is +based on an execution-based self-consistency prompting approach designed for +Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our +best knowledge is the first to outperform previous state-of-the-art with +fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the +fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying +SQL-PaLM to real-world scenarios we further evaluate its robustness on other +challenging variants of Spider and demonstrate the superior generalization +capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate +the impressive intelligent capabilities and various success enablers of +LLM-based Text-to-SQL. +" +Zero-Shot 3D Shape Correspondence,Ahmed Abdelreheem,http://arxiv.org/pdf/2306.03253v2.pdf,2023-06-05,['cs.cv'],2306.03253v2.pdf," We propose a novel zero-shot approach to computing correspondences between 3D +shapes. Existing approaches mainly focus on isometric and near-isometric shape +pairs (e.g., human vs. human), but less attention has been given to strongly +non-isometric and inter-class shape matching (e.g., human vs. cow). To this +end, we introduce a fully automatic method that exploits the exceptional +reasoning capabilities of recent foundation models in language and vision to +tackle difficult shape correspondence problems. Our approach comprises multiple +stages. First, we classify the 3D shapes in a zero-shot manner by feeding +rendered shape views to a language-vision model (e.g., BLIP2) to generate a +list of class proposals per shape. These proposals are unified into a single +class per shape by employing the reasoning capabilities of ChatGPT. Second, we +attempt to segment the two shapes in a zero-shot manner, but in contrast to the +co-segmentation problem, we do not require a mutual set of semantic regions. +Instead, we propose to exploit the in-context learning capabilities of ChatGPT +to generate two different sets of semantic regions for each shape and a +semantic mapping between them. This enables our approach to match strongly +non-isometric shapes with significant differences in geometric structure. +Finally, we employ the generated semantic mapping to produce coarse +correspondences that can further be refined by the functional maps framework to +produce dense point-to-point maps. Our approach, despite its simplicity, +produces highly plausible results in a zero-shot manner, especially between +strongly non-isometric shapes. Project webpage: +https://samir55.github.io/3dshapematch/. +" +MIMIC-IT: Multi-Modal In-Context Instruction Tuning,Bo Li,http://arxiv.org/pdf/2306.05425v1.pdf,2023-06-08,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.hc']",2306.05425v1.pdf," High-quality instructions and responses are essential for the zero-shot +performance of large language models on interactive natural language tasks. For +interactive vision-language tasks involving intricate visual scenes, a large +quantity of diverse and creative instruction-response pairs should be +imperative to tune vision-language models (VLMs). Nevertheless, the current +availability of vision-language instruction-response pairs in terms of +quantity, diversity, and creativity remains limited, posing challenges to the +generalization of interactive VLMs. Here we present MultI-Modal In-Context +Instruction Tuning (MIMIC-IT), a dataset comprising 2.8 million multimodal +instruction-response pairs, with 2.2 million unique instructions derived from +images and videos. Each pair is accompanied by multi-modal in-context +information, forming conversational contexts aimed at empowering VLMs in +perception, reasoning, and planning. The instruction-response collection +process, dubbed as Syphus, is scaled using an automatic annotation pipeline +that combines human expertise with GPT's capabilities. Using the MIMIC-IT +dataset, we train a large VLM named Otter. Based on extensive evaluations +conducted on vision-language benchmarks, it has been observed that Otter +demonstrates remarkable proficiency in multi-modal perception, reasoning, and +in-context learning. Human evaluation reveals it effectively aligns with the +user's intentions. We release the MIMIC-IT dataset, instruction-response +collection pipeline, benchmarks, and the Otter model. +" +MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification,Dequan Wang,http://arxiv.org/pdf/2306.09579v1.pdf,2023-06-16,['cs.cv'],2306.09579v1.pdf," Foundation models, often pre-trained with large-scale data, have achieved +paramount success in jump-starting various vision and language applications. +Recent advances further enable adapting foundation models in downstream tasks +efficiently using only a few training samples, e.g., in-context learning. Yet, +the application of such learning paradigms in medical image analysis remains +scarce due to the shortage of publicly accessible data and benchmarks. In this +paper, we aim at approaches adapting the foundation models for medical image +classification and present a novel dataset and benchmark for the evaluation, +i.e., examining the overall performance of accommodating the large-scale +foundation models downstream on a set of diverse real-world clinical tasks. We +collect five sets of medical imaging data from multiple institutes targeting a +variety of real-world clinical tasks (22,349 images in total), i.e., thoracic +diseases screening in X-rays, pathological lesion tissue screening, lesion +detection in endoscopy images, neonatal jaundice evaluation, and diabetic +retinopathy grading. Results of multiple baseline methods are demonstrated +using the proposed dataset from both accuracy and cost-effective perspectives. +" +JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving,Wayne Xin Zhao,http://arxiv.org/pdf/2306.11027v1.pdf,2023-06-19,"['cs.cl', 'cs.ai']",2306.11027v1.pdf," Although pre-trained language models~(PLMs) have recently advanced the +research progress in mathematical reasoning, they are not specially designed as +a capable multi-task solver, suffering from high cost for multi-task deployment +(\eg a model copy for a task) and inferior performance on complex mathematical +problems in practical applications. To address these issues, in this paper, we +propose \textbf{JiuZhang~2.0}, a unified Chinese PLM specially for multi-task +mathematical problem solving. Our idea is to maintain a moderate-sized model +and employ the \emph{cross-task knowledge sharing} to improve the model +capacity in a multi-task setting. Specially, we construct a +Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to +capture the common mathematical knowledge across tasks. For optimizing the MoE +architecture, we design \emph{multi-task continual pre-training} and +\emph{multi-task fine-tuning} strategies for multi-task adaptation. These +training strategies can effectively decompose the knowledge from the task data +and establish the cross-task sharing via expert networks. In order to further +improve the general capacity of solving different complex tasks, we leverage +large language models~(LLMs) as complementary models to iteratively refine the +generated solution by our PLM, via in-context learning. Extensive experiments +have demonstrated the effectiveness of our model. +" +A Chain of AI-based Solutions for Resolving FQNs and Fixing Syntax Errors in Partial Code,Qing Huang,http://arxiv.org/pdf/2306.11981v1.pdf,2023-06-21,['cs.se'],2306.11981v1.pdf," API documentation, technical blogs and programming Q&A sites contain numerous +partial code that can be reused in programming tasks, but often these code are +uncompilable due to unresolved names and syntax errors. To facilitate partial +code reuse, we propose the Partial Code Reuse Chain (PCR-Chain) for resolving +fully-qualified names (FQNs) and fixing last-mile syntax errors in partial code +based on a giant large language model (LLM) like ChatGPT. Methodologically, +PCR-Chain is backed up by the underlying global-level prompt architecture +(which combines three design ideas: hierarchical task breakdown, prompt +composition, and a mix of prompt-based AI and non-AI units) and the local-level +prompt design. Technically, we propose PCR-Chain, which employs in-context +learning rather than symbolic, costly training methods. Experimental results +demonstrate that in dynamically-typed languages (Python), PCR-Chain outperforms +current state-of-the-art (SOTA) 5% accuracy like RING. For statically-type +languages (Java), our approach achieves high accuracy of 80.5% in resolving +both non-FQNs and last-mile syntax errors, surpassing SOTA methods (RING) that +can only address last-mile syntax errors. The correct execution of the unit, +module, and PCR-Chain demonstrates the effectiveness of the prompt design, +composition, and architecture and opens up possibilities for building software +engineering tools based on LLMs, replacing traditional program analysis +methods. +" +Generative Multimodal Entity Linking,Senbao Shi,http://arxiv.org/pdf/2306.12725v2.pdf,2023-06-22,['cs.cl'],2306.12725v2.pdf," Multimodal Entity Linking (MEL) is the task of mapping mentions with +multimodal contexts to the referent entities from a knowledge base (e.g. +Wikipedia). Existing MEL methods mainly focus on designing complex multimodal +interaction mechanisms and require fine-tuning all model parameters, which can +be prohibitively costly and difficult to scale in the era of Large Language +Models (LLMs). In this work, we propose GEMEL, a simple yet effective +Generative Multimodal Entity Linking framework based on LLMs, which directly +generates target entity names. We keep the vision and language model frozen and +only train a feature mapper to enable cross-modality interactions. To adapt +LLMs to the MEL task, we take advantage of the emergent in-context learning +capability of LLMs by retrieving multimodal instances as demonstrations. +Extensive experiments show that, with only ~0.3% of the model parameters +fine-tuned, GEMEL achieves state-of-the-art results on two well-established MEL +datasets (7.7% accuracy gains on WikiDiverse and 8.8% accuracy gains on +WikiMEL). The performance gain stems from mitigating the popularity bias of LLM +predictions and disambiguating less common entities effectively. Further +analysis verifies the generality and scalability of GEMEL. Our approach is +compatible with any off-the-shelf language model, paving the way towards an +efficient and general solution for utilizing LLMs in the MEL task. +" +Kosmos-2: Grounding Multimodal Large Language Models to the World,Zhiliang Peng,http://arxiv.org/pdf/2306.14824v3.pdf,2023-06-26,"['cs.cl', 'cs.cv']",2306.14824v3.pdf," We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new +capabilities of perceiving object descriptions (e.g., bounding boxes) and +grounding text to the visual world. Specifically, we represent refer +expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where +object descriptions are sequences of location tokens. Together with multimodal +corpora, we construct large-scale data of grounded image-text pairs (called +GrIT) to train the model. In addition to the existing capabilities of MLLMs +(e.g., perceiving general modalities, following instructions, and performing +in-context learning), Kosmos-2 integrates the grounding capability into +downstream applications. We evaluate Kosmos-2 on a wide range of tasks, +including (i) multimodal grounding, such as referring expression comprehension, +and phrase grounding, (ii) multimodal referring, such as referring expression +generation, (iii) perception-language tasks, and (iv) language understanding +and generation. This work lays out the foundation for the development of +Embodiment AI and sheds light on the big convergence of language, multimodal +perception, action, and world modeling, which is a key step toward artificial +general intelligence. Code and pretrained models are available at +https://aka.ms/kosmos-2. +" +Supervised Pretraining Can Learn In-Context Reinforcement Learning,Jonathan N. Lee,http://arxiv.org/pdf/2306.14892v1.pdf,2023-06-26,"['cs.lg', 'cs.ai']",2306.14892v1.pdf," Large transformer models trained on diverse datasets have shown a remarkable +ability to learn in-context, achieving high few-shot performance on tasks they +were not explicitly trained to solve. In this paper, we study the in-context +learning capabilities of transformers in decision-making problems, i.e., +reinforcement learning (RL) for bandits and Markov decision processes. To do +so, we introduce and study Decision-Pretrained Transformer (DPT), a supervised +pretraining method where the transformer predicts an optimal action given a +query state and an in-context dataset of interactions, across a diverse set of +tasks. This procedure, while simple, produces a model with several surprising +capabilities. We find that the pretrained transformer can be used to solve a +range of RL problems in-context, exhibiting both exploration online and +conservatism offline, despite not being explicitly trained to do so. The model +also generalizes beyond the pretraining distribution to new tasks and +automatically adapts its decision-making strategies to unknown structure. +Theoretically, we show DPT can be viewed as an efficient implementation of +Bayesian posterior sampling, a provably sample-efficient RL algorithm. We +further leverage this connection to provide guarantees on the regret of the +in-context algorithm yielded by DPT, and prove that it can learn faster than +algorithms used to generate the pretraining data. These results suggest a +promising yet simple path towards instilling strong in-context decision-making +abilities in transformers. +" +A GPT-4 Reticular Chemist for Guiding MOF Discovery,Zhiling Zheng,http://arxiv.org/pdf/2306.14915v2.pdf,2023-06-20,"['cs.ai', 'cond-mat.mtrl-sci', 'physics.chem-ph']",2306.14915v2.pdf," We present a new framework integrating the AI model GPT-4 into the iterative +process of reticular chemistry experimentation, leveraging a cooperative +workflow of interaction between AI and a human researcher. This GPT-4 Reticular +Chemist is an integrated system composed of three phases. Each of these +utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed +instructions for chemical experimentation and the human provides feedback on +the experimental outcomes, including both success and failures, for the +in-context learning of AI in the next iteration. This iterative human-AI +interaction enabled GPT-4 to learn from the outcomes, much like an experienced +chemist, by a prompt-learning strategy. Importantly, the system is based on +natural language for both development and operation, eliminating the need for +coding skills, and thus, make it accessible to all chemists. Our collaboration +with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of +MOFs, with each synthesis fine-tuned through iterative feedback and expert +suggestions. This workflow presents a potential for broader applications in +scientific research by harnessing the capability of large language models like +GPT-4 to enhance the feasibility and efficiency of research activities. +" +Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale,Matthew Le,http://arxiv.org/pdf/2306.15687v2.pdf,2023-06-23,"['eess.as', 'cs.cl', 'cs.lg', 'cs.sd']",2306.15687v2.pdf," Large-scale generative models such as GPT and DALL-E have revolutionized the +research community. These models not only generate high fidelity outputs, but +are also generalists which can solve tasks not explicitly taught. In contrast, +speech generative models are still primitive in terms of scale and task +generalization. In this paper, we present Voicebox, the most versatile +text-guided generative model for speech at scale. Voicebox is a +non-autoregressive flow-matching model trained to infill speech, given audio +context and text, trained on over 50K hours of speech that are not filtered or +enhanced. Similar to GPT, Voicebox can perform many different tasks through +in-context learning, but is more flexible as it can also condition on future +context. Voicebox can be used for mono or cross-lingual zero-shot +text-to-speech synthesis, noise removal, content editing, style conversion, and +diverse sample generation. In particular, Voicebox outperforms the +state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs +1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to +20 times faster. Audio samples can be found in +\url{https://voicebox.metademolab.com}. +" +SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs,Lijun Yu,http://arxiv.org/pdf/2306.17842v3.pdf,2023-06-30,"['cs.cv', 'cs.cl', 'cs.mm']",2306.17842v3.pdf," In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling +frozen LLMs to perform both understanding and generation tasks involving +non-linguistic modalities such as images or videos. SPAE converts between raw +pixels and interpretable lexical tokens (or words) extracted from the LLM's +vocabulary. The resulting tokens capture both the semantic meaning and the +fine-grained details needed for visual reconstruction, effectively translating +the visual content into a language comprehensible to the LLM, and empowering it +to perform a wide array of multimodal tasks. Our approach is validated through +in-context learning experiments with frozen PaLM 2 and GPT 3.5 on a diverse set +of image understanding and generation tasks. Our method marks the first +successful attempt to enable a frozen LLM to generate image content while +surpassing state-of-the-art performance in image understanding tasks, under the +same setting, by over 25%. +" +RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models,Brandon Kynoch,http://arxiv.org/pdf/2307.02738v3.pdf,2023-07-06,"['cs.ai', 'cs.cl', 'cs.sc']",2307.02738v3.pdf," Large Language Models (LLMs) have made extraordinary progress in the field of +Artificial Intelligence and have demonstrated remarkable capabilities across a +large variety of tasks and domains. However, as we venture closer to creating +Artificial General Intelligence (AGI) systems, we recognize the need to +supplement LLMs with long-term memory to overcome the context window limitation +and more importantly, to create a foundation for sustained reasoning, +cumulative learning and long-term user interaction. In this paper we propose +RecallM, a novel architecture for providing LLMs with an adaptable and +updatable long-term memory mechanism. Unlike previous methods, the RecallM +architecture is particularly effective at belief updating and maintaining a +temporal understanding of the knowledge provided to it. We demonstrate through +various experiments the effectiveness of this architecture. Furthermore, +through our own temporal understanding and belief updating experiments, we show +that RecallM is four times more effective than using a vector database for +updating knowledge previously stored in long-term memory. We also demonstrate +that RecallM shows competitive performance on general question-answering and +in-context learning tasks. +" +One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention,Arvind Mahankali,http://arxiv.org/pdf/2307.03576v1.pdf,2023-07-07,['cs.lg'],2307.03576v1.pdf," Recent works have empirically analyzed in-context learning and shown that +transformers trained on synthetic linear regression tasks can learn to +implement ridge regression, which is the Bayes-optimal predictor, given +sufficient capacity [Aky\""urek et al., 2023], while one-layer transformers with +linear self-attention and no MLP layer will learn to implement one step of +gradient descent (GD) on a least-squares linear regression objective [von +Oswald et al., 2022]. However, the theory behind these observations remains +poorly understood. We theoretically study transformers with a single layer of +linear self-attention, trained on synthetic noisy linear regression data. +First, we mathematically show that when the covariates are drawn from a +standard Gaussian distribution, the one-layer transformer which minimizes the +pre-training loss will implement a single step of GD on the least-squares +linear regression objective. Then, we find that changing the distribution of +the covariates and weight vector to a non-isotropic Gaussian distribution has a +strong impact on the learned algorithm: the global minimizer of the +pre-training loss now implements a single step of $\textit{pre-conditioned}$ +GD. However, if only the distribution of the responses is changed, then this +does not have a large effect on the learned algorithm: even when the response +comes from a more general family of $\textit{nonlinear}$ functions, the global +minimizer of the pre-training loss still implements a single step of GD on a +least-squares linear regression objective. +" +Large Language Models as General Pattern Machines,Suvir Mirchandani,http://arxiv.org/pdf/2307.04721v2.pdf,2023-07-10,"['cs.ai', 'cs.cl', 'cs.ro']",2307.04721v2.pdf," We observe that pre-trained large language models (LLMs) are capable of +autoregressively completing complex token sequences -- from arbitrary ones +procedurally generated by probabilistic context-free grammars (PCFG), to more +rich spatial patterns found in the Abstraction and Reasoning Corpus (ARC), a +general AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern +completion proficiency can be partially retained even when the sequences are +expressed using tokens randomly sampled from the vocabulary. These results +suggest that without any additional training, LLMs can serve as general +sequence modelers, driven by in-context learning. In this work, we investigate +how these zero-shot capabilities may be applied to problems in robotics -- from +extrapolating sequences of numbers that represent states over time to complete +simple motions, to least-to-most prompting of reward-conditioned trajectories +that can discover and represent closed-loop policies (e.g., a stabilizing +controller for CartPole). While difficult to deploy today for real systems due +to latency, context size limitations, and compute costs, the approach of using +LLMs to drive low-level control may provide an exciting glimpse into how the +patterns among words could be transferred to actions. +" +Mega-TTS 2: Zero-Shot Text-to-Speech with Arbitrary Length Speech Prompts,Ziyue Jiang,http://arxiv.org/pdf/2307.07218v2.pdf,2023-07-14,"['eess.as', 'cs.sd']",2307.07218v2.pdf," Zero-shot text-to-speech aims at synthesizing voices with unseen speech +prompts. Previous large-scale multispeaker TTS models have successfully +achieved this goal with an enrolled recording within 10 seconds. However, most +of them are designed to utilize only short speech prompts. The limited +information in short speech prompts significantly hinders the performance of +fine-grained identity imitation. In this paper, we introduce Mega-TTS 2, a +generic zero-shot multispeaker TTS model that is capable of synthesizing speech +for unseen speakers with arbitrary-length prompts. Specifically, we 1) design a +multi-reference timbre encoder to extract timbre information from multiple +reference speeches; 2) and train a prosody language model with arbitrary-length +speech prompts; With these designs, our model is suitable for prompts of +different lengths, which extends the upper bound of speech quality for +zero-shot text-to-speech. Besides arbitrary-length prompts, we introduce +arbitrary-source prompts, which leverages the probabilities derived from +multiple P-LLM outputs to produce expressive and controlled prosody. +Furthermore, we propose a phoneme-level auto-regressive duration model to +introduce in-context learning capabilities to duration modeling. Experiments +demonstrate that our method could not only synthesize identity-preserving +speech with a short prompt of an unseen speaker but also achieve improved +performance with longer speech prompts. Audio samples can be found in +https://mega-tts.github.io/mega2_demo/. +" +Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study,Peiyu Liu,http://arxiv.org/pdf/2307.08072v2.pdf,2023-07-16,"['cs.cl', 'cs.ai']",2307.08072v2.pdf," Despite the superior performance, Large Language Models~(LLMs) require +significant computational resources for deployment and use. To overcome this +issue, quantization methods have been widely applied to reduce the memory +footprint of LLMs as well as increasing the inference rate. However, a major +challenge is that low-bit quantization methods often lead to performance +degradation. It is important to understand how quantization impacts the +capacity of LLMs. Different from previous studies focused on overall +performance, this work aims to investigate the impact of quantization on +\emph{emergent abilities}, which are important characteristics that distinguish +LLMs from small language models. Specially, we examine the abilities of +in-context learning, chain-of-thought reasoning, and instruction-following in +quantized LLMs. Our empirical experiments show that these emergent abilities +still exist in 4-bit quantization models, while 2-bit models encounter severe +performance degradation on the test of these abilities. To improve the +performance of low-bit models, we conduct two special experiments: (1) +fine-gained impact analysis that studies which components (or substructures) +are more sensitive to quantization, and (2) performance compensation through +model fine-tuning. Our work derives a series of important findings to +understand the impact of quantization on emergent abilities, and sheds lights +on the possibilities of extremely low-bit quantization for LLMs. +" +Generating Mathematical Derivations with Large Language Models,Jordan Meadows,http://arxiv.org/pdf/2307.09998v3.pdf,2023-07-19,"['cs.cl', 'math.ho']",2307.09998v3.pdf," The derivation of mathematical results in specialised fields, using Large +Language Models (LLMs), is an emerging research direction that can help +identify models' limitations, and potentially support mathematical discovery. +In this paper, we leverage a symbolic engine to generate derivations of +equations at scale, and investigate the capabilities of LLMs when deriving goal +equations from premises. Specifically, we employ in-context learning for GPT +and fine-tune a range of T5 models to compare the robustness and generalisation +of pre-training strategies to specialised models. Empirical results show that +fine-tuned FLAN-T5-large (MathT5) outperforms GPT models on all static and +out-of-distribution test sets in conventional scores. However, an in-depth +analysis reveals that the fine-tuned models are more sensitive to perturbations +involving unseen symbols and (to a lesser extent) changes to equation +structure. In addition, we analyse 1.7K equations, and over 200 derivations, to +highlight common reasoning errors such as the inclusion of incorrect, +irrelevant, and redundant equations. Finally, we explore the suitability of +existing metrics for evaluating mathematical derivations and find evidence +that, while they can capture general properties such as sensitivity to +perturbations, they fail to highlight fine-grained reasoning errors and +essential differences between models. Overall, this work demonstrates that +training models on synthetic data may improve their math capabilities beyond +much larger LLMs, but current metrics are not appropriately assessing the +quality of generated mathematical text. +" +LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition,Chengsong Huang,http://arxiv.org/pdf/2307.13269v1.pdf,2023-07-25,"['cs.cl', 'cs.ai']",2307.13269v1.pdf," Low-rank adaptations (LoRA) are often employed to fine-tune large language +models (LLMs) for new tasks. This paper investigates LoRA composability for +cross-task generalization and introduces LoraHub, a strategic framework devised +for the purposive assembly of LoRA modules trained on diverse given tasks, with +the objective of achieving adaptable performance on unseen tasks. With just a +few examples from a novel task, LoraHub enables the fluid combination of +multiple LoRA modules, eradicating the need for human expertise. Notably, the +composition requires neither additional model parameters nor gradients. Our +empirical results, derived from the Big-Bench Hard (BBH) benchmark, suggest +that LoraHub can effectively mimic the performance of in-context learning in +few-shot scenarios, excluding the necessity of in-context examples alongside +each inference input. A significant contribution of our research is the +fostering of a community for LoRA, where users can share their trained LoRA +modules, thereby facilitating their application to new tasks. We anticipate +this resource will widen access to and spur advancements in general +intelligence as well as LLMs in production. Code will be available at +https://github.com/sail-sg/lorahub. +" +LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation,Leigang Qu,http://arxiv.org/pdf/2308.05095v2.pdf,2023-08-09,"['cs.cv', 'cs.ai']",2308.05095v2.pdf," In the text-to-image generation field, recent remarkable progress in Stable +Diffusion makes it possible to generate rich kinds of novel photorealistic +images. However, current models still face misalignment issues (e.g., +problematic spatial relation understanding and numeration failure) in complex +natural scenes, which impedes the high-faithfulness text-to-image generation. +Although recent efforts have been made to improve controllability by giving +fine-grained guidance (e.g., sketch and scribbles), this issue has not been +fundamentally tackled since users have to provide such guidance information +manually. In this work, we strive to synthesize high-fidelity images that are +semantically aligned with a given textual prompt without any guidance. Toward +this end, we propose a coarse-to-fine paradigm to achieve layout planning and +image generation. Concretely, we first generate the coarse-grained layout +conditioned on a given textual prompt via in-context learning based on Large +Language Models. Afterward, we propose a fine-grained object-interaction +diffusion method to synthesize high-faithfulness images conditioned on the +prompt and the automatically generated layout. Extensive experiments +demonstrate that our proposed method outperforms the state-of-the-art models in +terms of layout and image generation. Our code and settings are available at +https://layoutllm-t2i.github.io. +" +AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining,Haohe Liu,http://arxiv.org/pdf/2308.05734v2.pdf,2023-08-10,"['cs.sd', 'cs.ai', 'cs.mm', 'eess.as', 'eess.sp']",2308.05734v2.pdf," Although audio generation shares commonalities across different types of +audio, such as speech, music, and sound effects, designing models for each type +requires careful consideration of specific objectives and biases that can +significantly differ from those of other types. To bring us closer to a unified +perspective of audio generation, this paper proposes a framework that utilizes +the same learning method for speech, music, and sound effect generation. Our +framework introduces a general representation of audio, called ""language of +audio"" (LOA). Any audio can be translated into LOA based on AudioMAE, a +self-supervised pre-trained representation learning model. In the generation +process, we translate any modalities into LOA by using a GPT-2 model, and we +perform self-supervised audio generation learning with a latent diffusion model +conditioned on LOA. The proposed framework naturally brings advantages such as +in-context learning abilities and reusable self-supervised pretrained AudioMAE +and latent diffusion models. Experiments on the major benchmarks of +text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art +or competitive performance against previous approaches. Our code, pretrained +model, and demo are available at https://audioldm.github.io/audioldm2. +" +Time Travel in LLMs: Tracing Data Contamination in Large Language Models,Shahriar Golchin,http://arxiv.org/pdf/2308.08493v2.pdf,2023-08-16,"['cs.cl', 'cs.cr', 'cs.lg']",2308.08493v2.pdf," Data contamination, i.e., the presence of test data from downstream tasks in +the training data of large language models (LLMs), is a potential major issue +in measuring LLMs' real effectiveness on other tasks. We propose a +straightforward yet effective method for identifying data contamination within +LLMs. At its core, our approach starts by identifying potential contamination +at the instance level; using this information, our approach then assesses wider +contamination at the partition level. To estimate contamination of individual +instances, we employ ""guided instruction:"" a prompt consisting of the dataset +name, partition type, and the random-length initial segment of a reference +instance, asking the LLM to complete it. An instance is flagged as contaminated +if the LLM's output either exactly or nearly matches the latter segment of the +reference. To understand if an entire partition is contaminated, we propose two +ideas. The first idea marks a dataset partition as contaminated if the average +overlap score with the reference instances (as measured by ROUGE-L or BLEURT) +is statistically significantly better with the completions from guided +instruction compared to a ""general instruction"" that does not include the +dataset and partition name. The second idea marks a dataset partition as +contaminated if a classifier based on GPT-4 with few-shot in-context learning +prompt marks multiple generated completions as exact/near-exact matches of the +corresponding reference instances. Our best method achieves an accuracy between +92% and 100% in detecting if an LLM is contaminated with seven datasets, +containing train and test/validation partitions, when contrasted with manual +evaluation by human experts. Further, our findings indicate that GPT-4 is +contaminated with AG News, WNLI, and XSum datasets. +" +Inductive-bias Learning: Generating Code Models with Large Language Model,Toma Tanaka,http://arxiv.org/pdf/2308.09890v1.pdf,2023-08-19,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.pl']",2308.09890v1.pdf," Large Language Models(LLMs) have been attracting attention due to a ability +called in-context learning(ICL). ICL, without updating the parameters of a LLM, +it is possible to achieve highly accurate inference based on rules ``in the +context'' by merely inputting a training data into the prompt. Although ICL is +a developing field with many unanswered questions, LLMs themselves serves as a +inference model, seemingly realizing inference without explicitly indicate +``inductive bias''. On the other hand, a code generation is also a highlighted +application of LLMs. The accuracy of code generation has dramatically improved, +enabling even non-engineers to generate code to perform the desired tasks by +crafting appropriate prompts. In this paper, we propose a novel ``learning'' +method called an ``Inductive-Bias Learning (IBL)'', which combines the +techniques of ICL and code generation. An idea of IBL is straightforward. Like +ICL, IBL inputs a training data into the prompt and outputs a code with a +necessary structure for inference (we referred to as ``Code Model'') from a +``contextual understanding''. Despite being a seemingly simple approach, IBL +encompasses both a ``property of inference without explicit inductive bias'' +inherent in ICL and a ``readability and explainability'' of the code +generation. Surprisingly, generated Code Models have been found to achieve +predictive accuracy comparable to, and in some cases surpassing, ICL and +representative machine learning models. Our IBL code is open source: +https://github.com/fuyu-quant/IBLM +" +Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models,Martin Weyssow,http://arxiv.org/pdf/2308.10462v1.pdf,2023-08-21,"['cs.se', 'cs.cl', 'cs.lg']",2308.10462v1.pdf," Large Language Models (LLMs) possess impressive capabilities to generate +meaningful code snippets given natural language intents in zero-shot, i.e., +without the need for specific fine-tuning. In the perspective of unleashing +their full potential, prior work has demonstrated the benefits of fine-tuning +the models to task-specific data. However, fine-tuning process demands heavy +computational costs and is intractable when resources are scarce, especially +for models with billions of parameters. In light of these challenges, previous +studies explored In-Context Learning (ICL) as an effective strategy to generate +contextually appropriate code without fine-tuning. However, it operates at +inference time and does not involve learning task-specific parameters, +potentially limiting the model's performance on downstream tasks. In this +context, we foresee that Parameter-Efficient Fine-Tuning (PEFT) techniques +carry a high potential for efficiently specializing LLMs to task-specific data. +In this paper, we deliver a comprehensive study of LLMs with the impact of PEFT +techniques under the automated code generation scenario. Our experimental +results reveal the superiority and potential of such techniques over ICL on a +wide range of LLMs in reducing the computational burden and improving +performance. Therefore, the study opens opportunities for broader applications +of PEFT in software engineering scenarios. +" +Analyzing Transformer Dynamics as Movement through Embedding Space,Sumeet S. Singh,http://arxiv.org/pdf/2308.10874v1.pdf,2023-08-21,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.ne']",2308.10874v1.pdf," Transformer language models exhibit intelligent behaviors such as +understanding natural language, recognizing patterns, acquiring knowledge, +reasoning, planning, reflecting and using tools. This paper explores how their +underlying mechanics give rise to intelligent behaviors. We adopt a systems +approach to analyze Transformers in detail and develop a mathematical framework +that frames their dynamics as movement through embedding space. This novel +perspective provides a principled way of thinking about the problem and reveals +important insights related to the emergence of intelligence: + 1. At its core the Transformer is a Embedding Space walker, mapping +intelligent behavior to trajectories in this vector space. + 2. At each step of the walk, it composes context into a single composite +vector whose location in Embedding Space defines the next step. + 3. No learning actually occurs during decoding; in-context learning and +generalization are simply the result of different contexts composing into +different vectors. + 4. Ultimately the knowledge, intelligence and skills exhibited by the model +are embodied in the organization of vectors in Embedding Space rather than in +specific neurons or layers. These abilities are properties of this +organization. + 5. Attention's contribution boils down to the association-bias it lends to +vector composition and which influences the aforementioned organization. +However, more investigation is needed to ascertain its significance. + 6. The entire model is composed from two principal operations: data +independent filtering and data dependent aggregation. This generalization +unifies Transformers with other sequence models and across modalities. + Building upon this foundation we formalize and test a semantic space theory +which posits that embedding vectors represent semantic concepts and find some +evidence of its validity. +" +Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: a Case Study on Hateful Memes,Yosuke Miyanishi,http://arxiv.org/pdf/2308.11585v1.pdf,2023-08-19,"['cs.ai', 'cs.cl']",2308.11585v1.pdf," In the wake of the explosive growth of machine learning (ML) usage, +particularly within the context of emerging Large Language Models (LLMs), +comprehending the semantic significance rooted in their internal workings is +crucial. While causal analyses focus on defining semantics and its +quantification, the gradient-based approach is central to explainable AI (XAI), +tackling the interpretation of the black box. By synergizing these approaches, +the exploration of how a model's internal mechanisms illuminate its causal +effect has become integral for evidence-based decision-making. A parallel line +of research has revealed that intersectionality - the combinatory impact of +multiple demographics of an individual - can be structured in the form of an +Averaged Treatment Effect (ATE). Initially, this study illustrates that the +hateful memes detection problem can be formulated as an ATE, assisted by the +principles of intersectionality, and that a modality-wise summarization of +gradient-based attention attribution scores can delineate the distinct +behaviors of three Transformerbased models concerning ATE. Subsequently, we +show that the latest LLM LLaMA2 has the ability to disentangle the +intersectional nature of memes detection in an in-context learning setting, +with their mechanistic properties elucidated via meta-gradient, a secondary +form of gradient. In conclusion, this research contributes to the ongoing +dialogue surrounding XAI and the multifaceted nature of ML models. +" +Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering,Keheng Wang,http://arxiv.org/pdf/2308.13259v2.pdf,2023-08-25,"['cs.cl', 'cs.ai']",2308.13259v2.pdf," Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown +impressive reasoning ability in various downstream tasks. Even so, suffering +from hallucinations and the inability to access external knowledge, LLMs often +come with incorrect or unfaithful intermediate reasoning steps, especially in +the context of answering knowledge-intensive tasks such as KBQA. To alleviate +this issue, we propose a framework called Knowledge-Driven Chain-of-Thought +(KD-CoT) to verify and modify reasoning traces in CoT via interaction with +external knowledge, and thus overcome the hallucinations and error propagation. +Concretely, we formulate the CoT rationale process of LLMs into a structured +multi-round QA format. In each round, LLMs interact with a QA system that +retrieves external knowledge and produce faithful reasoning traces based on +retrieved precise answers. The structured CoT reasoning of LLMs is facilitated +by our developed KBQA CoT collection, which serves as in-context learning +demonstrations and can also be utilized as feedback augmentation to train a +robust retriever. Extensive experiments on WebQSP and ComplexWebQuestion +datasets demonstrate the effectiveness of proposed KD-CoT in task-solving +reasoning generation, which outperforms the vanilla CoT ICL with an absolute +success rate of 8.0% and 5.1%. Furthermore, our proposed feedback-augmented +retriever outperforms the state-of-the-art baselines for retrieving knowledge, +achieving significant improvement in Hit and recall performance. Our code and +data are released on https://github.com/AdelWang/KD-CoT/tree/main. +" +Empowering Dynamics-aware Text-to-Video Diffusion with Large Language Models,Hao Fei,http://arxiv.org/pdf/2308.13812v1.pdf,2023-08-26,"['cs.ai', 'cs.cv']",2308.13812v1.pdf," Text-to-video (T2V) synthesis has gained increasing attention in the +community, in which the recently emerged diffusion models (DMs) have +promisingly shown stronger performance than the past approaches. While existing +state-of-the-art DMs are competent to achieve high-resolution video generation, +they may largely suffer from key limitations (e.g., action occurrence +disorders, crude video motions) with respect to the intricate temporal dynamics +modeling, one of the crux of video synthesis. In this work, we investigate +strengthening the awareness of video dynamics for DMs, for high-quality T2V +generation. Inspired by human intuition, we design an innovative dynamic scene +manager (dubbed as Dysen) module, which includes (step-1) extracting from input +text the key actions with proper time-order arrangement, (step-2) transforming +the action schedules into the dynamic scene graph (DSG) representations, and +(step-3) enriching the scenes in the DSG with sufficient and reasonable +details. Taking advantage of the existing powerful LLMs (e.g., ChatGPT) via +in-context learning, Dysen realizes (nearly) human-level temporal dynamics +understanding. Finally, the resulting video DSG with rich action scene details +is encoded as fine-grained spatio-temporal features, integrated into the +backbone T2V DM for video generating. Experiments on popular T2V datasets +suggest that our framework consistently outperforms prior arts with significant +margins, especially in the scenario with complex actions. Project page at +https://haofei.vip/Dysen-VDM +" +Identifying and Mitigating the Security Risks of Generative AI,Clark Barrett,http://arxiv.org/pdf/2308.14840v3.pdf,2023-08-28,['cs.ai'],2308.14840v3.pdf," Every major technical invention resurfaces the dual-use dilemma -- the new +technology has the potential to be used for good as well as for harm. +Generative AI (GenAI) techniques, such as large language models (LLMs) and +diffusion models, have shown remarkable capabilities (e.g., in-context +learning, code-completion, and text-to-image generation and editing). However, +GenAI can be used just as well by attackers to generate new attacks and +increase the velocity and efficacy of existing attacks. + This paper reports the findings of a workshop held at Google (co-organized by +Stanford University and the University of Wisconsin-Madison) on the dual-use +dilemma posed by GenAI. This paper is not meant to be comprehensive, but is +rather an attempt to synthesize some of the interesting findings from the +workshop. We discuss short-term and long-term goals for the community on this +topic. We hope this paper provides both a launching point for a discussion on +this important topic as well as interesting problems that the research +community can work to address. +" +AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models,Zhaopeng Gu,http://arxiv.org/pdf/2308.15366v3.pdf,2023-08-29,['cs.cv'],2308.15366v3.pdf," Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have +demonstrated the capability of understanding images and achieved remarkable +performance in various visual tasks. Despite their strong abilities in +recognizing common objects due to extensive training datasets, they lack +specific domain knowledge and have a weaker understanding of localized details +within objects, which hinders their effectiveness in the Industrial Anomaly +Detection (IAD) task. On the other hand, most existing IAD methods only provide +anomaly scores and necessitate the manual setting of thresholds to distinguish +between normal and abnormal samples, which restricts their practical +implementation. In this paper, we explore the utilization of LVLM to address +the IAD problem and propose AnomalyGPT, a novel IAD approach based on LVLM. We +generate training data by simulating anomalous images and producing +corresponding textual descriptions for each image. We also employ an image +decoder to provide fine-grained semantic and design a prompt learner to +fine-tune the LVLM using prompt embeddings. Our AnomalyGPT eliminates the need +for manual threshold adjustments, thus directly assesses the presence and +locations of anomalies. Additionally, AnomalyGPT supports multi-turn dialogues +and exhibits impressive few-shot in-context learning capabilities. With only +one normal shot, AnomalyGPT achieves the state-of-the-art performance with an +accuracy of 86.1%, an image-level AUC of 94.1%, and a pixel-level AUC of 95.3% +on the MVTec-AD dataset. Code is available at +https://github.com/CASIA-IVA-Lab/AnomalyGPT. +" +Taken out of context: On measuring situational awareness in LLMs,Lukas Berglund,http://arxiv.org/pdf/2309.00667v1.pdf,2023-09-01,"['cs.cl', 'cs.lg']",2309.00667v1.pdf," We aim to better understand the emergence of `situational awareness' in large +language models (LLMs). A model is situationally aware if it's aware that it's +a model and can recognize whether it's currently in testing or deployment. +Today's LLMs are tested for safety and alignment before they are deployed. An +LLM could exploit situational awareness to achieve a high score on safety +tests, while taking harmful actions after deployment. Situational awareness may +emerge unexpectedly as a byproduct of model scaling. One way to better foresee +this emergence is to run scaling experiments on abilities necessary for +situational awareness. As such an ability, we propose `out-of-context +reasoning' (in contrast to in-context learning). We study out-of-context +reasoning experimentally. First, we finetune an LLM on a description of a test +while providing no examples or demonstrations. At test time, we assess whether +the model can pass the test. To our surprise, we find that LLMs succeed on this +out-of-context reasoning task. Their success is sensitive to the training setup +and only works when we apply data augmentation. For both GPT-3 and LLaMA-1, +performance improves with model size. These findings offer a foundation for +further empirical study, towards predicting and potentially controlling the +emergence of situational awareness in LLMs. Code is available at: +https://github.com/AsaCooperStickland/situational-awareness-evals. +" +Business Process Text Sketch Automation Generation Using Large Language Model,Rui Zhu,http://arxiv.org/pdf/2309.01071v1.pdf,2023-09-03,['cs.cl'],2309.01071v1.pdf," Business Process Management (BPM) is gaining increasing attention as it has +the potential to cut costs while boosting output and quality. Business process +document generation is a crucial stage in BPM. However, due to a shortage of +datasets, data-driven deep learning techniques struggle to deliver the expected +results. We propose an approach to transform Conditional Process Trees (CPTs) +into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs). +The traditional prompting approach (Few-shot In-Context Learning) tries to get +the correct answer in one go, and it can find the pattern of transforming +simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy, +the traditional prompts perform weakly and with low correctness. We suggest +using this technique to break down a difficult CPT into a number of basic CPTs +and then solve each one in turn, drawing inspiration from the +divide-and-conquer strategy. We chose 100 process trees with depths ranging +from 2 to 5 at random, as well as CPTs with many nodes, many degrees of +selection, and cyclic nesting. Experiments show that our method can achieve a +correct rate of 93.42%, which is 45.17% better than traditional prompting +methods. Our proposed method provides a solution for business process document +generation in the absence of datasets, and secondly, it becomes potentially +possible to provide a large number of datasets for the process model extraction +(PME) domain. +" +Textbooks Are All You Need II: phi-1.5 technical report,Yuanzhi Li,http://arxiv.org/pdf/2309.05463v1.pdf,2023-09-11,"['cs.cl', 'cs.ai']",2309.05463v1.pdf," We continue the investigation into the power of smaller Transformer-based +language models as initiated by \textbf{TinyStories} -- a 10 million parameter +model that can produce coherent English -- and the follow-up work on +\textbf{phi-1}, a 1.3 billion parameter model with Python coding performance +close to the state-of-the-art. The latter work proposed to use existing Large +Language Models (LLMs) to generate ``textbook quality"" data as a way to enhance +the learning process compared to traditional web data. We follow the +``Textbooks Are All You Need"" approach, focusing this time on common sense +reasoning in natural language, and create a new 1.3 billion parameter model +named \textbf{phi-1.5}, with performance on natural language tasks comparable +to models 5x larger, and surpassing most non-frontier LLMs on more complex +reasoning tasks such as grade-school mathematics and basic coding. More +generally, \textbf{phi-1.5} exhibits many of the traits of much larger LLMs, +both good -- such as the ability to ``think step by step"" or perform some +rudimentary in-context learning -- and bad, including hallucinations and the +potential for toxic and biased generations -- encouragingly though, we are +seeing improvement on that front thanks to the absence of web data. We +open-source \textbf{phi-1.5} to promote further research on these urgent +topics. +" +Uncovering mesa-optimization algorithms in Transformers,Johannes von Oswald,http://arxiv.org/pdf/2309.05858v1.pdf,2023-09-11,"['cs.lg', 'cs.ai']",2309.05858v1.pdf," Transformers have become the dominant model in deep learning, but the reason +for their superior performance is poorly understood. Here, we hypothesize that +the strong performance of Transformers stems from an architectural bias towards +mesa-optimization, a learned process running within the forward pass of a model +consisting of the following two steps: (i) the construction of an internal +learning objective, and (ii) its corresponding solution found through +optimization. To test this hypothesis, we reverse-engineer a series of +autoregressive Transformers trained on simple sequence modeling tasks, +uncovering underlying gradient-based mesa-optimization algorithms driving the +generation of predictions. Moreover, we show that the learned forward-pass +optimization algorithm can be immediately repurposed to solve supervised +few-shot tasks, suggesting that mesa-optimization might underlie the in-context +learning capabilities of large language models. Finally, we propose a novel +self-attention layer, the mesa-layer, that explicitly and efficiently solves +optimization problems specified in context. We find that this layer can lead to +improved performance in synthetic and preliminary language modeling +experiments, adding weight to our hypothesis that mesa-optimization is an +important operation hidden within the weights of trained Transformers. +" +Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model,Mingxin Li,http://arxiv.org/pdf/2309.06453v1.pdf,2023-09-12,"['cs.cl', 'cs.lg']",2309.06453v1.pdf," Sentence Representation Learning (SRL) is a fundamental task in Natural +Language Processing (NLP), with Contrastive learning of Sentence Embeddings +(CSE) as the mainstream technique due to its superior performance. An +intriguing phenomenon in CSE is the significant performance gap between +supervised and unsupervised methods, even when their sentence encoder and loss +function are the same. Previous works attribute this performance gap to +differences in two representation properties (alignment and uniformity). +However, alignment and uniformity only measure the results, which means they +cannot answer ""What happens during the training process that leads to the +performance gap?"" and ""How can the performance gap be narrowed?"". In this +paper, we conduct empirical experiments to answer these ""What"" and ""How"" +questions. We first answer the ""What"" question by thoroughly comparing the +behavior of supervised and unsupervised CSE during their respective training +processes. From the comparison, We observe a significant difference in fitting +difficulty. Thus, we introduce a metric, called Fitting Difficulty Increment +(FDI), to measure the fitting difficulty gap between the evaluation dataset and +the held-out training dataset, and use the metric to answer the ""What"" +question. Then, based on the insights gained from the ""What"" question, we +tackle the ""How"" question by increasing the fitting difficulty of the training +dataset. We achieve this by leveraging the In-Context Learning (ICL) capability +of the Large Language Model (LLM) to generate data that simulates complex +patterns. By utilizing the hierarchical patterns in the LLM-generated data, we +effectively narrow the gap between supervised and unsupervised CSE. +" +Understanding Catastrophic Forgetting in Language Models via Implicit Inference,Suhas Kotha,http://arxiv.org/pdf/2309.10105v1.pdf,2023-09-18,"['cs.cl', 'cs.lg']",2309.10105v1.pdf," Fine-tuning (via methods such as instruction-tuning or reinforcement learning +from human feedback) is a crucial step in training language models to robustly +carry out tasks of interest. However, we lack a systematic understanding of the +effects of fine-tuning, particularly on tasks outside the narrow fine-tuning +distribution. In a simplified scenario, we demonstrate that improving +performance on tasks within the fine-tuning data distribution comes at the +expense of suppressing model capabilities on other tasks. This degradation is +especially pronounced for tasks ""closest"" to the fine-tuning distribution. We +hypothesize that language models implicitly infer the task of the prompt +corresponds, and the fine-tuning process predominantly skews this task +inference towards tasks in the fine-tuning distribution. To test this +hypothesis, we propose Conjugate Prompting to see if we can recover pretrained +capabilities. Conjugate prompting artificially makes the task look farther from +the fine-tuning distribution while requiring the same capability. We find that +conjugate prompting systematically recovers some of the pretraining +capabilities on our synthetic setup. We then apply conjugate prompting to +real-world LLMs using the observation that fine-tuning distributions are +typically heavily skewed towards English. We find that simply translating the +prompts to different languages can cause the fine-tuned models to respond like +their pretrained counterparts instead. This allows us to recover the in-context +learning abilities lost via instruction tuning, and more concerningly, to +recover harmful content generation suppressed by safety fine-tuning in chatbots +like ChatGPT. +" +GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models,Yonggan Fu,http://arxiv.org/pdf/2309.10730v1.pdf,2023-09-19,"['cs.lg', 'cs.ar']",2309.10730v1.pdf," The remarkable capabilities and intricate nature of Artificial Intelligence +(AI) have dramatically escalated the imperative for specialized AI +accelerators. Nonetheless, designing these accelerators for various AI +workloads remains both labor- and time-intensive. While existing design +exploration and automation tools can partially alleviate the need for extensive +human involvement, they still demand substantial hardware expertise, posing a +barrier to non-experts and stifling AI accelerator development. Motivated by +the astonishing potential of large language models (LLMs) for generating +high-quality content in response to human language instructions, we embark on +this work to examine the possibility of harnessing LLMs to automate AI +accelerator design. Through this endeavor, we develop GPT4AIGChip, a framework +intended to democratize AI accelerator design by leveraging human natural +languages instead of domain-specific languages. Specifically, we first perform +an in-depth investigation into LLMs' limitations and capabilities for AI +accelerator design, thus aiding our understanding of our current position and +garnering insights into LLM-powered automated AI accelerator design. +Furthermore, drawing inspiration from the above insights, we develop a +framework called GPT4AIGChip, which features an automated demo-augmented +prompt-generation pipeline utilizing in-context learning to guide LLMs towards +creating high-quality AI accelerator design. To our knowledge, this work is the +first to demonstrate an effective pipeline for LLM-powered automated AI +accelerator generation. Accordingly, we anticipate that our insights and +framework can serve as a catalyst for innovations in next-generation +LLM-powered design automation tools. +" +User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue,Sam Davidson,http://arxiv.org/pdf/2309.13233v1.pdf,2023-09-23,['cs.cl'],2309.13233v1.pdf," One of the major impediments to the development of new task-oriented dialogue +(TOD) systems is the need for human evaluation at multiple stages and +iterations of the development process. In an effort to move toward automated +evaluation of TOD, we propose a novel user simulator built using recently +developed large pretrained language models (LLMs). In order to increase the +linguistic diversity of our system relative to the related previous work, we do +not fine-tune the LLMs used by our system on existing TOD datasets; rather we +use in-context learning to prompt the LLMs to generate robust and +linguistically diverse output with the goal of simulating the behavior of human +interlocutors. Unlike previous work, which sought to maximize goal success rate +(GSR) as the primary metric of simulator performance, our goal is a system +which achieves a GSR similar to that observed in human interactions with TOD +systems. Using this approach, our current simulator is effectively able to +interact with several TOD systems, especially on single-intent conversational +goals, while generating lexically and syntactically diverse output relative to +previous simulators that rely upon fine-tuned models. Finally, we collect a +Human2Bot dataset of humans interacting with the same TOD systems with which we +experimented in order to better quantify these achievements. +" +A Benchmark for Learning to Translate a New Language from One Grammar Book,Garrett Tanzer,http://arxiv.org/pdf/2309.16575v1.pdf,2023-09-28,['cs.cl'],2309.16575v1.pdf," Large language models (LLMs) can perform impressive feats with in-context +learning or lightweight finetuning. It is natural to wonder how well these +models adapt to genuinely new tasks, but how does one find tasks that are +unseen in internet-scale training sets? We turn to a field that is explicitly +motivated and bottlenecked by a scarcity of web data: low-resource languages. +In this paper, we introduce MTOB (Machine Translation from One Book), a +benchmark for learning to translate between English and Kalamang -- a language +with less than 200 speakers and therefore virtually no presence on the web -- +using several hundred pages of field linguistics reference materials. This task +framing is novel in that it asks a model to learn a language from a single +human-readable book of grammar explanations, rather than a large mined corpus +of in-domain data, more akin to L2 learning than L1 acquisition. We demonstrate +that baselines using current LLMs are promising but fall short of human +performance, achieving 44.7 chrF on Kalamang to English translation and 45.8 +chrF on English to Kalamang translation, compared to 51.6 and 57.0 chrF by a +human who learned Kalamang from the same reference materials. We hope that MTOB +will help measure LLM capabilities along a new dimension, and that the methods +developed to solve it could help expand access to language technology for +underserved communities by leveraging qualitatively different kinds of data +than traditional machine translation. +" +Benchmarking Cognitive Biases in Large Language Models as Evaluators,Ryan Koo,http://arxiv.org/pdf/2309.17012v1.pdf,2023-09-29,"['cs.cl', 'cs.ai', 'cs.lg']",2309.17012v1.pdf," Large Language Models (LLMs) have recently been shown to be effective as +automatic evaluators with simple prompting and in-context learning. In this +work, we assemble 15 LLMs of four different size ranges and evaluate their +output responses by preference ranking from the other LLMs as evaluators, such +as System Star is better than System Square. We then evaluate the quality of +ranking outputs introducing the Cognitive Bias Benchmark for LLMs as Evaluators +(CoBBLEr), a benchmark to measure six different cognitive biases in LLM +evaluation outputs, such as the Egocentric bias where a model prefers to rank +its own outputs highly in evaluation. We find that LLMs are biased text quality +evaluators, exhibiting strong indications on our bias benchmark (average of 40% +of comparisons across all models) within each of their evaluations that +question their robustness as evaluators. Furthermore, we examine the +correlation between human and machine preferences and calculate the average +Rank-Biased Overlap (RBO) score to be 49.6%, indicating that machine +preferences are misaligned with humans. According to our findings, LLMs may +still be unable to be utilized for automatic annotation aligned with human +preferences. Our project page is at: https://minnesotanlp.github.io/cobbler. +" +Fewer-token Neural Speech Codec with Time-invariant Codes,Yong Ren,http://arxiv.org/pdf/2310.00014v1.pdf,2023-09-15,"['cs.sd', 'eess.as']",2310.00014v1.pdf," Language model based text-to-speech (TTS) models, like VALL-E, have gained +attention for their outstanding in-context learning capability in zero-shot +scenarios. Neural speech codec is a critical component of these models, which +can convert speech into discrete token representations. However, excessive +token sequences from the codec may negatively affect prediction accuracy and +restrict the progression of Language model based TTS models. To address this +issue, this paper proposes a novel neural speech codec with time-invariant +codes named TiCodec. By encoding and quantizing time-invariant information into +a separate code, TiCodec can reduce the amount of frame-level information that +needs encoding, effectively decreasing the number of tokens as codes of speech. +Furthermore, this paper introduces a time-invariant encoding consistency loss +to enhance the consistency of time-invariant code within an utterance and force +it to capture more global information, which can benefit the zero-shot TTS +task. Experimental results demonstrate that TiCodec can not only enhance the +quality of reconstruction speech with fewer tokens but also increase the +similarity and naturalness, as well as reduce the word error rate of the +synthesized speech by the TTS model. +" +ReAcTable: Enhancing ReAct for Table Question Answering,Yunjia Zhang,http://arxiv.org/pdf/2310.00815v1.pdf,2023-10-01,['cs.db'],2310.00815v1.pdf," Table Question Answering (TQA) presents a substantial challenge at the +intersection of natural language processing and data analytics. This task +involves answering natural language (NL) questions on top of tabular data, +demanding proficiency in logical reasoning, understanding of data semantics, +and fundamental analytical capabilities. Due to its significance, a substantial +volume of research has been dedicated to exploring a wide range of strategies +aimed at tackling this challenge including approaches that leverage Large +Language Models (LLMs) through in-context learning or Chain-of-Thought (CoT) +prompting as well as approaches that train and fine-tune custom models. + Nonetheless, a conspicuous gap exists in the research landscape, where there +is limited exploration of how innovative foundational research, which +integrates incremental reasoning with external tools in the context of LLMs, as +exemplified by the ReAct paradigm, could potentially bring advantages to the +TQA task. In this paper, we aim to fill this gap, by introducing ReAcTable +(ReAct for Table Question Answering tasks), a framework inspired by the ReAct +paradigm that is carefully enhanced to address the challenges uniquely +appearing in TQA tasks such as interpreting complex data semantics, dealing +with errors generated by inconsistent data and generating intricate data +transformations. ReAcTable relies on external tools such as SQL and Python code +executors, to progressively enhance the data by generating intermediate data +representations, ultimately transforming it into a more accessible format for +answering the questions with greater ease. We demonstrate that ReAcTable +achieves remarkable performance even when compared to fine-tuned approaches. In +particular, it outperforms the best prior result on the WikiTQ benchmark, +achieving an accuracy of 68.0% without requiring training a new model or +fine-tuning. +" +GraphText: Graph Reasoning in Text Space,Jianan Zhao,http://arxiv.org/pdf/2310.01089v1.pdf,2023-10-02,"['cs.cl', 'cs.lg']",2310.01089v1.pdf," Large Language Models (LLMs) have gained the ability to assimilate human +knowledge and facilitate natural language interactions with both humans and +other LLMs. However, despite their impressive achievements, LLMs have not made +significant advancements in the realm of graph machine learning. This +limitation arises because graphs encapsulate distinct relational data, making +it challenging to transform them into natural language that LLMs understand. In +this paper, we bridge this gap with a novel framework, GraphText, that +translates graphs into natural language. GraphText derives a graph-syntax tree +for each graph that encapsulates both the node attributes and inter-node +relationships. Traversal of the tree yields a graph text sequence, which is +then processed by an LLM to treat graph tasks as text generation tasks. +Notably, GraphText offers multiple advantages. It introduces training-free +graph reasoning: even without training on graph data, GraphText with ChatGPT +can achieve on par with, or even surpassing, the performance of +supervised-trained graph neural networks through in-context learning (ICL). +Furthermore, GraphText paves the way for interactive graph reasoning, allowing +both humans and LLMs to communicate with the model seamlessly using natural +language. These capabilities underscore the vast, yet-to-be-explored potential +of LLMs in the domain of graph machine learning. +" +LLMParser: A LLM-based Log Parsing Framework,Zhihan Jiang,http://arxiv.org/pdf/2310.01796v1.pdf,2023-10-03,['cs.se'],2310.01796v1.pdf," The process of log parsing, which converts log messages into structured +formats, is a crucial step for various log analysis tasks. Although numerous +log parsers have been proposed, their effectiveness on complex log data is +often hindered due to reliance on human-made rules or learning-based models +with limited training data. The recent rise of powerful large language models +(LLMs) shows potential for log parsing due to their extensive pre-trained +knowledge related to code and logging. However, their accuracy is currently +limited due to the lack of specialized log parsing capabilities. Additionally, +the inconsistency of their answers and significant overhead obstruct the +practical implementation of LLM-based log parsing. + To tackle these challenges, we introduce LLMParser, the first practical +LLM-based log parsing framework. LLMParser enables accurate and robust log +parsing by leveraging the in-context learning (ICL) capability of the LLM, +employing a hierarchical candidate sampling algorithm, and selecting +high-quality demonstrations. LLMParser also includes a novel adaptive parsing +cache component to store and refine the templates generated by the LLM. This +design aids in addressing the inefficiency of LLMs by rapid matching to +previously parsed log templates. LLMParser also adaptively updates the +templates in the parsing cache to ensure consistent parsed results. Extensive +evaluation on large-scale public datasets demonstrates that LLMParser surpasses +the state-of-the-art methods. Furthermore, LLMParser significantly reduces the +query times to LLMs, achieving efficiency comparable to the most efficient +baseline, Drain. +" +Uncovering hidden geometry in Transformers via disentangling position and context,Jiajun Song,http://arxiv.org/pdf/2310.04861v1.pdf,2023-10-07,"['cs.lg', 'cs.ai', 'stat.ml']",2310.04861v1.pdf," Transformers are widely used to extract complex semantic meanings from input +tokens, yet they usually operate as black-box models. In this paper, we present +a simple yet informative decomposition of hidden states (or embeddings) of +trained transformers into interpretable components. For any layer, embedding +vectors of input sequence samples are represented by a tensor $\boldsymbol{h} +\in \mathbb{R}^{C \times T \times d}$. Given embedding vector +$\boldsymbol{h}_{c,t} \in \mathbb{R}^d$ at sequence position $t \le T$ in a +sequence (or context) $c \le C$, extracting the mean effects yields the +decomposition \[ \boldsymbol{h}_{c,t} = \boldsymbol{\mu} + \mathbf{pos}_t + +\mathbf{ctx}_c + \mathbf{resid}_{c,t} \] where $\boldsymbol{\mu}$ is the global +mean vector, $\mathbf{pos}_t$ and $\mathbf{ctx}_c$ are the mean vectors across +contexts and across positions respectively, and $\mathbf{resid}_{c,t}$ is the +residual vector. For popular transformer architectures and diverse text +datasets, empirically we find pervasive mathematical structure: (1) +$(\mathbf{pos}_t)_{t}$ forms a low-dimensional, continuous, and often spiral +shape across layers, (2) $(\mathbf{ctx}_c)_c$ shows clear cluster structure +that falls into context topics, and (3) $(\mathbf{pos}_t)_{t}$ and +$(\mathbf{ctx}_c)_c$ are mutually incoherent -- namely $\mathbf{pos}_t$ is +almost orthogonal to $\mathbf{ctx}_c$ -- which is canonical in compressed +sensing and dictionary learning. This decomposition offers structural insights +about input formats in in-context learning (especially for induction heads) and +in arithmetic tasks. +" +Lightweight In-Context Tuning for Multimodal Unified Models,Yixin Chen,http://arxiv.org/pdf/2310.05109v1.pdf,2023-10-08,['cs.cv'],2310.05109v1.pdf," In-context learning (ICL) involves reasoning from given contextual examples. +As more modalities comes, this procedure is becoming more challenging as the +interleaved input modalities convolutes the understanding process. This is +exemplified by the observation that multimodal models often struggle to +effectively extrapolate from contextual examples to perform ICL. To address +these challenges, we introduce MultiModal In-conteXt Tuning (M$^2$IXT), a +lightweight module to enhance the ICL capabilities of multimodal unified +models. The proposed M$^2$IXT module perceives an expandable context window to +incorporate various labeled examples of multiple modalities (e.g., text, image, +and coordinates). It can be prepended to various multimodal unified models +(e.g., OFA, Unival, LLaVA) of different architectures and trained via a +mixed-tasks strategy to enable rapid few-shot adaption on multiple tasks and +datasets. When tuned on as little as 50K multimodal data, M$^2$IXT can boost +the few-shot ICL performance significantly (e.g., 18\% relative increase for +OFA), and obtained state-of-the-art results across an array of tasks including +visual question answering, image captioning, visual grounding, and visual +entailment, while being considerably small in terms of model parameters (e.g., +$\sim$$20\times$ smaller than Flamingo or MMICL), highlighting the flexibility +and effectiveness of M$^2$IXT as a multimodal in-context learner. +" +Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models,Haoran Wang,http://arxiv.org/pdf/2310.05253v2.pdf,2023-10-08,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05253v2.pdf," Claim verification plays a crucial role in combating misinformation. While +existing works on claim verification have shown promising results, a crucial +piece of the puzzle that remains unsolved is to understand how to verify claims +without relying on human-annotated data, which is expensive to create at a +large scale. Additionally, it is important for models to provide comprehensive +explanations that can justify their decisions and assist human fact-checkers. +This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK) +Reasoning that can verify complex claims and generate explanations without the +need for annotated evidence using Large Language Models (LLMs). FOLK leverages +the in-context learning ability of LLMs to translate the claim into a +First-Order-Logic (FOL) clause consisting of predicates, each corresponding to +a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning +over a set of knowledge-grounded question-and-answer pairs to make veracity +predictions and generate explanations to justify its decision-making process. +This process makes our model highly explanatory, providing clear explanations +of its reasoning process in human-readable form. Our experiment results +indicate that FOLK outperforms strong baselines on three datasets encompassing +various claim verification challenges. Our code and data are available. +" +Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models,Marco Bronzini,http://arxiv.org/pdf/2310.05628v2.pdf,2023-10-09,"['cs.cl', 'cs.ce', 'cs.cy']",2310.05628v2.pdf," Over the last decade, several regulatory bodies have started requiring the +disclosure of non-financial information from publicly listed companies, in +light of the investors' increasing attention to Environmental, Social, and +Governance (ESG) issues. Such information is publicly released in a variety of +non-structured and multi-modal documentation. Hence, it is not straightforward +to aggregate and consolidate such data in a cohesive framework to further +derive insights about sustainability practices across companies and markets. +Given these premises, it is natural to resort to Information Extraction (IE) +techniques to provide concise, informative, and actionable data to the +stakeholders. Moving beyond traditional text processing techniques, in this +work we leverage Large Language Models (LLMs), along with the prominent +in-context learning technique and the Retrieved Augmented Generation (RAG) +paradigm, to extract semantically structured ESG-related information from +companies' sustainability reports. We then adopt graph-based representations to +conduct meaningful statistical, similarity and correlation analyses concerning +the ESG-related actions disclosed by companies in their sustainability reports. +These analyses unveiled that companies address ESG-related issues through +several actions encompassing recognition, compliance, and partnerships; +highlighting the complexity and joint efforts needed to address them. Moreover, +disclosure similarities emerged among companies from the same region or sector. +Lastly, we investigate which factual aspects impact the most on companies' ESG +scores using our findings and other company information. This analysis unveiled +that companies' disclosures affect ESG scores more than other financial or +company characteristics. +" +Are Large Language Models Post Hoc Explainers?,Nicholas Kroeger,http://arxiv.org/pdf/2310.05797v2.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05797v2.pdf," Large Language Models (LLMs) are increasingly used as powerful tools for a +plethora of natural language processing (NLP) applications. A recent +innovation, in-context learning (ICL), enables LLMs to learn new tasks by +supplying a few examples in the prompt during inference time, thereby +eliminating the need for model fine-tuning. While LLMs have been utilized in +several applications, their applicability in explaining the behavior of other +models remains relatively unexplored. Despite the growing number of new +explanation techniques, many require white-box access to the model and/or are +computationally expensive, highlighting a need for next-generation post hoc +explainers. In this work, we present the first framework to study the +effectiveness of LLMs in explaining other predictive models. More specifically, +we propose a novel framework encompassing multiple prompting strategies: i) +Perturbation-based ICL, ii) Prediction-based ICL, iii) Instruction-based ICL, +and iv) Explanation-based ICL, with varying levels of information about the +underlying ML model and the local neighborhood of the test sample. We conduct +extensive experiments with real-world benchmark datasets to demonstrate that +LLM-generated explanations perform on par with state-of-the-art post hoc +explainers using their ability to leverage ICL examples and their internal +knowledge in generating model explanations. On average, across four datasets +and two ML models, we observe that LLMs identify the most important feature +with 72.19% accuracy, opening up new frontiers in explainable artificial +intelligence (XAI) to explore LLM-based explanation frameworks. +" +SALMON: Self-Alignment with Principle-Following Reward Models,Zhiqing Sun,http://arxiv.org/pdf/2310.05910v1.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05910v1.pdf," Supervised Fine-Tuning (SFT) on response demonstrations combined with +Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful +paradigm for aligning LLM-based AI agents. However, a significant limitation of +such an approach is its dependency on high-quality human annotations, making +its application to intricate tasks challenging due to difficulties in obtaining +consistent response demonstrations and in-distribution response preferences. +This paper presents a novel approach, namely SALMON (Self-ALignMent with +principle-fOllowiNg reward models), to align base language models with minimal +human supervision, using only a small set of human-defined principles, yet +achieving superior performance. Central to our approach is a +principle-following reward model. Trained on synthetic preference data, this +model can generate reward scores based on arbitrary human-defined principles. +By merely adjusting these principles during the RL training phase, we gain full +control over the preferences with the reward model, subsequently influencing +the behavior of the RL-trained policies, and eliminating the reliance on the +collection of online human preferences. Applying our method to the LLaMA-2-70b +base language model, we developed an AI assistant named Dromedary-2. With only +6 exemplars for in-context learning and 31 human-defined principles, +Dromedary-2 significantly surpasses the performance of several state-of-the-art +AI systems, including LLaMA-2-Chat-70b, on various benchmark datasets. We have +open-sourced the code and model weights to encourage further research into +aligning LLM-based AI agents with enhanced supervision efficiency, improved +controllability, and scalable oversight. +" +OpsEval: A Comprehensive Task-Oriented AIOps Benchmark for Large Language Models,Yuhe Liu,http://arxiv.org/pdf/2310.07637v2.pdf,2023-10-11,"['cs.ai', 'cs.ni']",2310.07637v2.pdf," Large language models (LLMs) have exhibited remarkable capabilities in +NLP-related tasks such as translation, summarizing, and generation. The +application of LLMs in specific areas, notably AIOps (Artificial Intelligence +for IT Operations), holds great potential due to their advanced abilities in +information summarizing, report analyzing, and ability of API calling. +Nevertheless, the performance of current LLMs in AIOps tasks is yet to be +determined. Furthermore, a comprehensive benchmark is required to steer the +optimization of LLMs tailored for AIOps. Compared with existing benchmarks that +focus on evaluating specific fields like network configuration, in this paper, +we present \textbf{OpsEval}, a comprehensive task-oriented AIOps benchmark +designed for LLMs. For the first time, OpsEval assesses LLMs' proficiency in +three crucial scenarios (Wired Network Operation, 5G Communication Operation, +and Database Operation) at various ability levels (knowledge recall, analytical +thinking, and practical application). The benchmark includes 7,200 questions in +both multiple-choice and question-answer (QA) formats, available in English and +Chinese. With quantitative and qualitative results, we show how various LLM +tricks can affect the performance of AIOps, including zero-shot, +chain-of-thought, and few-shot in-context learning. We find that GPT4-score is +more consistent with experts than widely used Bleu and Rouge, which can be used +to replace automatic metrics for large-scale qualitative evaluations. +" +EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation,Wang You,http://arxiv.org/pdf/2310.08185v1.pdf,2023-10-12,"['cs.cl', 'cs.ai']",2310.08185v1.pdf," Plan-and-Write is a common hierarchical approach in long-form narrative text +generation, which first creates a plan to guide the narrative writing. +Following this approach, several studies rely on simply prompting large +language models for planning, which often yields suboptimal results. In this +paper, we propose a new framework called Evaluation-guided Iterative Plan +Extraction for long-form narrative text generation (EIPE-text), which extracts +plans from the corpus of narratives and utilizes the extracted plans to +construct a better planner. EIPE-text has three stages: plan extraction, +learning, and inference. In the plan extraction stage, it iteratively extracts +and improves plans from the narrative corpus and constructs a plan corpus. We +propose a question answer (QA) based evaluation mechanism to automatically +evaluate the plans and generate detailed plan refinement instructions to guide +the iterative improvement. In the learning stage, we build a better planner by +fine-tuning with the plan corpus or in-context learning with examples in the +plan corpus. Finally, we leverage a hierarchical approach to generate long-form +narratives. We evaluate the effectiveness of EIPE-text in the domains of novels +and storytelling. Both GPT-4-based evaluations and human evaluations +demonstrate that our method can generate more coherent and relevant long-form +narratives. Our code will be released in the future. +" +Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation,Yuanyuan Liang,http://arxiv.org/pdf/2310.08395v3.pdf,2023-10-12,"['cs.cl', 'cs.ai']",2310.08395v3.pdf," The task of Question Generation over Knowledge Bases (KBQG) aims to convert a +logical form into a natural language question. For the sake of expensive cost +of large-scale question annotation, the methods of KBQG under low-resource +scenarios urgently need to be developed. However, current methods heavily rely +on annotated data for fine-tuning, which is not well-suited for few-shot +question generation. The emergence of Large Language Models (LLMs) has shown +their impressive generalization ability in few-shot tasks. Inspired by +Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for +reasoning, we formulate KBQG task as a reasoning problem, where the generation +of a complete question is splitted into a series of sub-question generation. +Our proposed prompting method KQG-CoT first retrieves supportive logical forms +from the unlabeled data pool taking account of the characteristics of the +logical form. Then, we write a prompt to explicit the reasoning chain of +generating complicated questions based on the selected demonstrations. To +further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the +logical forms by their complexity. We conduct extensive experiments over three +public KBQG datasets. The results demonstrate that our prompting method +consistently outperforms other prompting baselines on the evaluated datasets. +Remarkably, our KQG-CoT+ method could surpass existing few-shot SoTA results of +the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4, +METEOR, and ROUGE-L, respectively. +" +Do pretrained Transformers Really Learn In-context by Gradient Descent?,Lingfeng Shen,http://arxiv.org/pdf/2310.08540v1.pdf,2023-10-12,"['cs.cl', 'cs.ai', 'cs.lg']",2310.08540v1.pdf," Is In-Context Learning (ICL) implicitly equivalent to Gradient Descent (GD)? +Several recent works draw analogies between the dynamics of GD and the emergent +behavior of ICL in large language models. However, these works make assumptions +far from the realistic natural language setting in which language models are +trained. Such discrepancies between theory and practice, therefore, necessitate +further investigation to validate their applicability. + We start by highlighting the weaknesses in prior works that construct +Transformer weights to simulate gradient descent. Their experiments with +training Transformers on ICL objective, inconsistencies in the order +sensitivity of ICL and GD, sparsity of the constructed weights, and sensitivity +to parameter changes are some examples of a mismatch from the real-world +setting. + Furthermore, we probe and compare the ICL vs. GD hypothesis in a natural +setting. We conduct comprehensive empirical analyses on language models +pretrained on natural data (LLaMa-7B). Our comparisons on various performance +metrics highlight the inconsistent behavior of ICL and GD as a function of +various factors such as datasets, models, and number of demonstrations. We +observe that ICL and GD adapt the output distribution of language models +differently. These results indicate that the equivalence between ICL and GD is +an open hypothesis, requires nuanced considerations and calls for further +studies. +" +Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning,Jiachen Li,http://arxiv.org/pdf/2310.09676v1.pdf,2023-10-14,"['cs.ro', 'cs.ai']",2310.09676v1.pdf," Prompt-based learning has been demonstrated as a compelling paradigm +contributing to large language models' tremendous success (LLMs). Inspired by +their success in language tasks, existing research has leveraged LLMs in +embodied instruction following and task planning. However, not much attention +has been paid to embodied tasks with multimodal prompts, combining vision +signals with text descriptions. This type of task poses a major challenge to +robots' capability to understand the interconnection and complementarity +between vision and language signals. In this work, we introduce an effective +framework that learns a policy to perform robot manipulation with multimodal +prompts from multi-task expert trajectories. Our methods consist of a two-stage +training pipeline that performs inverse dynamics pretraining and multi-task +finetuning. To facilitate multimodal understanding, we design our multimodal +prompt encoder by augmenting a pretrained LM with a residual connection to the +visual input and model the dependencies among action dimensions. Empirically, +we evaluate the efficacy of our method on the VIMA-BENCH and establish a new +state-of-the-art (10% improvement in success rate). Moreover, we demonstrate +that our model exhibits remarkable in-context learning ability. +" +Unifying Image Processing as Visual Prompting Question Answering,Yihao Liu,http://arxiv.org/pdf/2310.10513v1.pdf,2023-10-16,"['cs.cv', 'eess.iv']",2310.10513v1.pdf," Image processing is a fundamental task in computer vision, which aims at +enhancing image quality and extracting essential features for subsequent vision +applications. Traditionally, task-specific models are developed for individual +tasks and designing such models requires distinct expertise. Building upon the +success of large language models (LLMs) in natural language processing (NLP), +there is a similar trend in computer vision, which focuses on developing +large-scale models through pretraining and in-context learning. This paradigm +shift reduces the reliance on task-specific models, yielding a powerful unified +model to deal with various tasks. However, these advances have predominantly +concentrated on high-level vision tasks, with less attention paid to low-level +vision tasks. To address this issue, we propose a universal model for general +image processing that covers image restoration, image enhancement, image +feature extraction tasks, \textit{etc}. Our proposed framework, named +PromptGIP, unifies these diverse image processing tasks within a universal +framework. Inspired by NLP question answering (QA) techniques, we employ a +visual prompting question answering paradigm. Specifically, we treat the +input-output image pair as a structured question-answer sentence, thereby +reprogramming the image processing task as a prompting QA problem. PromptGIP +can undertake diverse \textbf{cross-domain} tasks using provided visual +prompts, eliminating the need for task-specific finetuning. Our methodology +offers a universal and adaptive solution to general image processing. While +PromptGIP has demonstrated a certain degree of out-of-domain task +generalization capability, further research is expected to fully explore its +more powerful emergent generalization. +" +In-Context Pretraining: Language Modeling Beyond Document Boundaries,Weijia Shi,http://arxiv.org/pdf/2310.10638v3.pdf,2023-10-16,"['cs.cl', 'cs.ai', 'cs.lg']",2310.10638v3.pdf," Large language models (LMs) are currently trained to predict tokens given +document prefixes, enabling them to directly perform long-form generation and +prompting-style tasks which can be reduced to document completion. Existing +pretraining pipelines train LMs by concatenating random sets of short documents +to create input contexts but the prior documents provide no signal for +predicting the next document. We instead present In-Context Pretraining, a new +approach where language models are pretrained on a sequence of related +documents, thereby explicitly encouraging them to read and reason across +document boundaries. We can do In-Context Pretraining by simply changing the +document ordering so that each context contains related documents, and directly +applying existing pretraining pipelines. However, this document sorting problem +is challenging. There are billions of documents and we would like the sort to +maximize contextual similarity for every document without repeating any data. +To do this, we introduce approximate algorithms for finding related documents +with efficient nearest neighbor search and constructing coherent input contexts +with a graph traversal algorithm. Our experiments show In-Context Pretraining +offers a simple and scalable approach to significantly enhance LMs'performance: +we see notable improvements in tasks that require more complex contextual +reasoning, including in-context learning (+8%), reading comprehension (+15%), +faithfulness to previous contexts (+16%), long-context reasoning (+5%), and +retrieval augmentation (+9%). +" +IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models,Shaokun Zhang,http://arxiv.org/pdf/2310.10873v1.pdf,2023-10-16,['cs.cl'],2310.10873v1.pdf," In-context learning is a promising paradigm that utilizes in-context examples +as prompts for the predictions of large language models. These prompts are +crucial for achieving strong performance. However, since the prompts need to be +sampled from a large volume of annotated examples, finding the right prompt may +result in high annotation costs. To address this challenge, this paper +introduces an influence-driven selective annotation method that aims to +minimize annotation costs while improving the quality of in-context examples. +The essence of our method is to select a pivotal subset from a large-scale +unlabeled data pool to annotate for the subsequent sampling of prompts. +Specifically, a directed graph is first constructed to represent unlabeled +data. Afterward, the influence of candidate unlabeled subsets is quantified +with a diffusion process. A simple yet effective greedy algorithm for unlabeled +data selection is lastly introduced. It iteratively selects the data if it +provides a maximum marginal gain with respect to quantified influence. Compared +with previous efforts on selective annotations, our influence-driven method +works in an end-to-end manner, avoids an intractable explicit balance between +data diversity and representativeness, and enjoys theoretical support. +Experiments confirm the superiority of the proposed method on various +benchmarks, achieving better performance under lower time consumption during +subset selection. The project page is available at +https://skzhang1.github.io/IDEAL/. +" +Eureka: Human-Level Reward Design via Coding Large Language Models,Yecheng Jason Ma,http://arxiv.org/pdf/2310.12931v1.pdf,2023-10-19,"['cs.ro', 'cs.ai', 'cs.lg']",2310.12931v1.pdf," Large Language Models (LLMs) have excelled as high-level semantic planners +for sequential decision-making tasks. However, harnessing them to learn complex +low-level manipulation tasks, such as dexterous pen spinning, remains an open +problem. We bridge this fundamental gap and present Eureka, a human-level +reward design algorithm powered by LLMs. Eureka exploits the remarkable +zero-shot generation, code-writing, and in-context improvement capabilities of +state-of-the-art LLMs, such as GPT-4, to perform evolutionary optimization over +reward code. The resulting rewards can then be used to acquire complex skills +via reinforcement learning. Without any task-specific prompting or pre-defined +reward templates, Eureka generates reward functions that outperform expert +human-engineered rewards. In a diverse suite of 29 open-source RL environments +that include 10 distinct robot morphologies, Eureka outperforms human experts +on 83% of the tasks, leading to an average normalized improvement of 52%. The +generality of Eureka also enables a new gradient-free in-context learning +approach to reinforcement learning from human feedback (RLHF), readily +incorporating human inputs to improve the quality and the safety of the +generated rewards without model updating. Finally, using Eureka rewards in a +curriculum learning setting, we demonstrate for the first time, a simulated +Shadow Hand capable of performing pen spinning tricks, adeptly manipulating a +pen in circles at rapid speed. +" +Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning,Jinyuan Wang,http://arxiv.org/pdf/2310.13552v2.pdf,2023-10-20,"['cs.cl', 'cs.ai']",2310.13552v2.pdf," In open-domain question-answering (ODQA), most existing questions require +single-hop reasoning on commonsense. To further extend this task, we officially +introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop +questions with explicit reasoning steps in open-domain setting. Recently, large +language models (LLMs) have found significant utility in facilitating ODQA +without external corpus. Furthermore, chain-of-thought (CoT) prompting boosts +the reasoning capability of LLMs to a greater extent with manual or automated +paradigms. However, existing automated methods lack of quality assurance, while +manual approaches suffer from limited scalability and poor diversity, hindering +the capabilities of LLMs. In this paper, we propose Self-prompted +Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality +CoTs of LLMs, by LLMs and for LLMs. SP-CoT introduces an automated generation +pipeline of high quality ODMR datasets, an adaptive sampler for in-context CoT +selection and self-prompted inference via in-context learning. Extensive +experiments on four multi-hop question-answering benchmarks show that our +proposed SP-CoT not only significantly surpasses the previous SOTA methods on +large-scale (175B) LLMs, but also nearly doubles the zero-shot performance of +small-scale (13B) LLMs. Further analysis reveals the remarkable capability of +SP-CoT to elicit direct and concise intermediate reasoning steps by recalling +$\sim$50\% of intermediate answers on MuSiQue-Ans dataset. +" +Explainable Depression Symptom Detection in Social Media,Eliseo Bao Souto,http://arxiv.org/pdf/2310.13664v2.pdf,2023-10-20,['cs.cl'],2310.13664v2.pdf," Users of social platforms often perceive these sites as supportive spaces to +post about their mental health issues. Those conversations contain important +traces about individuals' health risks. Recently, researchers have exploited +this online information to construct mental health detection models, which aim +to identify users at risk on platforms like Twitter, Reddit or Facebook. Most +of these models are centred on achieving good classification results, ignoring +the explainability and interpretability of the decisions. Recent research has +pointed out the importance of using clinical markers, such as the use of +symptoms, to improve trust in the computational models by health professionals. +In this paper, we propose using transformer-based architectures to detect and +explain the appearance of depressive symptom markers in the users' writings. We +present two approaches: i) train a model to classify, and another one to +explain the classifier's decision separately and ii) unify the two tasks +simultaneously using a single model. Additionally, for this latter manner, we +also investigated the performance of recent conversational LLMs when using +in-context learning. Our natural language explanations enable clinicians to +interpret the models' decisions based on validated symptoms, enhancing trust in +the automated process. We evaluate our approach using recent symptom-based +datasets, employing both offline and expert-in-the-loop metrics to assess the +quality of the explanations generated by our models. The experimental results +show that it is possible to achieve good classification results while +generating interpretable symptom-based explanations. +" +Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs,Young-Suk Lee,http://arxiv.org/pdf/2310.13961v1.pdf,2023-10-21,"['cs.cl', 'cs.ai']",2310.13961v1.pdf," Using in-context learning (ICL) for data generation, techniques such as +Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023) +can train strong conversational agents with only a small amount of human +supervision. One limitation of these approaches is that they resort to very +large language models (around 175B parameters) that are also proprietary and +non-public. Here we explore the application of such techniques to language +models that are much smaller (around 10B--40B parameters) and have permissive +licenses. We find the Self-Instruct approach to be less effective at these +sizes and propose new ICL methods that draw on two main ideas: (a) +Categorization and simplification of the ICL templates to make prompt learning +easier for the LM, and (b) Ensembling over multiple LM outputs to help select +high-quality synthetic examples. Our algorithm leverages the 175 Self-Instruct +seed tasks and employs separate pipelines for instructions that require an +input and instructions that do not. Empirical investigations with different LMs +show that: (1) Our proposed method yields higher-quality instruction tuning +data than Self-Instruct, (2) It improves performances of both vanilla and +instruction-tuned LMs by significant margins, and (3) Smaller instruction-tuned +LMs generate more useful outputs than their larger un-tuned counterparts. Our +codebase is available at https://github.com/IBM/ensemble-instruct. +" +Investigating the Fairness of Large Language Models for Predictions on Tabular Data,Yanchen Liu,http://arxiv.org/pdf/2310.14607v1.pdf,2023-10-23,"['cs.cl', 'cs.lg']",2310.14607v1.pdf," Recent literature has suggested the potential of using large language models +(LLMs) to make predictions for tabular tasks. However, LLMs have been shown to +exhibit harmful social biases that reflect the stereotypes and inequalities +present in the society. To this end, as well as the widespread use of tabular +data in many high-stake applications, it is imperative to explore the following +questions: what sources of information do LLMs draw upon when making +predictions for tabular tasks; whether and to what extent are LLM predictions +for tabular tasks influenced by social biases and stereotypes; and what are the +consequential implications for fairness? Through a series of experiments, we +delve into these questions and show that LLMs tend to inherit social biases +from their training data which significantly impact their fairness in tabular +prediction tasks. Furthermore, our investigations show that in the context of +bias mitigation, though in-context learning and fine-tuning have a moderate +effect, the fairness metric gap between different subgroups is still larger +than that in traditional machine learning models, such as Random Forest and +shallow Neural Networks. This observation emphasizes that the social biases are +inherent within the LLMs themselves and inherited from their pre-training +corpus, not only from the downstream task datasets. Besides, we demonstrate +that label-flipping of in-context examples can significantly reduce biases, +further highlighting the presence of inherent bias within LLMs. +" +Large Language Models are Visual Reasoning Coordinators,Liangyu Chen,http://arxiv.org/pdf/2310.15166v1.pdf,2023-10-23,"['cs.cv', 'cs.cl']",2310.15166v1.pdf," Visual reasoning requires multimodal perception and commonsense cognition of +the world. Recently, multiple vision-language models (VLMs) have been proposed +with excellent commonsense reasoning ability in various domains. However, how +to harness the collective power of these complementary VLMs is rarely explored. +Existing methods like ensemble still struggle to aggregate these models with +the desired higher-order communications. In this work, we propose Cola, a novel +paradigm that coordinates multiple VLMs for visual reasoning. Our key insight +is that a large language model (LLM) can efficiently coordinate multiple VLMs +by facilitating natural language communication that leverages their distinct +and complementary capabilities. Extensive experiments demonstrate that our +instruction tuning variant, Cola-FT, achieves state-of-the-art performance on +visual question answering (VQA), outside knowledge VQA, visual entailment, and +visual spatial reasoning tasks. Moreover, we show that our in-context learning +variant, Cola-Zero, exhibits competitive performance in zero and few-shot +settings, without finetuning. Through systematic ablation studies and +visualizations, we validate that a coordinator LLM indeed comprehends the +instruction prompts as well as the separate functionalities of VLMs; it then +coordinates them to enable impressive visual reasoning capabilities. +" +Function Vectors in Large Language Models,Eric Todd,http://arxiv.org/pdf/2310.15213v1.pdf,2023-10-23,"['cs.cl', 'cs.lg']",2310.15213v1.pdf," We report the presence of a simple neural mechanism that represents an +input-output function as a vector within autoregressive transformer language +models (LMs). Using causal mediation analysis on a diverse range of +in-context-learning (ICL) tasks, we find that a small number attention heads +transport a compact representation of the demonstrated task, which we call a +function vector (FV). FVs are robust to changes in context, i.e., they trigger +execution of the task on inputs such as zero-shot and natural text settings +that do not resemble the ICL contexts from which they are collected. We test +FVs across a range of tasks, models, and layers and find strong causal effects +across settings in middle layers. We investigate the internal structure of FVs +and find while that they often contain information that encodes the output +space of the function, this information alone is not sufficient to reconstruct +an FV. Finally, we test semantic vector composition in FVs, and find that to +some extent they can be summed to create vectors that trigger new complex +tasks. Taken together, our findings suggest that LLMs contain internal +abstractions of general-purpose functions that can be invoked in a variety of +contexts. +" +TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction,Junyi Liu,http://arxiv.org/pdf/2310.15556v2.pdf,2023-10-24,"['cs.cl', 'cs.ir']",2310.15556v2.pdf," Since ChatGPT released its API for public use, the number of applications +built on top of commercial large language models (LLMs) increase exponentially. +One popular usage of such models is leveraging its in-context learning ability +and generating responses given user queries leveraging knowledge obtained by +retrieval augmentation. One problem of deploying commercial retrieval-augmented +LLMs is the cost due to the additionally retrieved context that largely +increases the input token size of the LLMs. To mitigate this, we propose a +token compression scheme that includes two methods: summarization compression +and semantic compression. The first method applies a T5-based model that is +fine-tuned by datasets generated using self-instruct containing samples with +varying lengths and reduce token size by doing summarization. The second method +further compresses the token size by removing words with lower impact on the +semantic. In order to adequately evaluate the effectiveness of the proposed +methods, we propose and utilize a dataset called Food-Recommendation DB (FRDB) +focusing on food recommendation for women around pregnancy period or infants. +Our summarization compression can reduce 65% of the retrieval token size with +further 0.3% improvement on the accuracy; semantic compression provides a more +flexible way to trade-off the token size with performance, for which we can +reduce the token size by 20% with only 1.6% of accuracy drop. +" +Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language Model,Zhe Liu,http://arxiv.org/pdf/2310.15657v1.pdf,2023-10-24,['cs.se'],2310.15657v1.pdf," Mobile applications have become a ubiquitous part of our daily life, +providing users with access to various services and utilities. Text input, as +an important interaction channel between users and applications, plays an +important role in core functionality such as search queries, authentication, +messaging, etc. However, certain special text (e.g., -18 for Font Size) can +cause the app to crash, and generating diversified unusual inputs for fully +testing the app is highly demanded. Nevertheless, this is also challenging due +to the combination of explosion dilemma, high context sensitivity, and complex +constraint relations. This paper proposes InputBlaster which leverages the LLM +to automatically generate unusual text inputs for mobile app crash detection. +It formulates the unusual inputs generation problem as a task of producing a +set of test generators, each of which can yield a batch of unusual text inputs +under the same mutation rule. In detail, InputBlaster leverages LLM to produce +the test generators together with the mutation rules serving as the reasoning +chain, and utilizes the in-context learning schema to demonstrate the LLM with +examples for boosting the performance. InputBlaster is evaluated on 36 text +input widgets with cash bugs involving 31 popular Android apps, and results +show that it achieves 78% bug detection rate, with 136% higher than the best +baseline. Besides, we integrate it with the automated GUI testing tool and +detect 37 unseen crashes in real-world apps from Google Play. +" +ExPT: Synthetic Pretraining for Few-Shot Experimental Design,Tung Nguyen,http://arxiv.org/pdf/2310.19961v1.pdf,2023-10-30,"['cs.lg', 'cs.ai']",2310.19961v1.pdf," Experimental design is a fundamental problem in many science and engineering +fields. In this problem, sample efficiency is crucial due to the time, money, +and safety costs of real-world design evaluations. Existing approaches either +rely on active data collection or access to large, labeled datasets of past +experiments, making them impractical in many real-world scenarios. In this +work, we address the more challenging yet realistic setting of few-shot +experimental design, where only a few labeled data points of input designs and +their corresponding values are available. We approach this problem as a +conditional generation task, where a model conditions on a few labeled examples +and the desired output to generate an optimal input design. To this end, we +introduce Experiment Pretrained Transformers (ExPT), a foundation model for +few-shot experimental design that employs a novel combination of synthetic +pretraining with in-context learning. In ExPT, we only assume knowledge of a +finite collection of unlabelled data points from the input domain and pretrain +a transformer neural network to optimize diverse synthetic functions defined +over this domain. Unsupervised pretraining allows ExPT to adapt to any design +task at test time in an in-context fashion by conditioning on a few labeled +data points from the target task and generating the candidate optima. We +evaluate ExPT on few-shot experimental design in challenging domains and +demonstrate its superior generality and performance compared to existing +methods. The source code is available at https://github.com/tung-nd/ExPT.git. +" +Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving,Zhan Ling,http://arxiv.org/pdf/2311.00694v1.pdf,2023-11-01,"['cs.ai', 'cs.cl']",2311.00694v1.pdf," Large Language Models (LLMs) have achieved tremendous progress, yet they +still often struggle with challenging reasoning problems. Current approaches +address this challenge by sampling or searching detailed and low-level +reasoning chains. However, these methods are still limited in their exploration +capabilities, making it challenging for correct solutions to stand out in the +huge solution space. In this work, we unleash LLMs' creative potential for +exploring multiple diverse problem solving strategies by framing an LLM as a +hierarchical policy via in-context learning. This policy comprises of a +visionary leader that proposes multiple diverse high-level problem-solving +tactics as hints, accompanied by a follower that executes detailed +problem-solving processes following each of the high-level instruction. The +follower uses each of the leader's directives as a guide and samples multiple +reasoning chains to tackle the problem, generating a solution group for each +leader proposal. Additionally, we propose an effective and efficient +tournament-based approach to select among these explored solution groups to +reach the final answer. Our approach produces meaningful and inspiring hints, +enhances problem-solving strategy exploration, and improves the final answer +accuracy on challenging problems in the MATH dataset. Code will be released at +https://github.com/lz1oceani/LLM-As-Hierarchical-Policy. +" +Sentiment Analysis through LLM Negotiations,Xiaofei Sun,http://arxiv.org/pdf/2311.01876v1.pdf,2023-11-03,['cs.cl'],2311.01876v1.pdf," A standard paradigm for sentiment analysis is to rely on a singular LLM and +makes the decision in a single round under the framework of in-context +learning. This framework suffers the key disadvantage that the single-turn +output generated by a single LLM might not deliver the perfect decision, just +as humans sometimes need multiple attempts to get things right. This is +especially true for the task of sentiment analysis where deep reasoning is +required to address the complex linguistic phenomenon (e.g., clause +composition, irony, etc) in the input. + To address this issue, this paper introduces a multi-LLM negotiation +framework for sentiment analysis. The framework consists of a reasoning-infused +generator to provide decision along with rationale, a explanation-deriving +discriminator to evaluate the credibility of the generator. The generator and +the discriminator iterate until a consensus is reached. The proposed framework +naturally addressed the aforementioned challenge, as we are able to take the +complementary abilities of two LLMs, have them use rationale to persuade each +other for correction. + Experiments on a wide range of sentiment analysis benchmarks (SST-2, Movie +Review, Twitter, yelp, amazon, IMDB) demonstrate the effectiveness of proposed +approach: it consistently yields better performances than the ICL baseline +across all benchmarks, and even superior performances to supervised baselines +on the Twitter and movie review datasets. +" +ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models,Zhelun Shi,http://arxiv.org/pdf/2311.02692v1.pdf,2023-11-05,['cs.cv'],2311.02692v1.pdf," Multimodal Large Language Models (MLLMs) have shown impressive abilities in +interacting with visual content with myriad potential downstream tasks. +However, even though a list of benchmarks has been proposed, the capabilities +and limitations of MLLMs are still not comprehensively understood, due to a +lack of a standardized and holistic evaluation framework. To this end, we +present the first Comprehensive Evaluation Framework (ChEF) that can +holistically profile each MLLM and fairly compare different MLLMs. First, we +structure ChEF as four modular components, i.e., Scenario as scalable +multimodal datasets, Instruction as flexible instruction retrieving formulae, +Inferencer as reliable question answering strategies, and Metric as indicative +task-specific score functions. Based on them, ChEF facilitates versatile +evaluations in a standardized framework, and new evaluations can be built by +designing new Recipes (systematic selection of these four components). Notably, +current MLLM benchmarks can be readily summarized as recipes of ChEF. Second, +we introduce 6 new recipes to quantify competent MLLMs' desired capabilities +(or called desiderata, i.e., calibration, in-context learning, instruction +following, language performance, hallucination, and robustness) as reliable +agents that can perform real-world multimodal interactions. Third, we conduct a +large-scale evaluation of 9 prominent MLLMs on 9 scenarios and 6 desiderata. +Our evaluation summarized over 20 valuable observations concerning the +generalizability of MLLMs across various scenarios and the composite capability +of MLLMs required for multimodal interactions. We will publicly release all the +detailed implementations for further analysis, as well as an easy-to-use +modular toolkit for the integration of new recipes and models, so that ChEF can +be a growing evaluation framework for the MLLM community. +" +Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs,Wenke Xia,http://arxiv.org/pdf/2311.02847v2.pdf,2023-11-06,"['cs.ro', 'cs.ai']",2311.02847v2.pdf," Generalizable articulated object manipulation is essential for home-assistant +robots. Recent efforts focus on imitation learning from demonstrations or +reinforcement learning in simulation, however, due to the prohibitive costs of +real-world data collection and precise object simulation, it still remains +challenging for these works to achieve broad adaptability across diverse +articulated objects. Recently, many works have tried to utilize the strong +in-context learning ability of Large Language Models (LLMs) to achieve +generalizable robotic manipulation, but most of these researches focus on +high-level task planning, sidelining low-level robotic control. In this work, +building on the idea that the kinematic structure of the object determines how +we can manipulate it, we propose a kinematic-aware prompting framework that +prompts LLMs with kinematic knowledge of objects to generate low-level motion +trajectory waypoints, supporting various object manipulation. To effectively +prompt LLMs with the kinematic structure of different objects, we design a +unified kinematic knowledge parser, which represents various articulated +objects as a unified textual description containing kinematic joints and +contact location. Building upon this unified description, a kinematic-aware +planner model is proposed to generate precise 3D manipulation waypoints via a +designed kinematic-aware chain-of-thoughts prompting method. Our evaluation +spanned 48 instances across 16 distinct categories, revealing that our +framework not only outperforms traditional methods on 8 seen categories but +also shows a powerful zero-shot capability for 8 unseen articulated object +categories. Moreover, the real-world experiments on 7 different object +categories prove our framework's adaptability in practical scenarios. Code is +released at +\href{https://github.com/GeWu-Lab/LLM_articulated_object_manipulation/tree/main}{here}. +" +In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models,Yunlong Chen,http://arxiv.org/pdf/2311.02956v1.pdf,2023-11-06,"['cs.cl', 'cs.ai', 'i.2.7']",2311.02956v1.pdf," Knowledge Base Question Answering (KBQA) aims to answer factoid questions +based on knowledge bases. However, generating the most appropriate knowledge +base query code based on Natural Language Questions (NLQ) poses a significant +challenge in KBQA. In this work, we focus on the CCKS2023 Competition of +Question Answering with Knowledge Graph Inference for Unmanned Systems. +Inspired by the recent success of large language models (LLMs) like ChatGPT and +GPT-3 in many QA tasks, we propose a ChatGPT-based Cypher Query Language (CQL) +generation framework to generate the most appropriate CQL based on the given +NLQ. Our generative framework contains six parts: an auxiliary model predicting +the syntax-related information of CQL based on the given NLQ, a proper noun +matcher extracting proper nouns from the given NLQ, a demonstration example +selector retrieving similar examples of the input sample, a prompt constructor +designing the input template of ChatGPT, a ChatGPT-based generation model +generating the CQL, and an ensemble model to obtain the final answers from +diversified outputs. With our ChatGPT-based CQL generation framework, we +achieved the second place in the CCKS 2023 Question Answering with Knowledge +Graph Inference for Unmanned Systems competition, achieving an F1-score of +0.92676. +" +Retrieval-Augmented Code Generation for Universal Information Extraction,Yucan Guo,http://arxiv.org/pdf/2311.02962v1.pdf,2023-11-06,"['cs.ai', 'cs.cl', 'cs.ir']",2311.02962v1.pdf," Information Extraction (IE) aims to extract structural knowledge (e.g., +entities, relations, events) from natural language texts, which brings +challenges to existing methods due to task-specific schemas and complex text +expressions. Code, as a typical kind of formalized language, is capable of +describing structural knowledge under various schemas in a universal way. On +the other hand, Large Language Models (LLMs) trained on both codes and texts +have demonstrated powerful capabilities of transforming texts into codes, which +provides a feasible solution to IE tasks. Therefore, in this paper, we propose +a universal retrieval-augmented code generation framework based on LLMs, called +Code4UIE, for IE tasks. Specifically, Code4UIE adopts Python classes to define +task-specific schemas of various structural knowledge in a universal way. By so +doing, extracting knowledge under these schemas can be transformed into +generating codes that instantiate the predefined Python classes with the +information in texts. To generate these codes more precisely, Code4UIE adopts +the in-context learning mechanism to instruct LLMs with examples. In order to +obtain appropriate examples for different tasks, Code4UIE explores several +example retrieval strategies, which can retrieve examples semantically similar +to the given texts. Extensive experiments on five representative IE tasks +across nine datasets demonstrate the effectiveness of the Code4UIE framework. +" +Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning,Sarkar Snigdha Sarathi Das,http://arxiv.org/pdf/2311.03748v1.pdf,2023-11-07,['cs.cl'],2311.03748v1.pdf," Unified Sequence Labeling that articulates different sequence labeling +problems such as Named Entity Recognition, Relation Extraction, Semantic Role +Labeling, etc. in a generalized sequence-to-sequence format opens up the +opportunity to make the maximum utilization of large language model knowledge +toward structured prediction. Unfortunately, this requires formatting them into +specialized augmented format unknown to the base pretrained language model +(PLMs) necessitating finetuning to the target format. This significantly bounds +its usefulness in data-limited settings where finetuning large models cannot +properly generalize to the target format. To address this challenge and +leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic +sparse finetuning strategy that selectively focuses on a fraction of +parameters, informed by feedback from highly regressing examples, during the +fine-tuning process. By leveraging the dynamism of sparsity, our approach +mitigates the impact of well-learned samples and prioritizes underperforming +instances for improvement in generalization. Across five tasks of sequence +labeling, we demonstrate that FISH-DIP can smoothly optimize the model in low +resource settings offering upto 40% performance improvements over full +fine-tuning depending on target evaluation settings. Also, compared to +in-context learning and other parameter-efficient fine-tuning approaches, +FISH-DIP performs comparably or better, notably in extreme low-resource +settings. +" +UL2: Unifying Language Learning Paradigms,Yi Tay,http://arxiv.org/pdf/2205.05131v3.pdf,2022-05-10,['cs.cl'],2205.05131v3.pdf," Existing pre-trained models are generally geared towards a particular class +of problems. To date, there seems to be still no consensus on what the right +architecture and pre-training setup should be. This paper presents a unified +framework for pre-training models that are universally effective across +datasets and setups. We begin by disentangling architectural archetypes with +pre-training objectives -- two concepts that are commonly conflated. Next, we +present a generalized & unified perspective for self-supervision in NLP and +show how different pre-training objectives can be cast as one another and how +interpolating between different objectives can be effective. We then propose +Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse +pre-training paradigms together. We furthermore introduce a notion of mode +switching, wherein downstream fine-tuning is associated with specific +pre-training schemes. We conduct extensive ablative experiments to compare +multiple pre-training objectives and find that our method pushes the +Pareto-frontier by outperforming T5 & GPT-like models across multiple diverse +setups. By scaling our model up to 20B parameters, we achieve SOTA performance +on 50 well-established supervised finetuning based NLP tasks. Our model also +achieve strong results at in-context learning, outperforming 175B GPT-3 on +zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot +summarization. On 0-shot MMLU, UL2 20B outperforms T0 and T5 models. UL2 20B +also works well with chain-of-thought prompting and reasoning, making it an +appealing choice for research into reasoning at a small to medium scale of 20B +parameters. Finally, we apply FLAN instruction tuning to the UL2 20B model, +achieving MMLU and Big-Bench scores competitive to FLAN-PaLM 62B. We release +Flax-based T5X checkpoints for the UL2 20B & Flan-UL2 20B. +" +Human-Timescale Adaptation in an Open-Ended Task Space, Adaptive Agent Team,http://arxiv.org/pdf/2301.07608v1.pdf,2023-01-18,"['cs.lg', 'cs.ai', 'cs.ne']",2301.07608v1.pdf," Foundation models have shown impressive adaptation and scalability in +supervised and self-supervised learning problems, but so far these successes +have not fully translated to reinforcement learning (RL). In this work, we +demonstrate that training an RL agent at scale leads to a general in-context +learning algorithm that can adapt to open-ended novel embodied 3D problems as +quickly as humans. In a vast space of held-out environment dynamics, our +adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration, +efficient exploitation of acquired knowledge, and can successfully be prompted +with first-person demonstrations. Adaptation emerges from three ingredients: +(1) meta-reinforcement learning across a vast, smooth and diverse task +distribution, (2) a policy parameterised as a large-scale attention-based +memory architecture, and (3) an effective automated curriculum that prioritises +tasks at the frontier of an agent's capabilities. We demonstrate characteristic +scaling laws with respect to network size, memory length, and richness of the +training task distribution. We believe our results lay the foundation for +increasingly general and adaptive RL agents that perform well across +ever-larger open-ended domains. +" +DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4,Zhengliang Liu,http://arxiv.org/pdf/2303.11032v1.pdf,2023-03-20,"['cs.cl', 'cs.cy']",2303.11032v1.pdf," The digitization of healthcare has facilitated the sharing and re-using of +medical data but has also raised concerns about confidentiality and privacy. +HIPAA (Health Insurance Portability and Accountability Act) mandates removing +re-identifying information before the dissemination of medical records. Thus, +effective and efficient solutions for de-identifying medical data, especially +those in free-text forms, are highly needed. While various computer-assisted +de-identification methods, including both rule-based and learning-based, have +been developed and used in prior practice, such solutions still lack +generalizability or need to be fine-tuned according to different scenarios, +significantly imposing restrictions in wider use. The advancement of large +language models (LLM), such as ChatGPT and GPT-4, have shown great potential in +processing text data in the medical domain with zero-shot in-context learning, +especially in the task of privacy protection, as these models can identify +confidential information by their powerful named entity recognition (NER) +capability. In this work, we developed a novel GPT4-enabled de-identification +framework (""DeID-GPT"") to automatically identify and remove the identifying +information. Compared to existing commonly used medical text data +de-identification methods, our developed DeID-GPT showed the highest accuracy +and remarkable reliability in masking private information from the unstructured +medical text while preserving the original structure and meaning of the text. +This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text +data processing and de-identification, which provides insights for further +research and solution development on the use of LLMs such as ChatGPT/GPT-4 in +healthcare. Codes and benchmarking data information are available at +https://github.com/yhydhx/ChatGPT-API. +" +TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs,Yaobo Liang,http://arxiv.org/pdf/2303.16434v1.pdf,2023-03-29,"['cs.ai', 'cs.cl']",2303.16434v1.pdf," Artificial Intelligence (AI) has made incredible progress recently. On the +one hand, advanced foundation models like ChatGPT can offer powerful +conversation, in-context learning and code generation abilities on a broad +range of open-domain tasks. They can also generate high-level solution outlines +for domain-specific tasks based on the common sense knowledge they have +acquired. However, they still face difficulties with some specialized tasks +because they lack enough domain-specific data during pre-training or they often +have errors in their neural network computations on those tasks that need +accurate executions. On the other hand, there are also many existing models and +systems (symbolic-based or neural-based) that can do some domain-specific tasks +very well. However, due to the different implementation or working mechanisms, +they are not easily accessible or compatible with foundation models. Therefore, +there is a clear and pressing need for a mechanism that can leverage foundation +models to propose task solution outlines and then automatically match some of +the sub-tasks in the outlines to the off-the-shelf models and systems with +special functionalities to complete them. Inspired by this, we introduce +TaskMatrix.AI as a new AI ecosystem that connects foundation models with +millions of APIs for task completion. Unlike most previous work that aimed to +improve a single AI model, TaskMatrix.AI focuses more on using existing +foundation models (as a brain-like central system) and APIs of other AI models +and systems (as sub-task solvers) to achieve diversified tasks in both digital +and physical domains. As a position paper, we will present our vision of how to +build such an ecosystem, explain each key component, and use study cases to +illustrate both the feasibility of this vision and the main challenges we need +to address next. +" +Subject-driven Text-to-Image Generation via Apprenticeship Learning,Wenhu Chen,http://arxiv.org/pdf/2304.00186v5.pdf,2023-04-01,"['cs.cv', 'cs.ai']",2304.00186v5.pdf," Recent text-to-image generation models like DreamBooth have made remarkable +progress in generating highly customized images of a target subject, by +fine-tuning an ``expert model'' for a given subject from a few examples. +However, this process is expensive, since a new expert model must be learned +for each subject. In this paper, we present SuTI, a Subject-driven +Text-to-Image generator that replaces subject-specific fine tuning with +in-context learning. Given a few demonstrations of a new subject, SuTI can +instantly generate novel renditions of the subject in different scenes, without +any subject-specific optimization. SuTI is powered by apprenticeship learning, +where a single apprentice model is learned from data generated by a massive +number of subject-specific expert models. Specifically, we mine millions of +image clusters from the Internet, each centered around a specific visual +subject. We adopt these clusters to train a massive number of expert models, +each specializing in a different subject. The apprentice model SuTI then learns +to imitate the behavior of these fine-tuned experts. SuTI can generate +high-quality and customized subject-specific images 20x faster than +optimization-based SoTA methods. On the challenging DreamBench and +DreamBench-v2, our human evaluation shows that SuTI significantly outperforms +existing models like InstructPix2Pix, Textual Inversion, Imagic, Prompt2Prompt, +Re-Imagen and DreamBooth, especially on the subject and text alignment aspects. +" +Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT,Yinlin Deng,http://arxiv.org/pdf/2304.02014v1.pdf,2023-04-04,['cs.se'],2304.02014v1.pdf," Deep Learning (DL) library bugs affect downstream DL applications, +emphasizing the need for reliable systems. Generating valid input programs for +fuzzing DL libraries is challenging due to the need for satisfying both +language syntax/semantics and constraints for constructing valid computational +graphs. Recently, the TitanFuzz work demonstrates that modern Large Language +Models (LLMs) can be directly leveraged to implicitly learn all the constraints +to generate valid DL programs for fuzzing. However, LLMs tend to generate +ordinary programs following similar patterns seen in their massive training +corpora, while fuzzing favors unusual inputs that cover edge cases or are +unlikely to be manually produced. + To fill this gap, this paper proposes FuzzGPT, the first technique to prime +LLMs to synthesize unusual programs for fuzzing. FuzzGPT is built on the +well-known hypothesis that historical bug-triggering programs may include +rare/valuable code ingredients important for bug finding. Traditional +techniques leveraging such historical information require intensive human +efforts to design dedicated generators and ensure the validity of generated +programs. FuzzGPT demonstrates that this process can be fully automated via the +intrinsic capabilities of LLMs (including fine-tuning and in-context learning), +while being generalizable and applicable to challenging domains. While FuzzGPT +can be applied with different LLMs, this paper focuses on the powerful +GPT-style models: Codex and CodeGen. Moreover, FuzzGPT also shows the potential +of directly leveraging the instruct-following capability of the recent ChatGPT +for effective fuzzing. Evaluation on two popular DL libraries (PyTorch and +TensorFlow) shows that FuzzGPT can substantially outperform TitanFuzz, +detecting 76 bugs, with 49 already confirmed as previously unknown bugs, +including 11 high-priority bugs or security vulnerabilities. +" +ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT,Chong Ma,http://arxiv.org/pdf/2304.08448v2.pdf,2023-04-17,"['cs.cl', 'cs.ai']",2304.08448v2.pdf," The 'Impression' section of a radiology report is a critical basis for +communication between radiologists and other physicians, and it is typically +written by radiologists based on the 'Findings' section. However, writing +numerous impressions can be laborious and error-prone for radiologists. +Although recent studies have achieved promising results in automatic impression +generation using large-scale medical text data for pre-training and fine-tuning +pre-trained language models, such models often require substantial amounts of +medical text data and have poor generalization performance. While large +language models (LLMs) like ChatGPT have shown strong generalization +capabilities and performance, their performance in specific domains, such as +radiology, remains under-investigated and potentially limited. To address this +limitation, we propose ImpressionGPT, which leverages the in-context learning +capability of LLMs by constructing dynamic contexts using domain-specific, +individualized data. This dynamic prompt approach enables the model to learn +contextual knowledge from semantically similar examples from existing data. +Additionally, we design an iterative optimization algorithm that performs +automatic evaluation on the generated impression results and composes the +corresponding instruction prompts to further optimize the model. The proposed +ImpressionGPT model achieves state-of-the-art performance on both MIMIC-CXR and +OpenI datasets without requiring additional training data or fine-tuning the +LLMs. This work presents a paradigm for localizing LLMs that can be applied in +a wide range of similar application scenarios, bridging the gap between +general-purpose LLMs and the specific language processing needs of various +domains. +" +NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers,Kai Shen,http://arxiv.org/pdf/2304.09116v3.pdf,2023-04-18,"['eess.as', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.sd']",2304.09116v3.pdf," Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild +datasets is important to capture the diversity in human speech such as speaker +identities, prosodies, and styles (e.g., singing). Current large TTS systems +usually quantize speech into discrete tokens and use language models to +generate these tokens one by one, which suffer from unstable prosody, word +skipping/repeating issue, and poor voice quality. In this paper, we develop +NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual +vector quantizers to get the quantized latent vectors and uses a diffusion +model to generate these latent vectors conditioned on text input. To enhance +the zero-shot capability that is important to achieve diverse speech synthesis, +we design a speech prompting mechanism to facilitate in-context learning in the +diffusion model and the duration/pitch predictor. We scale NaturalSpeech 2 to +large-scale datasets with 44K hours of speech and singing data and evaluate its +voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS +systems by a large margin in terms of prosody/timbre similarity, robustness, +and voice quality in a zero-shot setting, and performs novel zero-shot singing +synthesis with only a speech prompt. Audio samples are available at +https://speechresearch.github.io/naturalspeech2. +" +Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback,Yao Fu,http://arxiv.org/pdf/2305.10142v1.pdf,2023-05-17,['cs.cl'],2305.10142v1.pdf," We study whether multiple large language models (LLMs) can autonomously +improve each other in a negotiation game by playing, reflecting, and +criticizing. We are interested in this question because if LLMs were able to +improve each other, it would imply the possibility of creating strong AI agents +with minimal human intervention. We ask two LLMs to negotiate with each other, +playing the roles of a buyer and a seller, respectively. They aim to reach a +deal with the buyer targeting a lower price and the seller a higher one. A +third language model, playing the critic, provides feedback to a player to +improve the player's negotiation strategies. We let the two agents play +multiple rounds, using previous negotiation history and AI feedback as +in-context demonstrations to improve the model's negotiation strategy +iteratively. We use different LLMs (GPT and Claude) for different roles and use +the deal price as the evaluation metric. Our experiments reveal multiple +intriguing findings: (1) Only a subset of the language models we consider can +self-play and improve the deal price from AI feedback, weaker models either do +not understand the game's rules or cannot incorporate AI feedback for further +improvement. (2) Models' abilities to learn from the feedback differ when +playing different roles. For example, it is harder for Claude-instant to +improve as the buyer than as the seller. (3) When unrolling the game to +multiple rounds, stronger agents can consistently improve their performance by +meaningfully using previous experiences and iterative AI feedback, yet have a +higher risk of breaking the deal. We hope our work provides insightful initial +explorations of having models autonomously improve each other with game playing +and AI feedback. +" +XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages,Sebastian Ruder,http://arxiv.org/pdf/2305.11938v2.pdf,2023-05-19,['cs.cl'],2305.11938v2.pdf," Data scarcity is a crucial issue for the development of highly multilingual +NLP systems. Yet for many under-represented languages (ULs) -- languages for +which NLP re-search is particularly far behind in meeting user needs -- it is +feasible to annotate small amounts of data. Motivated by this, we propose +XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather +than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by +speakers of high-resource languages; and its focus on under-represented +languages where this scarce-data scenario tends to be most realistic. XTREME-UP +evaluates the capabilities of language models across 88 under-represented +languages over 9 key user-centric technologies including ASR, OCR, MT, and +information access tasks that are of general utility. We create new datasets +for OCR, autocomplete, semantic parsing, and transliteration, and build on and +refine existing datasets for other tasks. XTREME-UP provides methodology for +evaluating many modeling scenarios including text-only, multi-modal (vision, +audio, and text),supervised parameter tuning, and in-context learning. We +evaluate commonly used models on the benchmark. We release all code and scripts +to train and evaluate models +" +Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization,Jeonghoon Kim,http://arxiv.org/pdf/2305.14152v2.pdf,2023-05-23,"['cs.lg', 'cs.ai']",2305.14152v2.pdf," Large language models (LLMs) face the challenges in fine-tuning and +deployment due to their high memory demands and computational costs. While +parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage +of the optimizer state during fine-tuning, the inherent size of pre-trained LLM +weights continues to be a pressing concern. Even though quantization techniques +are widely proposed to ease memory demands and accelerate LLM inference, most +of these techniques are geared towards the deployment phase. To bridge this +gap, this paper presents Parameter-Efficient and Quantization-aware Adaptation +(PEQA) - a simple yet effective method that combines the advantages of PEFT +with quantized LLMs. By updating solely the quantization scales, PEQA can be +directly applied to quantized LLMs, ensuring seamless task transitions. +Parallel to existing PEFT methods, PEQA significantly reduces the memory +overhead associated with the optimizer state. Furthermore, it leverages the +advantages of quantization to substantially reduce model sizes. Even after +fine-tuning, the quantization structure of a PEQA-tuned LLM remains intact, +allowing for accelerated inference on the deployment stage. We employ +PEQA-tuning for task-specific adaptation on LLMs with up to 65 billion +parameters. To assess the logical reasoning and language comprehension of +PEQA-tuned LLMs, we fine-tune low-bit quantized LLMs using a instruction +dataset. Our results show that even when LLMs are quantized to below 4-bit +precision, their capabilities in language modeling, few-shot in-context +learning, and comprehension can be resiliently restored to (or even improved +over) their full-precision original performances with PEQA. +" +PaLI-X: On Scaling up a Multilingual Vision and Language Model,Xi Chen,http://arxiv.org/pdf/2305.18565v1.pdf,2023-05-29,"['cs.cv', 'cs.cl', 'cs.lg']",2305.18565v1.pdf," We present the training recipe and results of scaling up PaLI-X, a +multilingual vision and language model, both in terms of size of the components +and the breadth of its training task mixture. Our model achieves new levels of +performance on a wide-range of varied and complex tasks, including multiple +image-based captioning and question-answering tasks, image-based document +understanding and few-shot (in-context) learning, as well as object detection, +video question answering, and video captioning. PaLI-X advances the +state-of-the-art on most vision-and-language benchmarks considered (25+ of +them). Finally, we observe emerging capabilities, such as complex counting and +multilingual object detection, tasks that are not explicitly in the training +mix. +" +"Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations",Lifan Yuan,http://arxiv.org/pdf/2306.04618v2.pdf,2023-06-07,"['cs.cl', 'cs.cr', 'cs.lg']",2306.04618v2.pdf," This paper reexamines the research on out-of-distribution (OOD) robustness in +the field of NLP. We find that the distribution shift settings in previous +studies commonly lack adequate challenges, hindering the accurate evaluation of +OOD robustness. To address these issues, we propose a benchmark construction +protocol that ensures clear differentiation and challenging distribution +shifts. Then we introduce BOSS, a Benchmark suite for Out-of-distribution +robustneSS evaluation covering 5 tasks and 20 datasets. Based on BOSS, we +conduct a series of experiments on pre-trained language models for analysis and +evaluation of OOD robustness. First, for vanilla fine-tuning, we examine the +relationship between in-distribution (ID) and OOD performance. We identify +three typical types that unveil the inner learning mechanism, which could +potentially facilitate the forecasting of OOD robustness, correlating with the +advancements on ID datasets. Then, we evaluate 5 classic methods on BOSS and +find that, despite exhibiting some effectiveness in specific cases, they do not +offer significant improvement compared to vanilla fine-tuning. Further, we +evaluate 5 LLMs with various adaptation paradigms and find that when sufficient +ID data is available, fine-tuning domain-specific models outperform LLMs on ID +examples significantly. However, in the case of OOD instances, prioritizing +LLMs with in-context learning yields better results. We identify that both +fine-tuned small models and LLMs face challenges in effectively addressing +downstream tasks. The code is public at +\url{https://github.com/lifan-yuan/OOD_NLP}. +" +Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection,Yu Bai,http://arxiv.org/pdf/2306.04637v2.pdf,2023-06-07,"['cs.lg', 'cs.ai', 'cs.cl', 'math.st', 'stat.ml', 'stat.th']",2306.04637v2.pdf," Neural sequence models based on the transformer architecture have +demonstrated remarkable \emph{in-context learning} (ICL) abilities, where they +can perform new tasks when prompted with training and test examples, without +any parameter update to the model. This work first provides a comprehensive +statistical theory for transformers to perform ICL. Concretely, we show that +transformers can implement a broad class of standard machine learning +algorithms in context, such as least squares, ridge regression, Lasso, learning +generalized linear models, and gradient descent on two-layer neural networks, +with near-optimal predictive power on various in-context data distributions. +Using an efficient implementation of in-context gradient descent as the +underlying mechanism, our transformer constructions admit mild size bounds, and +can be learned with polynomially many pretraining sequences. + Building on these ``base'' ICL algorithms, intriguingly, we show that +transformers can implement more complex ICL procedures involving +\emph{in-context algorithm selection}, akin to what a statistician can do in +real life -- A \emph{single} transformer can adaptively select different base +ICL algorithms -- or even perform qualitatively different tasks -- on different +input sequences, without any explicit prompting of the right algorithm or task. +We both establish this in theory by explicit constructions, and also observe +this phenomenon experimentally. In theory, we construct two general mechanisms +for algorithm selection with concrete examples: pre-ICL testing, and post-ICL +validation. As an example, we use the post-ICL validation mechanism to +construct a transformer that can perform nearly Bayes-optimal ICL on a +challenging task -- noisy linear models with mixed noise levels. +Experimentally, we demonstrate the strong in-context algorithm selection +capabilities of standard transformer architectures. +" +Instruction Tuned Models are Quick Learners,Himanshu Gupta,http://arxiv.org/pdf/2306.05539v1.pdf,2023-05-17,['cs.cl'],2306.05539v1.pdf," Instruction tuning of language models has demonstrated the ability to enhance +model generalization to unseen tasks via in-context learning using a few +examples. However, typical supervised learning still requires a plethora of +downstream training data for finetuning. Often in real-world situations, there +is a scarcity of data available for finetuning, falling somewhere between few +shot inference and fully supervised finetuning. In this work, we demonstrate +the sample efficiency of instruction tuned models over various tasks by +estimating the minimal downstream training data required by them to perform +transfer learning and match the performance of state-of-the-art (SOTA) +supervised models. We conduct experiments on 119 tasks from Super Natural +Instructions (SuperNI) in both the single task learning (STL) and multi task +learning (MTL) settings. Our findings reveal that, in the STL setting, +instruction tuned models equipped with 25% of the downstream train data surpass +the SOTA performance on the downstream tasks. In the MTL setting, an +instruction tuned model trained on only 6% of downstream training data achieve +SOTA, while using 100% of the training data results in a 3.69% points +improvement (ROUGE-L 74.68) over the previous SOTA. We conduct an analysis on +T5 vs Tk-Instruct by developing several baselines to demonstrate that +instruction tuning aids in increasing both sample efficiency and transfer +learning. Additionally, we observe a consistent ~4% performance increase in +both settings when pre-finetuning is performed with instructions. Finally, we +conduct a categorical study and find that contrary to previous results, tasks +in the question rewriting and title generation categories suffer from +instruction tuning. +" +Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control,Longtao Zheng,http://arxiv.org/pdf/2306.07863v2.pdf,2023-06-13,['cs.ai'],2306.07863v2.pdf," Building agents using large language models (LLMs) to control computers is an +emerging research field, where the agent perceives computer states and performs +actions to accomplish complex tasks. Previous computer agents have demonstrated +the benefits of in-context learning (ICL); however, their performance is +hindered by several issues. First, the limited context length of LLMs and +complex computer states restrict the number of exemplars, as a single webpage +can consume the entire context. Second, the exemplars in current methods, such +as high-level plans and multi-choice questions, cannot represent complete +trajectories, leading to suboptimal performance in tasks that require many +steps or repeated actions. Third, existing computer agents rely on +task-specific exemplars and overlook the similarity among tasks, resulting in +poor generalization to novel tasks. To address these challenges, we introduce +Synapse, featuring three key components: i) state abstraction, which filters +out task-irrelevant information from raw states, allowing more exemplars within +the limited context, ii) trajectory-as-exemplar prompting, which prompts the +LLM with complete trajectories of the abstracted states and actions for +improved multi-step decision-making, and iii) exemplar memory, which stores the +embeddings of exemplars and retrieves them via similarity search for +generalization to novel tasks. We evaluate Synapse on MiniWoB++, a standard +task suite, and Mind2Web, a real-world website benchmark. In MiniWoB++, Synapse +achieves a 99.2% average success rate (a 10% relative improvement) across 64 +tasks using demonstrations from only 48 tasks. Notably, Synapse is the first +ICL method to solve the book-flight task in MiniWoB++. Synapse also exhibits a +53% relative improvement in average step success rate over the previous +state-of-the-art prompting scheme in Mind2Web. +" +Language to Rewards for Robotic Skill Synthesis,Wenhao Yu,http://arxiv.org/pdf/2306.08647v2.pdf,2023-06-14,"['cs.ro', 'cs.ai', 'cs.lg']",2306.08647v2.pdf," Large language models (LLMs) have demonstrated exciting progress in acquiring +diverse new capabilities through in-context learning, ranging from logical +reasoning to code-writing. Robotics researchers have also explored using LLMs +to advance the capabilities of robotic control. However, since low-level robot +actions are hardware-dependent and underrepresented in LLM training corpora, +existing efforts in applying LLMs to robotics have largely treated LLMs as +semantic planners or relied on human-engineered control primitives to interface +with the robot. On the other hand, reward functions are shown to be flexible +representations that can be optimized for control policies to achieve diverse +tasks, while their semantic richness makes them suitable to be specified by +LLMs. In this work, we introduce a new paradigm that harnesses this realization +by utilizing LLMs to define reward parameters that can be optimized and +accomplish variety of robotic tasks. Using reward as the intermediate interface +generated by LLMs, we can effectively bridge the gap between high-level +language instructions or corrections to low-level robot actions. Meanwhile, +combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive +behavior creation experience where users can immediately observe the results +and provide feedback to the system. To systematically evaluate the performance +of our proposed method, we designed a total of 17 tasks for a simulated +quadruped robot and a dexterous manipulator robot. We demonstrate that our +proposed method reliably tackles 90% of the designed tasks, while a baseline +using primitive skills as the interface with Code-as-policies achieves 50% of +the tasks. We further validated our method on a real robot arm where complex +manipulation skills such as non-prehensile pushing emerge through our +interactive system. +" +Trained Transformers Learn Linear Models In-Context,Ruiqi Zhang,http://arxiv.org/pdf/2306.09927v3.pdf,2023-06-16,"['stat.ml', 'cs.ai', 'cs.cl', 'cs.lg']",2306.09927v3.pdf," Attention-based neural networks such as transformers have demonstrated a +remarkable ability to exhibit in-context learning (ICL): Given a short prompt +sequence of tokens from an unseen task, they can formulate relevant per-token +and next-token predictions without any parameter updates. By embedding a +sequence of labeled training data and unlabeled test data as a prompt, this +allows for transformers to behave like supervised learning algorithms. Indeed, +recent work has shown that when training transformer architectures over random +instances of linear regression problems, these models' predictions mimic those +of ordinary least squares. + Towards understanding the mechanisms underlying this phenomenon, we +investigate the dynamics of ICL in transformers with a single linear +self-attention layer trained by gradient flow on linear regression tasks. We +show that despite non-convexity, gradient flow with a suitable random +initialization finds a global minimum of the objective function. At this global +minimum, when given a test prompt of labeled examples from a new prediction +task, the transformer achieves prediction error competitive with the best +linear predictor over the test prompt distribution. We additionally +characterize the robustness of the trained transformer to a variety of +distribution shifts and show that although a number of shifts are tolerated, +shifts in the covariate distribution of the prompts are not. Motivated by this, +we consider a generalized ICL setting where the covariate distributions can +vary across prompts. We show that although gradient flow succeeds at finding a +global minimum in this setting, the trained transformer is still brittle under +mild covariate shifts. We complement this finding with experiments on large, +nonlinear transformer architectures which we show are more robust under +covariate shifts. +" +HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution,Eric Nguyen,http://arxiv.org/pdf/2306.15794v1.pdf,2023-06-27,"['cs.lg', 'q-bio.gn']",2306.15794v1.pdf," Genomic (DNA) sequences encode an enormous amount of information for gene +regulation and protein synthesis. Similar to natural language models, +researchers have proposed foundation models in genomics to learn generalizable +features from unlabeled genome data that can then be fine-tuned for downstream +tasks such as identifying regulatory elements. Due to the quadratic scaling of +attention, previous Transformer-based genomic models have used 512 to 4k tokens +as context (<0.001% of the human genome), significantly limiting the modeling +of long-range interactions in DNA. In addition, these methods rely on +tokenizers to aggregate meaningful DNA units, losing single nucleotide +resolution where subtle genetic variations can completely alter protein +function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a large +language model based on implicit convolutions was shown to match attention in +quality while allowing longer context lengths and lower time complexity. +Leveraging Hyenas new long-range capabilities, we present HyenaDNA, a genomic +foundation model pretrained on the human reference genome with context lengths +of up to 1 million tokens at the single nucleotide-level, an up to 500x +increase over previous dense attention-based models. HyenaDNA scales +sub-quadratically in sequence length (training up to 160x faster than +Transformer), uses single nucleotide tokens, and has full global context at +each layer. We explore what longer context enables - including the first use of +in-context learning in genomics for simple adaptation to novel tasks without +updating pretrained model weights. On fine-tuned benchmarks from the Nucleotide +Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 17 datasets +using a model with orders of magnitude less parameters and pretraining data. On +the GenomicBenchmarks, HyenaDNA surpasses SotA on all 8 datasets on average by ++9 accuracy points. +" +Generative Type Inference for Python,Yun Peng,http://arxiv.org/pdf/2307.09163v1.pdf,2023-07-18,['cs.se'],2307.09163v1.pdf," Python is a popular dynamic programming language, evidenced by its ranking as +the second most commonly used language on GitHub. However, its dynamic type +system can lead to potential type errors, leading researchers to explore +automatic type inference approaches for Python programs. The rule-based type +inference approaches can ensure the accuracy of predicted variable types, but +they suffer from low coverage problems. Supervised type inference approaches, +while feature-agnostic, require large, high-quality annotated datasets and are +limited to pre-defined types. As zero-shot approaches, the cloze-style +approaches reformulate the type inference problem into a fill-in-the-blank +problem. However, their performance is limited. + This paper introduces TypeGen, a few-shot generative type inference approach +that incorporates static domain knowledge from static analysis. TypeGen creates +chain-of-thought (COT) prompts by translating the type inference steps of +static analysis into prompts based on the type dependency graphs (TDGs), +enabling language models to learn from how static analysis infers types. By +combining COT prompts with code slices and type hints, TypeGen constructs +example prompts from human annotations. TypeGen only requires very few +annotated examples to teach language models to generate similar COT prompts via +in-context learning. Moreover, TypeGen enhances the interpretability of results +through the use of the input-explanation-output strategy. Experiments show that +TypeGen outperforms the best baseline Type4Py by 10.0% for argument type +prediction and 22.5% in return value type prediction in terms of top-1 Exact +Match by using only five examples. Furthermore, TypeGen achieves substantial +improvements of 27% to 84% compared to the zero-shot performance of large +language models with parameter sizes ranging from 1.3B to 175B in terms of +top-1 Exact Match. +" +Hypothesis Search: Inductive Reasoning with Language Models,Ruocheng Wang,http://arxiv.org/pdf/2309.05660v1.pdf,2023-09-11,"['cs.lg', 'cs.ai', 'cs.cl']",2309.05660v1.pdf," Inductive reasoning is a core problem-solving capacity: humans can identify +underlying principles from a few examples, which can then be robustly +generalized to novel scenarios. Recent work has evaluated large language models +(LLMs) on inductive reasoning tasks by directly prompting them yielding ""in +context learning."" This can work well for straightforward inductive tasks, but +performs very poorly on more complex tasks such as the Abstraction and +Reasoning Corpus (ARC). In this work, we propose to improve the inductive +reasoning ability of LLMs by generating explicit hypotheses at multiple levels +of abstraction: we prompt the LLM to propose multiple abstract hypotheses about +the problem, in natural language, then implement the natural language +hypotheses as concrete Python programs. These programs can be directly verified +by running on the observed examples and generalized to novel inputs. Because of +the prohibitive cost of generation with state-of-the-art LLMs, we consider a +middle step to filter the set of hypotheses that will be implemented into +programs: we either ask the LLM to summarize into a smaller set of hypotheses, +or ask human annotators to select a subset of the hypotheses. We verify our +pipeline's effectiveness on the ARC visual inductive reasoning benchmark, its +variant 1D-ARC, and string transformation dataset SyGuS. On a random 40-problem +subset of ARC, our automated pipeline using LLM summaries achieves 27.5% +accuracy, significantly outperforming the direct prompting baseline (accuracy +of 12.5%). With the minimal human input of selecting from LLM-generated +candidates, the performance is boosted to 37.5%. (And we argue this is a lower +bound on the performance of our approach without filtering.) Our ablation +studies show that abstract hypothesis generation and concrete program +representations are both beneficial for LLMs to perform inductive reasoning +tasks. +" +How FaR Are Large Language Models From Agents with Theory-of-Mind?,Pei Zhou,http://arxiv.org/pdf/2310.03051v1.pdf,2023-10-04,"['cs.cl', 'cs.ai']",2310.03051v1.pdf," ""Thinking is for Doing."" Humans can infer other people's mental states from +observations--an ability called Theory-of-Mind (ToM)--and subsequently act +pragmatically on those inferences. Existing question answering benchmarks such +as ToMi ask models questions to make inferences about beliefs of characters in +a story, but do not test whether models can then use these inferences to guide +their actions. We propose a new evaluation paradigm for large language models +(LLMs): Thinking for Doing (T4D), which requires models to connect inferences +about others' mental states to actions in social scenarios. Experiments on T4D +demonstrate that LLMs such as GPT-4 and PaLM 2 seemingly excel at tracking +characters' beliefs in stories, but they struggle to translate this capability +into strategic action. Our analysis reveals the core challenge for LLMs lies in +identifying the implicit inferences about mental states without being +explicitly asked about as in ToMi, that lead to choosing the correct action in +T4D. To bridge this gap, we introduce a zero-shot prompting framework, Foresee +and Reflect (FaR), which provides a reasoning structure that encourages LLMs to +anticipate future challenges and reason about potential actions. FaR boosts +GPT-4's performance from 50% to 71% on T4D, outperforming other prompting +methods such as Chain-of-Thought and Self-Ask. Moreover, FaR generalizes to +diverse out-of-distribution story structures and scenarios that also require +ToM inferences to choose an action, consistently outperforming other methods +including few-shot in-context learning. +" +Entity Matching using Large Language Models,Ralph Peeters,http://arxiv.org/pdf/2310.11244v1.pdf,2023-10-17,"['cs.cl', 'cs.lg']",2310.11244v1.pdf," Entity Matching is the task of deciding whether two entity descriptions refer +to the same real-world entity. Entity Matching is a central step in most data +integration pipelines and an enabler for many e-commerce applications which +require to match products offers from different vendors. State-of-the-art +entity matching methods often rely on pre-trained language models (PLMs) such +as BERT or RoBERTa. Two major drawbacks of these models for entity matching are +that (i) the models require significant amounts of task-specific training data +and (ii) the fine-tuned models are not robust concerning out-of-distribution +entities. In this paper, we investigate using large language models (LLMs) for +entity matching as a less domain-specific training data reliant and more robust +alternative to PLM-based matchers. Our study covers hosted LLMs, such as GPT3.5 +and GPT4, as well as open source LLMs based on Llama2 which can be run locally. +We evaluate these models in a zero-shot scenario as well as a scenario where +task-specific training data is available. We compare different prompt designs +as well as the prompt sensitivity of the models in the zero-shot scenario. We +investigate (i) the selection of in-context demonstrations, (ii) the generation +of matching rules, as well as (iii) fine-tuning GPT3.5 in the second scenario +using the same pool of training data across the different approaches. Our +experiments show that GPT4 without any task-specific training data outperforms +fine-tuned PLMs (RoBERTa and Ditto) on three out of five benchmark datasets +reaching F1 scores around 90%. The experiments with in-context learning and +rule generation show that all models beside of GPT4 benefit from these +techniques (on average 5.9% and 2.2% F1), while GPT4 does not need such +additional guidance in most cases... +" +CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment,Jixiang Hong,http://arxiv.org/pdf/2310.16271v1.pdf,2023-10-25,"['cs.cl', 'cs.ai']",2310.16271v1.pdf," Language models trained on large-scale corpus often generate content that is +harmful, toxic, or contrary to human preferences, making their alignment with +human values a critical concern. Reinforcement learning from human feedback +(RLHF) with algorithms like PPO is a prevalent approach for alignment but is +often complex, unstable, and resource-intensive. Recently, ranking-based +alignment methods have emerged, offering stability and effectiveness by +replacing the RL framework with supervised fine-tuning, but they are costly due +to the need for annotated data. Considering that existing large language models +(LLMs) like ChatGPT are already relatively well-aligned and cost-friendly, +researchers have begun to align the language model with human preference from +AI feedback. The common practices, which unidirectionally distill the +instruction-following responses from LLMs, are constrained by their bottleneck. +Thus we introduce CycleAlign to distill alignment capabilities from +parameter-invisible LLMs (black-box) to a parameter-visible model (white-box) +in an iterative manner. With in-context learning (ICL) as the core of the +cycle, the black-box models are able to rank the model-generated responses +guided by human-craft instruction and demonstrations about their preferences. +During iterative interaction, the white-box models also have a judgment about +responses generated by them. Consequently, the agreement ranking could be +viewed as a pseudo label to dynamically update the in-context demonstrations +and improve the preference ranking ability of black-box models. Through +multiple interactions, the CycleAlign framework could align the white-box model +with the black-box model effectively in a low-resource way. Empirical results +illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing +methods, and achieves the state-of-the-art performance in alignment with human +value. +" +Transformers are Efficient In-Context Estimators for Wireless Communication,Vicram Rajagopalan,http://arxiv.org/pdf/2311.00226v1.pdf,2023-11-01,"['eess.sp', 'cs.lg']",2311.00226v1.pdf," Pre-trained transformers can perform in-context learning, where they adapt to +a new task using only a small number of prompts without any explicit model +optimization. Inspired by this attribute, we propose a novel approach, called +in-context estimation, for the canonical communication problem of estimating +transmitted symbols from received symbols. A communication channel is +essentially a noisy function that maps transmitted symbols to received symbols, +and this function can be represented by an unknown parameter whose statistics +depend on an (also unknown) latent context. Conventional approaches ignore this +hierarchical structure and simply attempt to use known transmissions, called +pilots, to perform a least-squares estimate of the channel parameter, which is +then used to estimate successive, unknown transmitted symbols. We make the +basic connection that transformers show excellent contextual sequence +completion with a few prompts, and so they should be able to implicitly +determine the latent context from pilot symbols to perform end-to-end +in-context estimation of transmitted symbols. Furthermore, the transformer +should use information efficiently, i.e., it should utilize any pilots received +to attain the best possible symbol estimates. Through extensive simulations, we +show that in-context estimation not only significantly outperforms standard +approaches, but also achieves the same performance as an estimator with perfect +knowledge of the latent context within a few context examples. Thus, we make a +strong case that transformers are efficient in-context estimators in the +communication setting. +" +Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels,Bang Yang,http://arxiv.org/pdf/2307.01969v1.pdf,2023-07-05,['cs.cv'],2307.01969v1.pdf," Generating an informative and attractive title for the product is a crucial +task for e-commerce. Most existing works follow the standard multimodal natural +language generation approaches, e.g., image captioning, and employ the large +scale of human-labelled datasets to train desirable models. However, for novel +products, especially in a different domain, there are few existing labelled +data. In this paper, we propose a prompt-based approach, i.e., the Multimodal +Prompt Learning framework, to accurately and efficiently generate titles for +novel products with limited labels. We observe that the core challenges of +novel product title generation are the understanding of novel product +characteristics and the generation of titles in a novel writing style. To this +end, we build a set of multimodal prompts from different modalities to preserve +the corresponding characteristics and writing styles of novel products. As a +result, with extremely limited labels for training, the proposed method can +retrieve the multimodal prompts to generate desirable titles for novel +products. The experiments and analyses are conducted on five novel product +categories under both the in-domain and out-of-domain experimental settings. +The results show that, with only 1% of downstream labelled data for training, +our proposed approach achieves the best few-shot results and even achieves +competitive results with fully-supervised methods trained on 100% of training +data; With the full labelled data for training, our method achieves +state-of-the-art results. +" +Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt,Xiaocui Yang,http://arxiv.org/pdf/2305.10169v2.pdf,2023-05-17,['cs.mm'],2305.10169v2.pdf," We have witnessed the rapid proliferation of multimodal data on numerous +social media platforms. Conventional studies typically require massive labeled +data to train models for Multimodal Aspect-Based Sentiment Analysis (MABSA). +However, collecting and annotating fine-grained multimodal data for MABSA is +tough. To alleviate the above issue, we perform three MABSA-related tasks with +quite a small number of labeled multimodal samples. We first build diverse and +comprehensive multimodal few-shot datasets according to the data distribution. +To capture the specific prompt for each aspect term in a few-shot scenario, we +propose a novel Generative Multimodal Prompt (GMP) model for MABSA, which +includes the Multimodal Encoder module and the N-Stream Decoders module. We +further introduce a subtask to predict the number of aspect terms in each +instance to construct the multimodal prompt. Extensive experiments on two +datasets demonstrate that our approach outperforms strong baselines on two +MABSA-related tasks in the few-shot setting. +" +VIMA: General Robot Manipulation with Multimodal Prompts,Yunfan Jiang,http://arxiv.org/pdf/2210.03094v2.pdf,2022-10-06,"['cs.ro', 'cs.ai', 'cs.lg']",2210.03094v2.pdf," Prompt-based learning has emerged as a successful paradigm in natural +language processing, where a single general-purpose language model can be +instructed to perform any task specified by input prompts. Yet task +specification in robotics comes in various forms, such as imitating one-shot +demonstrations, following language instructions, and reaching visual goals. +They are often considered different tasks and tackled by specialized models. We +show that a wide spectrum of robot manipulation tasks can be expressed with +multimodal prompts, interleaving textual and visual tokens. Accordingly, we +develop a new simulation benchmark that consists of thousands of +procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert +trajectories for imitation learning, and a four-level evaluation protocol for +systematic generalization. We design a transformer-based robot agent, VIMA, +that processes these prompts and outputs motor actions autoregressively. VIMA +features a recipe that achieves strong model scalability and data efficiency. +It outperforms alternative designs in the hardest zero-shot generalization +setting by up to $2.9\times$ task success rate given the same training data. +With $10\times$ less training data, VIMA still performs $2.7\times$ better than +the best competing variant. Code and video demos are available at +https://vimalabs.github.io/ +" +Delving into Multimodal Prompting for Fine-grained Visual Classification,Xin Jiang,http://arxiv.org/pdf/2309.08912v1.pdf,2023-09-16,"['cs.cv', 'cs.mm']",2309.08912v1.pdf," Fine-grained visual classification (FGVC) involves categorizing fine +subdivisions within a broader category, which poses challenges due to subtle +inter-class discrepancies and large intra-class variations. However, prevailing +approaches primarily focus on uni-modal visual concepts. Recent advancements in +pre-trained vision-language models have demonstrated remarkable performance in +various high-level vision tasks, yet the applicability of such models to FGVC +tasks remains uncertain. In this paper, we aim to fully exploit the +capabilities of cross-modal description to tackle FGVC tasks and propose a +novel multimodal prompting solution, denoted as MP-FGVC, based on the +contrastive language-image pertaining (CLIP) model. Our MP-FGVC comprises a +multimodal prompts scheme and a multimodal adaptation scheme. The former +includes Subcategory-specific Vision Prompt (SsVP) and Discrepancy-aware Text +Prompt (DaTP), which explicitly highlights the subcategory-specific +discrepancies from the perspectives of both vision and language. The latter +aligns the vision and text prompting elements in a common semantic space, +facilitating cross-modal collaborative reasoning through a Vision-Language +Fusion Module (VLFM) for further improvement on FGVC. Moreover, we tailor a +two-stage optimization strategy for MP-FGVC to fully leverage the pre-trained +CLIP model and expedite efficient adaptation for FGVC. Extensive experiments +conducted on four FGVC datasets demonstrate the effectiveness of our MP-FGVC. +" +Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation,Shihao Zou,http://arxiv.org/pdf/2310.04456v1.pdf,2023-10-04,"['cs.cl', 'cs.sd', 'eess.as']",2310.04456v1.pdf," Emotion Recognition in Conversation (ERC) plays an important role in driving +the development of human-machine interaction. Emotions can exist in multiple +modalities, and multimodal ERC mainly faces two problems: (1) the noise problem +in the cross-modal information fusion process, and (2) the prediction problem +of less sample emotion labels that are semantically similar but different +categories. To address these issues and fully utilize the features of each +modality, we adopted the following strategies: first, deep emotion cues +extraction was performed on modalities with strong representation ability, and +feature filters were designed as multimodal prompt information for modalities +with weak representation ability. Then, we designed a Multimodal Prompt +Transformer (MPT) to perform cross-modal information fusion. MPT embeds +multimodal fusion information into each attention layer of the Transformer, +allowing prompt information to participate in encoding textual features and +being fused with multi-level textual information to obtain better multimodal +fusion features. Finally, we used the Hybrid Contrastive Learning (HCL) +strategy to optimize the model's ability to handle labels with few samples. +This strategy uses unsupervised contrastive learning to improve the +representation ability of multimodal fusion and supervised contrastive learning +to mine the information of labels with few samples. Experimental results show +that our proposed model outperforms state-of-the-art models in ERC on two +benchmark datasets. +" +2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection,Yunkang Cao,http://arxiv.org/pdf/2306.09067v2.pdf,2023-06-15,['cs.cv'],2306.09067v2.pdf," This technical report introduces the winning solution of the team Segment Any +Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. +Going beyond uni-modal prompt, e.g., language prompt, we present a novel +framework, i.e., Segment Any Anomaly + (SAA$+$), for zero-shot anomaly +segmentation with multi-modal prompts for the regularization of cascaded modern +foundation models. Inspired by the great zero-shot generalization ability of +foundation models like Segment Anything, we first explore their assembly (SAA) +to leverage diverse multi-modal prior knowledge for anomaly localization. +Subsequently, we further introduce multimodal prompts (SAA$+$) derived from +domain expert knowledge and target image context to enable the non-parameter +adaptation of foundation models to anomaly segmentation. The proposed SAA$+$ +model achieves state-of-the-art performance on several anomaly segmentation +benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will +release the code of our winning solution for the CVPR2023 VAN. +" +Multimodal Prompt Retrieval for Generative Visual Question Answering,Timothy Ossowski,http://arxiv.org/pdf/2306.17675v1.pdf,2023-06-30,"['cs.cv', 'cs.ai']",2306.17675v1.pdf," Recent years have witnessed impressive results of pre-trained vision-language +models on knowledge-intensive tasks such as visual question answering (VQA). +Despite the recent advances in VQA, existing methods mainly adopt a +discriminative formulation that predicts answers within a pre-defined label +set, leading to easy overfitting on low-resource domains with limited labeled +data (e.g., medicine) and poor generalization under domain shift to another +dataset. To tackle this limitation, we propose a novel generative model +enhanced by multimodal prompt retrieval (MPR) that integrates retrieved prompts +and multimodal features to generate answers in free text. Our generative model +enables rapid zero-shot dataset adaptation to unseen data distributions and +open-set answer labels across datasets. Our experiments on medical VQA tasks +show that MPR outperforms its non-retrieval counterpart by up to 30% accuracy +points in a few-shot domain adaptation setting. +" +Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts,Deniz Engin,http://arxiv.org/pdf/2309.15915v1.pdf,2023-09-27,['cs.cv'],2309.15915v1.pdf," Recent vision-language models are driven by large-scale pretrained models. +However, adapting pretrained models on limited data presents challenges such as +overfitting, catastrophic forgetting, and the cross-modal gap between vision +and language. We introduce a parameter-efficient method to address these +challenges, combining multimodal prompt learning and a transformer-based +mapping network, while keeping the pretrained models frozen. Our experiments on +several video question answering benchmarks demonstrate the superiority of our +approach in terms of performance and parameter efficiency on both zero-shot and +few-shot settings. Our code is available at https://engindeniz.github.io/vitis. +" +Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting,Syed Talal Wasim,http://arxiv.org/pdf/2304.03307v1.pdf,2023-04-06,"['cs.cv', 'eess.iv']",2304.03307v1.pdf," Adopting contrastive image-text pretrained models like CLIP towards video +classification has gained attention due to its cost-effectiveness and +competitive performance. However, recent works in this area face a trade-off. +Finetuning the pretrained model to achieve strong supervised performance +results in low zero-shot generalization. Similarly, freezing the backbone to +retain zero-shot capability causes significant drop in supervised accuracy. +Because of this, recent works in literature typically train separate models for +supervised and zero-shot action recognition. In this work, we propose a +multimodal prompt learning scheme that works to balance the supervised and +zero-shot performance under a single unified training. Our prompting approach +on the vision side caters for three aspects: 1) Global video-level prompts to +model the data distribution; 2) Local frame-level prompts to provide per-frame +discriminative conditioning; and 3) a summary prompt to extract a condensed +video representation. Additionally, we define a prompting scheme on the text +side to augment the textual context. Through this prompting scheme, we can +achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and +UCF101 while remaining competitive in the supervised setting. By keeping the +pretrained backbone frozen, we optimize a much lower number of parameters and +retain the existing general representation which helps achieve the strong +zero-shot performance. Our codes/models are released at +https://github.com/TalalWasim/Vita-CLIP. +" +Similarity-Aware Multimodal Prompt Learning for Fake News Detection,Ye Jiang,http://arxiv.org/pdf/2304.04187v3.pdf,2023-04-09,['cs.cl'],2304.04187v3.pdf," The standard paradigm for fake news detection mainly utilizes text +information to model the truthfulness of news. However, the discourse of online +fake news is typically subtle and it requires expert knowledge to use textual +information to debunk fake news. Recently, studies focusing on multimodal fake +news detection have outperformed text-only methods. Recent approaches utilizing +the pre-trained model to extract unimodal features, or fine-tuning the +pre-trained model directly, have become a new paradigm for detecting fake news. +Again, this paradigm either requires a large number of training instances, or +updates the entire set of pre-trained model parameters, making real-world fake +news detection impractical. Furthermore, traditional multimodal methods fuse +the cross-modal features directly without considering that the uncorrelated +semantic representation might inject noise into the multimodal features. This +paper proposes a Similarity-Aware Multimodal Prompt Learning (SAMPLE) +framework. First, we incorporate prompt learning into multimodal fake news +detection. Prompt learning, which only tunes prompts with a frozen language +model, can reduce memory usage significantly and achieve comparable +performances, compared with fine-tuning. We analyse three prompt templates with +a soft verbalizer to detect fake news. In addition, we introduce the +similarity-aware fusing method to adaptively fuse the intensity of multimodal +representation and mitigate the noise injection via uncorrelated cross-modal +features. For evaluation, SAMPLE surpasses the F1 and the accuracies of +previous works on two benchmark multimodal datasets, demonstrating the +effectiveness of the proposed method in detecting fake news. In addition, +SAMPLE also is superior to other approaches regardless of few-shot and +data-rich settings. +" +Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion,Nisha Huang,http://arxiv.org/pdf/2209.13360v2.pdf,2022-09-27,['cs.cv'],2209.13360v2.pdf," Digital art synthesis is receiving increasing attention in the multimedia +community because of engaging the public with art effectively. Current digital +art synthesis methods usually use single-modality inputs as guidance, thereby +limiting the expressiveness of the model and the diversity of generated +results. To solve this problem, we propose the multimodal guided artwork +diffusion (MGAD) model, which is a diffusion-based digital artwork generation +approach that utilizes multimodal prompts as guidance to control the +classifier-free diffusion model. Additionally, the contrastive language-image +pretraining (CLIP) model is used to unify text and image modalities. Extensive +experimental results on the quality and quantity of the generated digital art +paintings confirm the effectiveness of the combination of the diffusion model +and multimodal guidance. Code is available at +https://github.com/haha-lisa/MGAD-multimodal-guided-artwork-diffusion. +" +Multimodal Prompting with Missing Modalities for Visual Recognition,Yi-Lun Lee,http://arxiv.org/pdf/2303.03369v2.pdf,2023-03-06,['cs.cv'],2303.03369v2.pdf," In this paper, we tackle two challenges in multimodal learning for visual +recognition: 1) when missing-modality occurs either during training or testing +in real-world situations; and 2) when the computation resources are not +available to finetune on heavy transformer models. To this end, we propose to +utilize prompt learning and mitigate the above two challenges together. +Specifically, our modality-missing-aware prompts can be plugged into multimodal +transformers to handle general missing-modality cases, while only requiring +less than 1% learnable parameters compared to training the entire model. We +further explore the effect of different prompt configurations and analyze the +robustness to missing modality. Extensive experiments are conducted to show the +effectiveness of our prompt learning framework that improves the performance +under various missing-modality cases, while alleviating the requirement of +heavy model re-training. Code is available. +" +Audio Visual Language Maps for Robot Navigation,Chenguang Huang,http://arxiv.org/pdf/2303.07522v2.pdf,2023-03-13,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.lg']",2303.07522v2.pdf," While interacting in the world is a multi-sensory experience, many robots +continue to predominantly rely on visual perception to map and navigate in +their environments. In this work, we propose Audio-Visual-Language Maps +(AVLMaps), a unified 3D spatial map representation for storing cross-modal +information from audio, visual, and language cues. AVLMaps integrate the +open-vocabulary capabilities of multimodal foundation models pre-trained on +Internet-scale data by fusing their features into a centralized 3D voxel grid. +In the context of navigation, we show that AVLMaps enable robot systems to +index goals in the map based on multimodal queries, e.g., textual descriptions, +images, or audio snippets of landmarks. In particular, the addition of audio +information enables robots to more reliably disambiguate goal locations. +Extensive experiments in simulation show that AVLMaps enable zero-shot +multimodal goal navigation from multimodal prompts and provide 50% better +recall in ambiguous scenarios. These capabilities extend to mobile robots in +the real world - navigating to landmarks referring to visual, audio, and +spatial concepts. Videos and code are available at: https://avlmaps.github.io. +" +Multitask Multimodal Prompted Training for Interactive Embodied Task Completion,Georgios Pantazopoulos,http://arxiv.org/pdf/2311.04067v1.pdf,2023-11-07,"['cs.lg', 'cs.ai', 'cs.cv']",2311.04067v1.pdf," Interactive and embodied tasks pose at least two fundamental challenges to +existing Vision & Language (VL) models, including 1) grounding language in +trajectories of actions and observations, and 2) referential disambiguation. To +tackle these challenges, we propose an Embodied MultiModal Agent (EMMA): a +unified encoder-decoder model that reasons over images and trajectories, and +casts action prediction as multimodal text generation. By unifying all tasks as +text generation, EMMA learns a language of actions which facilitates transfer +across tasks. Different to previous modular approaches with independently +trained components, we use a single multitask model where each task contributes +to goal completion. EMMA performs on par with similar models on several VL +benchmarks and sets a new state-of-the-art performance (36.81% success rate) on +the Dialog-guided Task Completion (DTC), a benchmark to evaluate dialog-guided +agents in the Alexa Arena +" +MaPLe: Multi-modal Prompt Learning,Muhammad Uzair Khattak,http://arxiv.org/pdf/2210.03117v3.pdf,2022-10-06,['cs.cv'],2210.03117v3.pdf," Pre-trained vision-language (V-L) models such as CLIP have shown excellent +generalization ability to downstream tasks. However, they are sensitive to the +choice of input text prompts and require careful selection of prompt templates +to perform well. Inspired by the Natural Language Processing (NLP) literature, +recent CLIP adaptation approaches learn prompts as the textual inputs to +fine-tune CLIP for downstream tasks. We note that using prompting to adapt +representations in a single branch of CLIP (language or vision) is sub-optimal +since it does not allow the flexibility to dynamically adjust both +representation spaces on a downstream task. In this work, we propose +Multi-modal Prompt Learning (MaPLe) for both vision and language branches to +improve alignment between the vision and language representations. Our design +promotes strong coupling between the vision-language prompts to ensure mutual +synergy and discourages learning independent uni-modal solutions. Further, we +learn separate prompts across different early stages to progressively model the +stage-wise feature relationships to allow rich context learning. We evaluate +the effectiveness of our approach on three representative tasks of +generalization to novel classes, new target datasets and unseen domain shifts. +Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable +performance and achieves an absolute gain of 3.45% on novel classes and 2.72% +on overall harmonic-mean, averaged over 11 diverse image recognition datasets. +Our code and pre-trained models are available at +https://github.com/muzairkhattak/multimodal-prompt-learning. +" +Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts,Xiaocui Yang,http://arxiv.org/pdf/2211.06607v2.pdf,2022-11-12,"['cs.cl', 'cs.mm']",2211.06607v2.pdf," Multimodal sentiment analysis has gained significant attention due to the +proliferation of multimodal content on social media. However, existing studies +in this area rely heavily on large-scale supervised data, which is +time-consuming and labor-intensive to collect. Thus, there is a need to address +the challenge of few-shot multimodal sentiment analysis. To tackle this +problem, we propose a novel method called Multimodal Probabilistic Fusion +Prompts (MultiPoint) that leverages diverse cues from different modalities for +multimodal sentiment detection in the few-shot scenario. Specifically, we start +by introducing a Consistently Distributed Sampling approach called CDS, which +ensures that the few-shot dataset has the same category distribution as the +full dataset. Unlike previous approaches primarily using prompts based on the +text modality, we design unified multimodal prompts to reduce discrepancies +between different modalities and dynamically incorporate multimodal +demonstrations into the context of each multimodal instance. To enhance the +model's robustness, we introduce a probabilistic fusion method to fuse output +predictions from multiple diverse prompts for each input. Our extensive +experiments on six datasets demonstrate the effectiveness of our approach. +First, our method outperforms strong baselines in the multimodal few-shot +setting. Furthermore, under the same amount of data (1% of the full dataset), +our CDS-based experimental results significantly outperform those based on +previously sampled datasets constructed from the same number of instances of +each class. +" +Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing,Alberto Baldrati,http://arxiv.org/pdf/2304.02051v2.pdf,2023-04-04,"['cs.cv', 'cs.ai', 'cs.mm']",2304.02051v2.pdf," Fashion illustration is used by designers to communicate their vision and to +bring the design idea from conceptualization to realization, showing how +clothes interact with the human body. In this context, computer vision can thus +be used to improve the fashion design process. Differently from previous works +that mainly focused on the virtual try-on of garments, we propose the task of +multimodal-conditioned fashion image editing, guiding the generation of +human-centric fashion images by following multimodal prompts, such as text, +human body poses, and garment sketches. We tackle this problem by proposing a +new architecture based on latent diffusion models, an approach that has not +been used before in the fashion domain. Given the lack of existing datasets +suitable for the task, we also extend two existing fashion datasets, namely +Dress Code and VITON-HD, with multimodal annotations collected in a +semi-automatic manner. Experimental results on these new datasets demonstrate +the effectiveness of our proposal, both in terms of realism and coherence with +the given multimodal inputs. Source code and collected multimodal annotations +are publicly available at: +https://github.com/aimagelab/multimodal-garment-designer. +" +Parameter-efficient Tuning of Large-scale Multimodal Foundation Model,Haixin Wang,http://arxiv.org/pdf/2305.08381v3.pdf,2023-05-15,['cs.cv'],2305.08381v3.pdf," Driven by the progress of large-scale pre-training, parameter-efficient +transfer learning has gained immense popularity across different subfields of +Artificial Intelligence. The core is to adapt the model to downstream tasks +with only a small set of parameters. Recently, researchers have leveraged such +proven techniques in multimodal tasks and achieve promising results. However, +two critical issues remain unresolved: how to further reduce the complexity +with lightweight design and how to boost alignment between modalities under +extremely low parameters. In this paper, we propose A graceful prompt framework +for cross-modal transfer (Aurora) to overcome these challenges. Considering the +redundancy in existing architectures, we first utilize the mode approximation +to generate 0.1M trainable parameters to implement the multimodal prompt +tuning, which explores the low intrinsic dimension with only 0.04% parameters +of the pre-trained model. Then, for better modality alignment, we propose the +Informative Context Enhancement and Gated Query Transformation module under +extremely few parameters scenes. A thorough evaluation on six cross-modal +benchmarks shows that it not only outperforms the state-of-the-art but even +outperforms the full fine-tuning approach. Our code is available at: +https://github.com/WillDreamer/Aurora. +" +RM-PRT: Realistic Robotic Manipulation Simulator and Benchmark with Progressive Reasoning Tasks,Pengzhen Ren,http://arxiv.org/pdf/2306.11335v2.pdf,2023-06-20,"['cs.ro', 'cs.ai', 'cs.cv', 'cs.lg']",2306.11335v2.pdf," Recently, the advent of pre-trained large-scale language models (LLMs) like +ChatGPT and GPT-4 have significantly advanced the machine's natural language +understanding capabilities. This breakthrough has allowed us to seamlessly +integrate these open-source LLMs into a unified robot simulator environment to +help robots accurately understand and execute human natural language +instructions. To this end, in this work, we introduce a realistic robotic +manipulation simulator and build a Robotic Manipulation with Progressive +Reasoning Tasks (RM-PRT) benchmark on this basis. Specifically, the RM-PRT +benchmark builds a new high-fidelity digital twin scene based on Unreal Engine +5, which includes 782 categories, 2023 objects, and 15K natural language +instructions generated by ChatGPT for a detailed evaluation of robot +manipulation. We propose a general pipeline for the RM-PRT benchmark that takes +as input multimodal prompts containing natural language instructions and +automatically outputs actions containing the movement and position transitions. +We set four natural language understanding tasks with progressive reasoning +levels and evaluate the robot's ability to understand natural language +instructions in two modes of adsorption and grasping. In addition, we also +conduct a comprehensive analysis and comparison of the differences and +advantages of 10 different LLMs in instruction understanding and generation +quality. We hope the new simulator and benchmark will facilitate future +research on language-guided robotic manipulation. Project website: +https://necolizer.github.io/RM-PRT/ . +" +Reframing Instructional Prompts to GPTk's Language,Swaroop Mishra,http://arxiv.org/pdf/2109.07830v3.pdf,2021-09-16,"['cs.cl', 'cs.ai', 'cs.lg']",2109.07830v3.pdf," What kinds of instructional prompts are easier to follow for Language Models +(LMs)? We study this question by conducting extensive empirical analysis that +shed light on important features of successful instructional prompts. +Specifically, we study several classes of reframing techniques for manual +reformulation of prompts into more effective ones. Some examples include +decomposing a complex task instruction into multiple simpler tasks or itemizing +instructions into sequential steps. Our experiments compare the zero-shot and +few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks +across 6 categories. Compared with original instructions, our reframed +instructions lead to significant improvements across LMs with different sizes. +For example, the same reframed prompts boost few-shot performance of +GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all +tasks. Furthermore, reframed instructions reduce the number of examples +required to prompt LMs in the few-shot setting. We hope these +empirically-driven techniques will pave the way towards more effective future +prompting algorithms. +" +Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums,Kazuaki Kashihara,http://arxiv.org/pdf/2303.05400v1.pdf,2023-03-05,"['cs.cl', 'cs.ai', 'cs.cr']",2303.05400v1.pdf," With recent trends indicating cyber crimes increasing in both frequency and +cost, it is imperative to develop new methods that leverage data-rich hacker +forums to assist in combating ever evolving cyber threats. Defining +interactions within these forums is critical as it facilitates identifying +highly skilled users, which can improve prediction of novel threats and future +cyber attacks. We propose a method called Next Paragraph Prediction with +Instructional Prompting (NPP-IP) to predict thread structures while grounded on +the context around posts. This is the first time to apply an instructional +prompting approach to the cybersecurity domain. We evaluate our NPP-IP with the +Reddit dataset and Hacker Forums dataset that has posts and thread structures +of real hacker forums' threads, and compare our method's performance with +existing methods. The experimental evaluation shows that our proposed method +can predict the thread structure significantly better than existing methods +allowing for better social network prediction based on forum interactions. +" +Red Teaming Language Model Detectors with Language Models,Zhouxing Shi,http://arxiv.org/pdf/2305.19713v2.pdf,2023-05-31,"['cs.cl', 'cs.lg']",2305.19713v2.pdf," The prevalence and strong capability of large language models (LLMs) present +significant safety and ethical risks if exploited by malicious users. To +prevent the potentially deceptive usage of LLMs, recent works have proposed +algorithms to detect LLM-generated text and protect LLMs. In this paper, we +investigate the robustness and reliability of these LLM detectors under +adversarial attacks. We study two types of attack strategies: 1) replacing +certain words in an LLM's output with their synonyms given the context; 2) +automatically searching for an instructional prompt to alter the writing style +of the generation. In both strategies, we leverage an auxiliary LLM to generate +the word replacements or the instructional prompt. Different from previous +works, we consider a challenging setting where the auxiliary LLM can also be +protected by a detector. Experiments reveal that our attacks effectively +compromise the performance of all detectors in the study with plausible +generations, underscoring the urgent need to improve the robustness of +LLM-generated text detection systems. +" +Large Language Models Encode Clinical Knowledge,Karan Singhal,http://arxiv.org/pdf/2212.13138v1.pdf,2022-12-26,['cs.cl'],2212.13138v1.pdf," Large language models (LLMs) have demonstrated impressive capabilities in +natural language understanding and generation, but the quality bar for medical +and clinical applications is high. Today, attempts to assess models' clinical +knowledge typically rely on automated evaluations on limited benchmarks. There +is no standard to evaluate model predictions and reasoning across a breadth of +tasks. To address this, we present MultiMedQA, a benchmark combining six +existing open question answering datasets spanning professional medical exams, +research, and consumer queries; and HealthSearchQA, a new free-response dataset +of medical questions searched online. We propose a framework for human +evaluation of model answers along multiple axes including factuality, +precision, possible harm, and bias. In addition, we evaluate PaLM (a +540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on +MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves +state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, +MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US +Medical License Exam questions), surpassing prior state-of-the-art by over 17%. +However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve +this we introduce instruction prompt tuning, a parameter-efficient approach for +aligning LLMs to new domains using a few exemplars. The resulting model, +Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show +that comprehension, recall of knowledge, and medical reasoning improve with +model scale and instruction prompt tuning, suggesting the potential utility of +LLMs in medicine. Our human evaluations reveal important limitations of today's +models, reinforcing the importance of both evaluation frameworks and method +development in creating safe, helpful LLM models for clinical applications. +" +Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering,Wenjin Wang,http://arxiv.org/pdf/2306.00526v4.pdf,2023-06-01,"['cs.cl', 'cs.ai', 'cs.cv']",2306.00526v4.pdf," Layout-aware pre-trained models has achieved significant progress on document +image question answering. They introduce extra learnable modules into existing +language models to capture layout information within document images from text +bounding box coordinates obtained by OCR tools. However, extra modules +necessitate pre-training on extensive document images. This prevents these +methods from directly utilizing off-the-shelf instruction-tuning language +foundation models, which have recently shown promising potential in zero-shot +learning. Instead, in this paper, we find that instruction-tuning language +models like Claude and ChatGPT can understand layout by spaces and line breaks. +Based on this observation, we propose the LAyout and Task aware Instruction +Prompt (LATIN-Prompt), which consists of layout-aware document content and +task-aware instruction. Specifically, the former uses appropriate spaces and +line breaks to recover the layout information among text segments obtained by +OCR tools, and the latter ensures that generated answers adhere to formatting +requirements. Moreover, we propose the LAyout and Task aware Instruction Tuning +(LATIN-Tuning) to improve the performance of small instruction-tuning models +like Alpaca. Experimental results show that LATIN-Prompt enables zero-shot +performance of Claude and ChatGPT to be comparable to the fine-tuning +performance of SOTAs on document image question answering, and LATIN-Tuning +enhances the zero-shot performance of Alpaca significantly. For example, +LATIN-Prompt improves the performance of Claude and ChatGPT on DocVQA by 263% +and 20% respectively. LATIN-Tuning improves the performance of Alpaca on DocVQA +by 87.7%. Quantitative and qualitative analyses demonstrate the effectiveness +of LATIN-Prompt and LATIN-Tuning. We provide the code in supplementary and will +release it to facilitate future research. +" +InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction,Xiao Wang,http://arxiv.org/pdf/2304.08085v1.pdf,2023-04-17,"['cs.cl', 'cs.ai']",2304.08085v1.pdf," Large language models have unlocked strong multi-task capabilities from +reading instructive prompts. However, recent studies have shown that existing +large models still have difficulty with information extraction tasks. For +example, gpt-3.5-turbo achieved an F1 score of 18.22 on the Ontonotes dataset, +which is significantly lower than the state-of-the-art performance. In this +paper, we propose InstructUIE, a unified information extraction framework based +on instruction tuning, which can uniformly model various information extraction +tasks and capture the inter-task dependency. To validate the proposed method, +we introduce IE INSTRUCTIONS, a benchmark of 32 diverse information extraction +datasets in a unified text-to-text format with expert-written instructions. +Experimental results demonstrate that our method achieves comparable +performance to Bert in supervised settings and significantly outperforms the +state-of-the-art and gpt3.5 in zero-shot settings. +" +Camoscio: an Italian Instruction-tuned LLaMA,Andrea Santilli,http://arxiv.org/pdf/2307.16456v1.pdf,2023-07-31,['cs.cl'],2307.16456v1.pdf," In recent years Large Language Models (LLMs) have increased the state of the +art on several natural language processing tasks. However, their accessibility +is often limited to paid API services, posing challenges for researchers in +conducting extensive investigations. On the other hand, while some open-source +models have been proposed by the community, they are typically multilingual and +not specifically tailored for the Italian language. In an effort to democratize +the available and open resources for the Italian language, in this paper we +introduce Camoscio: a language model specifically tuned to follow users' +prompts in Italian. Specifically, we finetuned the smallest variant of LLaMA +(7b) with LoRA on a corpus of instruction prompts translated to Italian via +ChatGPT. Results indicate that the model's zero-shot performance on various +downstream tasks in Italian competes favorably with existing models +specifically finetuned for those tasks. All the artifacts (code, dataset, +model) are released to the community at the following url: +https://github.com/teelinsan/camoscio +" +Self-Alignment with Instruction Backtranslation,Xian Li,http://arxiv.org/pdf/2308.06259v2.pdf,2023-08-11,['cs.cl'],2308.06259v2.pdf," We present a scalable method to build a high quality instruction following +language model by automatically labelling human-written text with corresponding +instructions. Our approach, named instruction backtranslation, starts with a +language model finetuned on a small amount of seed data, and a given web +corpus. The seed model is used to construct training examples by generating +instruction prompts for web documents (self-augmentation), and then selecting +high quality examples from among these candidates (self-curation). This data is +then used to finetune a stronger model. Finetuning LLaMa on two iterations of +our approach yields a model that outperforms all other LLaMa-based models on +the Alpaca leaderboard not relying on distillation data, demonstrating highly +effective self-alignment. +" +Discrete Prompt Compression with Reinforcement Learning,Hoyoun Jung,http://arxiv.org/pdf/2308.08758v1.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.08758v1.pdf," Instruction-tuned Language Models (LMs) are widely used by users to address +various problems with task-specific prompts. Constraints associated with the +context window length and computational costs encourage the development of +compressed prompts. Existing methods rely heavily on training embeddings, which +are designed to accommodate multiple token meanings. This presents challenges +in terms of interpretability, a fixed number of embedding tokens, reusability +across different LMs, and inapplicability when interacting with black-box APIs. +This study proposes prompt compression with reinforcement learning (PCRL), a +novel discrete prompt compression method that addresses these issues. PCRL +employs a computationally efficient policy network that directly edits prompts. +The PCRL training approach can be flexibly applied to various types of LMs, as +well as decoder-only and encoder-decoder architecture, and can be trained +without gradient access to LMs or labeled data. PCRL achieves an average +reduction of 24.6% in token count across various instruction prompts while +preserving performance. Further, we demonstrate that the learned policy can be +transferred to larger LMs, and through various analyses, we aid the +understanding of token importance within prompts. +" +Casteist but Not Racist? Quantifying Disparities in Large Language Model Bias between India and the West,Khyati Khandelwal,http://arxiv.org/pdf/2309.08573v1.pdf,2023-09-15,"['cs.cl', 'cs.cy']",2309.08573v1.pdf," Large Language Models (LLMs), now used daily by millions of users, can encode +societal biases, exposing their users to representational harms. A large body +of scholarship on LLM bias exists but it predominantly adopts a Western-centric +frame and attends comparatively less to bias levels and potential harms in the +Global South. In this paper, we quantify stereotypical bias in popular LLMs +according to an Indian-centric frame and compare bias levels between the Indian +and Western contexts. To do this, we develop a novel dataset which we call +Indian-BhED (Indian Bias Evaluation Dataset), containing stereotypical and +anti-stereotypical examples for caste and religion contexts. We find that the +majority of LLMs tested are strongly biased towards stereotypes in the Indian +context, especially as compared to the Western context. We finally investigate +Instruction Prompting as a simple intervention to mitigate such bias and find +that it significantly reduces both stereotypical and anti-stereotypical biases +in the majority of cases for GPT-3.5. The findings of this work highlight the +need for including more diverse voices when evaluating LLMs. +" +Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support,Siyuan Brandon Loh,http://arxiv.org/pdf/2310.08017v1.pdf,2023-10-12,"['cs.cl', 'i.2']",2310.08017v1.pdf," Large Language Models (LLMs) have demonstrated remarkable performance across +various information-seeking and reasoning tasks. These computational systems +drive state-of-the-art dialogue systems, such as ChatGPT and Bard. They also +carry substantial promise in meeting the growing demands of mental health care, +albeit relatively unexplored. As such, this study sought to examine LLMs' +capability to generate empathetic responses in conversations that emulate those +in a mental health counselling setting. We selected five LLMs: version 3.5 and +version 4 of the Generative Pre-training (GPT), Vicuna FastChat-T5, Pathways +Language Model (PaLM) version 2, and Falcon-7B-Instruct. Based on a simple +instructional prompt, these models responded to utterances derived from the +EmpatheticDialogues (ED) dataset. Using three empathy-related metrics, we +compared their responses to those from traditional response generation dialogue +systems, which were fine-tuned on the ED dataset, along with human-generated +responses. Notably, we discovered that responses from the LLMs were remarkably +more empathetic in most scenarios. We position our findings in light of +catapulting advancements in creating empathetic conversational systems. +" +Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases,Shrimai Prabhumoye,http://arxiv.org/pdf/2112.07868v2.pdf,2021-12-15,"['cs.cl', 'cs.ai']",2112.07868v2.pdf," Detecting social bias in text is challenging due to nuance, subjectivity, and +difficulty in obtaining good quality labeled datasets at scale, especially +given the evolving nature of social biases and society. To address these +challenges, we propose a few-shot instruction-based method for prompting +pre-trained language models (LMs). We select a few class-balanced exemplars +from a small support repository that are closest to the query to be labeled in +the embedding space. We then provide the LM with instruction that consists of +this subset of labeled exemplars, the query text to be classified, a definition +of bias, and prompt it to make a decision. We demonstrate that large LMs used +in a few-shot context can detect different types of fine-grained biases with +similar and sometimes superior accuracy to fine-tuned models. We observe that +the largest 530B parameter model is significantly more effective in detecting +social bias compared to smaller models (achieving at least 13% improvement in +AUC metric compared to other models). It also maintains a high AUC (dropping +less than 2%) when the labeled repository is reduced to as few as $100$ +samples. Large pretrained language models thus make it easier and quicker to +build new bias detectors. +" +"GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models",Archiki Prasad,http://arxiv.org/pdf/2203.07281v2.pdf,2022-03-14,"['cs.cl', 'cs.ai', 'cs.lg']",2203.07281v2.pdf," Providing natural language instructions in prompts is a useful new paradigm +for improving task performance of large language models in a zero-shot setting. +Recent work has aimed to improve such prompts via manual rewriting or +gradient-based tuning. However, manual rewriting is time-consuming and requires +subjective interpretation, while gradient-based tuning can be extremely +computationally demanding for large models and may not be feasible for +API-based models. In this work, we introduce Gradient-free Instructional Prompt +Search (GrIPS), a gradient-free, edit-based search approach for improving task +instructions for large language models. GrIPS takes in instructions designed +for humans and automatically returns an improved, edited prompt, while allowing +for API-based tuning. With InstructGPT models, GrIPS improves the average task +performance by up to 4.30 percentage points on eight classification tasks from +the Natural Instructions dataset (with similar improvements for OPT, BLOOM, and +FLAN-T5). We see improvements for both instruction-only prompts and instruction ++ k-shot examples prompts. Notably, GrIPS outperforms manual rewriting and +purely example-based prompts while controlling for the available compute and +data budget. Further, performance of GrIPS is comparable to select +gradient-based tuning approaches. Qualitatively, we show our edits can simplify +instructions and at times make them incoherent but nonetheless improve +accuracy. Our code is available at: https://github.com/archiki/GrIPS +" +LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging,Andy Rosenbaum,http://arxiv.org/pdf/2209.09900v1.pdf,2022-09-20,"['cs.cl', 'cs.ai', 'cs.lg']",2209.09900v1.pdf," We present LINGUIST, a method for generating annotated data for Intent +Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a +5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a +flexible instruction prompt. In a 10-shot novel intent setting for the SNIPS +dataset, LINGUIST surpasses state-of-the-art approaches (Back-Translation and +Example Extrapolation) by a wide margin, showing absolute improvement for the +target intents of +1.9 points on IC Recall and +2.5 points on ST F1 Score. In +the zero-shot cross-lingual setting of the mATIS++ dataset, LINGUIST +out-performs a strong baseline of Machine Translation with Slot Alignment by ++4.14 points absolute on ST F1 Score across 6 languages, while matching +performance on IC. Finally, we verify our results on an internal large-scale +multilingual dataset for conversational agent IC+ST and show significant +improvements over a baseline which uses Back-Translation, Paraphrasing and Slot +Catalog Resampling. To our knowledge, we are the first to demonstrate +instruction fine-tuning of a large-scale seq2seq model to control the outputs +of multilingual intent- and slot-labeled data generation. +" +InferFix: End-to-End Program Repair with LLMs,Matthew Jin,http://arxiv.org/pdf/2303.07263v1.pdf,2023-03-13,['cs.se'],2303.07263v1.pdf," Software development life cycle is profoundly influenced by bugs: their +introduction, identification, and eventual resolution account for a significant +portion of software cost. This has motivated software engineering researchers +and practitioners to propose different approaches for automating the +identification and repair of software defects. Large language models have been +adapted to the program repair task through few-shot demonstration learning and +instruction prompting, treating this as an infilling task. However, these +models have only focused on learning general bug-fixing patterns for +uncategorized bugs mined from public repositories. In this paper, we propose +InferFix: a transformer-based program repair framework paired with a +state-of-the-art static analyzer to fix critical security and performance bugs. +InferFix combines a Retriever -- transformer encoder model pretrained via +contrastive learning objective, which aims at searching for semantically +equivalent bugs and corresponding fixes; and a Generator -- a large language +model (Codex Cushman) finetuned on supervised bug-fix data with prompts +augmented via bug type annotations and semantically similar fixes retrieved +from an external non-parametric memory. To train and evaluate our approach, we +curated InferredBugs, a novel, metadata-rich dataset of bugs extracted by +executing the Infer static analyzer on the change histories of thousands of +Java and C# repositories. Our evaluation demonstrates that InferFix outperforms +strong LLM baselines, with a top-1 accuracy of 65.6% for generating fixes in C# +and 76.8% in Java. We discuss the deployment of InferFix alongside Infer at +Microsoft which offers an end-to-end solution for detection, classification, +and localization of bugs, as well as fixing and validation of candidate +patches, integrated in the continuous integration pipeline to automate the +software development workflow. +" +Text-based Person Search without Parallel Image-Text Data,Yang Bai,http://arxiv.org/pdf/2305.12964v2.pdf,2023-05-22,['cs.cv'],2305.12964v2.pdf," Text-based person search (TBPS) aims to retrieve the images of the target +person from a large image gallery based on a given natural language +description. Existing methods are dominated by training models with parallel +image-text pairs, which are very costly to collect. In this paper, we make the +first attempt to explore TBPS without parallel image-text data ($\mu$-TBPS), in +which only non-parallel images and texts, or even image-only data, can be +adopted. Towards this end, we propose a two-stage framework, +generation-then-retrieval (GTR), to first generate the corresponding pseudo +text for each image and then perform the retrieval in a supervised manner. In +the generation stage, we propose a fine-grained image captioning strategy to +obtain an enriched description of the person image, which firstly utilizes a +set of instruction prompts to activate the off-the-shelf pretrained +vision-language model to capture and generate fine-grained person attributes, +and then converts the extracted attributes into a textual description via the +finetuned large language model or the hand-crafted template. In the retrieval +stage, considering the noise interference of the generated texts for training +model, we develop a confidence score-based training scheme by enabling more +reliable texts to contribute more during the training. Experimental results on +multiple TBPS benchmarks (i.e., CUHK-PEDES, ICFG-PEDES and RSTPReid) show that +the proposed GTR can achieve a promising performance without relying on +parallel image-text data. +" +EDM3: Event Detection as Multi-task Text Generation,Ujjwala Anantheswaran,http://arxiv.org/pdf/2305.16357v1.pdf,2023-05-25,['cs.cl'],2305.16357v1.pdf," Event detection refers to identifying event occurrences in a text and +comprises of two subtasks; event identification and classification. We present +EDM3, a novel approach for Event Detection that formulates three generative +tasks: identification, classification, and combined detection. We show that +EDM3 helps to learn transferable knowledge that can be leveraged to perform +Event Detection and its subtasks concurrently, mitigating the error propagation +inherent in pipelined approaches. Unlike previous dataset- or domain-specific +approaches, EDM3 utilizes the existing knowledge of language models, allowing +it to be trained over any classification schema. We evaluate EDM3 on multiple +event detection datasets: RAMS, WikiEvents, MAVEN, and MLEE, showing that EDM3 +outperforms 1) single-task performance by 8.4% on average and 2) multi-task +performance without instructional prompts by 2.4% on average. We obtain SOTA +results on RAMS (71.3% vs. 65.1% F-1) and competitive performance on other +datasets. We analyze our approach to demonstrate its efficacy in low-resource +and multi-sentence settings. We also show the effectiveness of this approach on +non-standard event configurations such as multi-word and multi-class event +triggers. Overall, our results show that EDM3 is a promising approach for Event +Detection that has the potential for real-world applications. +" +VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset,Sihan Chen,http://arxiv.org/pdf/2305.18500v2.pdf,2023-05-29,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg', 'eess.as']",2305.18500v2.pdf," Vision and text have been fully explored in contemporary video-text +foundational models, while other modalities such as audio and subtitles in +videos have not received sufficient attention. In this paper, we resort to +establish connections between multi-modality video tracks, including Vision, +Audio, and Subtitle, and Text by exploring an automatically generated +large-scale omni-modality video caption dataset called VAST-27M. Specifically, +we first collect 27 million open-domain video clips and separately train a +vision and an audio captioner to generate vision and audio captions. Then, we +employ an off-the-shelf Large Language Model (LLM) to integrate the generated +captions, together with subtitles and instructional prompts into omni-modality +captions. Based on the proposed VAST-27M dataset, we train an omni-modality +video-text foundational model named VAST, which can perceive and process +vision, audio, and subtitle modalities from video, and better support various +tasks including vision-text, audio-text, and multi-modal video-text tasks +(retrieval, captioning and QA). Extensive experiments have been conducted to +demonstrate the effectiveness of our proposed VAST-27M corpus and VAST +foundation model. VAST achieves 22 new state-of-the-art results on various +cross-modality benchmarks. Code, model and dataset will be released at +https://github.com/TXH-mercury/VAST. +" +Mondrian: Prompt Abstraction Attack Against Large Language Models for Cheaper API Pricing,Wai Man Si,http://arxiv.org/pdf/2308.03558v1.pdf,2023-08-07,"['cs.cr', 'cs.cl']",2308.03558v1.pdf," The Machine Learning as a Service (MLaaS) market is rapidly expanding and +becoming more mature. For example, OpenAI's ChatGPT is an advanced large +language model (LLM) that generates responses for various queries with +associated fees. Although these models can deliver satisfactory performance, +they are far from perfect. Researchers have long studied the vulnerabilities +and limitations of LLMs, such as adversarial attacks and model toxicity. +Inevitably, commercial ML models are also not exempt from such issues, which +can be problematic as MLaaS continues to grow. In this paper, we discover a new +attack strategy against LLM APIs, namely the prompt abstraction attack. +Specifically, we propose Mondrian, a simple and straightforward method that +abstracts sentences, which can lower the cost of using LLM APIs. In this +approach, the adversary first creates a pseudo API (with a lower established +price) to serve as the proxy of the target API (with a higher established +price). Next, the pseudo API leverages Mondrian to modify the user query, +obtain the abstracted response from the target API, and forward it back to the +end user. Our results show that Mondrian successfully reduces user queries' +token length ranging from 13% to 23% across various tasks, including text +classification, generation, and question answering. Meanwhile, these abstracted +queries do not significantly affect the utility of task-specific and general +language models like ChatGPT. Mondrian also reduces instruction prompts' token +length by at least 11% without compromising output quality. As a result, the +prompt abstraction attack enables the adversary to profit without bearing the +cost of API development and deployment. +" +Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving,Sumit Kumar Jha,http://arxiv.org/pdf/2309.16436v1.pdf,2023-09-28,"['cs.ai', 'cs.lo']",2309.16436v1.pdf," Generative large language models (LLMs) with instruct training such as GPT-4 +can follow human-provided instruction prompts and generate human-like responses +to these prompts. Apart from natural language responses, they have also been +found to be effective at generating formal artifacts such as code, plans, and +logical specifications from natural language prompts. Despite their remarkably +improved accuracy, these models are still known to produce factually incorrect +or contextually inappropriate results despite their syntactic coherence - a +phenomenon often referred to as hallucination. This limitation makes it +difficult to use these models to synthesize formal artifacts that are used in +safety-critical applications. Unlike tasks such as text summarization and +question-answering, bugs in code, plan, and other formal artifacts produced by +LLMs can be catastrophic. We posit that we can use the satisfiability modulo +theory (SMT) solvers as deductive reasoning engines to analyze the generated +solutions from the LLMs, produce counterexamples when the solutions are +incorrect, and provide that feedback to the LLMs exploiting the dialog +capability of instruct-trained LLMs. This interaction between inductive LLMs +and deductive SMT solvers can iteratively steer the LLM to generate the correct +response. In our experiments, we use planning over the domain of blocks as our +synthesis task for evaluating our approach. We use GPT-4, GPT3.5 Turbo, +Davinci, Curie, Babbage, and Ada as the LLMs and Z3 as the SMT solver. Our +method allows the user to communicate the planning problem in natural language; +even the formulation of queries to SMT solvers is automatically generated from +natural language. Thus, the proposed technique can enable non-expert users to +describe their problems in natural language, and the combination of LLMs and +SMT solvers can produce provably correct solutions. +" +Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report,Jason Holmes,http://arxiv.org/pdf/2310.03874v1.pdf,2023-10-05,"['physics.med-ph', 'cs.cl']",2310.03874v1.pdf," Purpose: To introduce the concept of using large language models (LLMs) to +re-label structure names in accordance with the American Association of +Physicists in Medicine (AAPM) Task Group (TG)-263 standard, and to establish a +benchmark for future studies to reference. + Methods and Materials: The Generative Pre-trained Transformer (GPT)-4 +application programming interface (API) was implemented as a Digital Imaging +and Communications in Medicine (DICOM) storage server, which upon receiving a +structure set DICOM file, prompts GPT-4 to re-label the structure names of both +target volumes and normal tissues according to the AAPM TG-263. Three disease +sites, prostate, head and neck, and thorax were selected for evaluation. For +each disease site category, 150 patients were randomly selected for manually +tuning the instructions prompt (in batches of 50) and 50 patients were randomly +selected for evaluation. Structure names that were considered were those that +were most likely to be relevant for studies utilizing structure contours for +many patients. + Results: The overall re-labeling accuracy of both target volumes and normal +tissues for prostate, head and neck, and thorax cases was 96.0%, 98.5%, and +96.9% respectively. Re-labeling of target volumes was less accurate on average +except for prostate - 100%, 93.1%, and 91.1% respectively. + Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of +both target volumes and normal tissues as presented in this work, LLMs are +poised to be the preferred method for standardizing structure names in +radiation oncology, especially considering the rapid advancements in LLM +capabilities that are likely to continue. +" +What Makes Pre-trained Language Models Better Zero-shot Learners?,Jinghui Lu,http://arxiv.org/pdf/2209.15206v3.pdf,2022-09-30,"['cs.cl', 'cs.ai']",2209.15206v3.pdf," Current methods for prompt learning in zeroshot scenarios widely rely on a +development set with sufficient human-annotated data to select the +best-performing prompt template a posteriori. This is not ideal because in a +realworld zero-shot scenario of practical relevance, no labelled data is +available. Thus, we propose a simple yet effective method for screening +reasonable prompt templates in zero-shot text classification: Perplexity +Selection (Perplection). We hypothesize that language discrepancy can be used +to measure the efficacy of prompt templates, and thereby develop a +substantiated perplexity-based scheme allowing for forecasting the performance +of prompt templates in advance. Experiments show that our method leads to +improved prediction performance in a realistic zero-shot setting, eliminating +the need for any labelled examples. +" +IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE,Luxi Xing,http://arxiv.org/pdf/2007.00924v1.pdf,2020-07-02,['cs.cl'],2007.00924v1.pdf," This paper introduces our systems for the first two subtasks of SemEval +Task4: Commonsense Validation and Explanation. To clarify the intention for +judgment and inject contrastive information for selection, we propose the input +reconstruction strategy with prompt templates. Specifically, we formalize the +subtasks into the multiple-choice question answering format and construct the +input with the prompt templates, then, the final prediction of question +answering is considered as the result of subtasks. Experimental results show +that our approaches achieve significant performance compared with the baseline +systems. Our approaches secure the third rank on both official test sets of the +first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 +respectively. +" +GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates,Jiayou Zhang,http://arxiv.org/pdf/2112.03002v1.pdf,2021-11-13,"['cs.cl', 'cs.ai']",2112.03002v1.pdf," Biomedical entity normalization unifies the language across biomedical +experiments and studies, and further enables us to obtain a holistic view of +life sciences. Current approaches mainly study the normalization of more +standardized entities such as diseases and drugs, while disregarding the more +ambiguous but crucial entities such as pathways, functions and cell types, +hindering their real-world applications. To achieve biomedical entity +normalization on these under-explored entities, we first introduce an +expert-curated dataset OBO-syn encompassing 70 different types of entities and +2 million curated entity-synonym pairs. To utilize the unique graph structure +in this dataset, we propose GraphPrompt, a prompt-based learning approach that +creates prompt templates according to the graphs. GraphPrompt obtained 41.0% +and 29.9% improvement on zero-shot and few-shot settings respectively, +indicating the effectiveness of these graph-based prompt templates. We envision +that our method GraphPrompt and OBO-syn dataset can be broadly applied to +graph-based NLP tasks, and serve as the basis for analyzing diverse and +accumulating biomedical data. +" +CCPrompt: Counterfactual Contrastive Prompt-Tuning for Many-Class Classification,Yang Li,http://arxiv.org/pdf/2211.05987v1.pdf,2022-11-11,['cs.cl'],2211.05987v1.pdf," With the success of the prompt-tuning paradigm in Natural Language Processing +(NLP), various prompt templates have been proposed to further stimulate +specific knowledge for serving downstream tasks, e.g., machine translation, +text generation, relation extraction, and so on. Existing prompt templates are +mainly shared among all training samples with the information of task +description. However, training samples are quite diverse. The sharing task +description is unable to stimulate the unique task-related information in each +training sample, especially for tasks with the finite-label space. To exploit +the unique task-related information, we imitate the human decision process +which aims to find the contrastive attributes between the objective factual and +their potential counterfactuals. Thus, we propose the \textbf{C}ounterfactual +\textbf{C}ontrastive \textbf{Prompt}-Tuning (CCPrompt) approach for many-class +classification, e.g., relation classification, topic classification, and entity +typing. Compared with simple classification tasks, these tasks have more +complex finite-label spaces and are more rigorous for prompts. First of all, we +prune the finite label space to construct fact-counterfactual pairs. Then, we +exploit the contrastive attributes by projecting training instances onto every +fact-counterfactual pair. We further set up global prototypes corresponding +with all contrastive attributes for selecting valid contrastive attributes as +additional tokens in the prompt template. Finally, a simple Siamese +representation learning is employed to enhance the robustness of the model. We +conduct experiments on relation classification, topic classification, and +entity typing tasks in both fully supervised setting and few-shot setting. The +results indicate that our model outperforms former baselines. +" +Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge,Jiawei Liu,http://arxiv.org/pdf/2305.03287v1.pdf,2023-05-05,"['cs.cl', 'cs.ai']",2305.03287v1.pdf," Fine-tuning pre-trained language models (PLMs), e.g., SciBERT, generally +requires large numbers of annotated data to achieve state-of-the-art +performance on a range of NLP tasks in the scientific domain. However, +obtaining the fine-tune data for scientific NLP task is still challenging and +expensive. Inspired by recent advancement in prompt learning, in this paper, we +propose the Mix Prompt Tuning (MPT), which is a semi-supervised method to +alleviate the dependence on annotated data and improve the performance of +multi-granularity academic function recognition tasks with a small number of +labeled examples. Specifically, the proposed method provides multi-perspective +representations by combining manual prompt templates with automatically learned +continuous prompt templates to help the given academic function recognition +task take full advantage of knowledge in PLMs. Based on these prompt templates +and the fine-tuned PLM, a large number of pseudo labels are assigned to the +unlabeled examples. Finally, we fine-tune the PLM using the pseudo training +set. We evaluate our method on three academic function recognition tasks of +different granularity including the citation function, the abstract sentence +function, and the keyword function, with datasets from computer science domain +and biomedical domain. Extensive experiments demonstrate the effectiveness of +our method and statistically significant improvements against strong baselines. +In particular, it achieves an average increase of 5% in Macro-F1 score compared +with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised +method under low-resource settings. In addition, MPT is a general method that +can be easily applied to other low-resource scientific classification tasks. +" +AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models,Jan Hendrik Metzen,http://arxiv.org/pdf/2309.16414v2.pdf,2023-09-28,"['cs.cv', 'cs.ai', 'cs.lg']",2309.16414v2.pdf," Classifiers built upon vision-language models such as CLIP have shown +remarkable zero-shot performance across a broad range of image classification +tasks. Prior work has studied different ways of automatically creating +descriptor sets for every class based on prompt templates, ranging from +manually engineered templates over templates obtained from a large language +model to templates built from random words and characters. Up until now, +deriving zero-shot classifiers from the respective encoded class descriptors +has remained nearly unchanged, i.e., classify to the class that maximizes +cosine similarity between its averaged encoded class descriptors and the image +encoding. However, weighing all class descriptors equally can be suboptimal +when certain descriptors match visual clues on a given image better than +others. In this work, we propose AutoCLIP, a method for auto-tuning zero-shot +classifiers. AutoCLIP tunes per-image weights to each prompt template at +inference time, based on statistics of class descriptor-image similarities. +AutoCLIP is fully unsupervised, has very low computational overhead, and can be +easily implemented in few lines of code. We show that AutoCLIP outperforms +baselines across a broad range of vision-language models, datasets, and prompt +templates consistently and by up to 3 percent point accuracy. +" +Position-based Prompting for Health Outcome Generation,M. Abaho,http://arxiv.org/pdf/2204.03489v1.pdf,2022-03-30,"['cs.cl', 'cs.lg']",2204.03489v1.pdf," Probing Pre-trained Language Models (PLMs) using prompts has indirectly +implied that language models (LMs) can be treated as knowledge bases. To this +end, this phenomena has been effective especially when these LMs are fine-tuned +towards not just data of a specific domain, but also to the style or linguistic +pattern of the prompts themselves. We observe that, satisfying a particular +linguistic pattern in prompts is an unsustainable constraint that unnecessarily +lengthens the probing task, especially because, they are often manually +designed and the range of possible prompt template patterns can vary depending +on the prompting objective and domain. We therefore explore an idea of using a +position-attention mechanism to capture positional information of each word in +a prompt relative to the mask to be filled, hence avoiding the need to +re-construct prompts when the prompts linguistic pattern changes. Using our +approach, we demonstrate the ability of eliciting answers to rare prompt +templates (in a case study on health outcome generation) such as Postfix and +Mixed patterns whose missing information is respectively at the start and in +multiple random places of the prompt. More so, using various biomedical PLMs, +our approach consistently outperforms a baseline in which the default mask +language model (MLM) representation is used to predict masked tokens. +" +Prompting Large Language Models With the Socratic Method,Edward Y. Chang,http://arxiv.org/pdf/2303.08769v2.pdf,2023-02-17,"['cs.lg', 'i.2.7']",2303.08769v2.pdf," This paper presents a systematic approach to using the Socratic method in +developing prompt templates that effectively interact with large language +models, including GPT-3. Various methods are examined, and those that yield +precise answers and justifications while fostering creativity and imagination +to enhance creative writing are identified. Techniques such as {\em +definition}, {\em elenchus}, {\em dialectic}, {\em maieutics}, {\em +generalization}, and {\em counterfactual reasoning} are discussed for their +application in engineering prompt templates and their connections to inductive, +deductive, and abductive reasoning. Through examples, the effectiveness of +these dialogue and reasoning methods is demonstrated. An interesting +observation is made that when the task's goal and user intent are conveyed to +GPT-3 via ChatGPT before the start of a dialogue, the large language model +seems to connect to the external context expressed in the intent and perform +more effectively. +" +Prompt Learning for News Recommendation,Zizhuo Zhang,http://arxiv.org/pdf/2304.05263v1.pdf,2023-04-11,"['cs.ir', 'cs.ai', 'h.3.3']",2304.05263v1.pdf," Some recent \textit{news recommendation} (NR) methods introduce a Pre-trained +Language Model (PLM) to encode news representation by following the vanilla +pre-train and fine-tune paradigm with carefully-designed +recommendation-specific neural networks and objective functions. Due to the +inconsistent task objective with that of PLM, we argue that their modeling +paradigm has not well exploited the abundant semantic information and +linguistic knowledge embedded in the pre-training process. Recently, the +pre-train, prompt, and predict paradigm, called \textit{prompt learning}, has +achieved many successes in natural language processing domain. In this paper, +we make the first trial of this new paradigm to develop a \textit{Prompt +Learning for News Recommendation} (Prompt4NR) framework, which transforms the +task of predicting whether a user would click a candidate news as a cloze-style +mask-prediction task. Specifically, we design a series of prompt templates, +including discrete, continuous, and hybrid templates, and construct their +corresponding answer spaces to examine the proposed Prompt4NR framework. +Furthermore, we use the prompt ensembling to integrate predictions from +multiple prompt templates. Extensive experiments on the MIND dataset validate +the effectiveness of our Prompt4NR with a set of new benchmark results. +" +Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification,Han Wang,http://arxiv.org/pdf/2204.06305v2.pdf,2022-04-13,"['cs.cl', 'cs.ai', 'cs.lg']",2204.06305v2.pdf," Prompt-based learning (i.e., prompting) is an emerging paradigm for +exploiting knowledge learned by a pretrained language model. In this paper, we +propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method +to automatically select label mappings for few-shot text classification with +prompting. Our method exploits one-to-many label mappings and a +statistics-based algorithm to select label mappings given a prompt template. +Our experiments demonstrate that AMuLaP achieves competitive performance on the +GLUE benchmark without human effort or external resources. +" +CoCoMo: Computational Consciousness Modeling for Generative and Ethical AI,Edward Y. Chang,http://arxiv.org/pdf/2304.02438v2.pdf,2023-03-17,"['cs.oh', 'i.2.7']",2304.02438v2.pdf," The CoCoMo model proposes a computational solution to the challenge of +incorporating ethical and emotional intelligence considerations into AI +systems, with the aim of creating AI agents that combine knowledge with +compassion. To achieve this goal, CoCoMo prioritizes fairness, beneficence, +non-maleficence, empathy, adaptability, transparency, and critical and +exploratory thinking abilities. The model employs consciousness modeling, +reinforcement learning, and prompt template formulation to support these +desired traits. By incorporating ethical and emotional intelligence +considerations, a generative AI model can potentially lead to improved +fairness, reduced toxicity, and increased reliability. +" +PromptNER: Prompt Locating and Typing for Named Entity Recognition,Yongliang Shen,http://arxiv.org/pdf/2305.17104v1.pdf,2023-05-26,['cs.cl'],2305.17104v1.pdf," Prompt learning is a new paradigm for utilizing pre-trained language models +and has achieved great success in many tasks. To adopt prompt learning in the +NER task, two kinds of methods have been explored from a pair of symmetric +perspectives, populating the template by enumerating spans to predict their +entity types or constructing type-specific prompts to locate entities. However, +these methods not only require a multi-round prompting manner with a high time +overhead and computational cost, but also require elaborate prompt templates, +that are difficult to apply in practical scenarios. In this paper, we unify +entity locating and entity typing into prompt learning, and design a dual-slot +multi-prompt template with the position slot and type slot to prompt locating +and typing respectively. Multiple prompts can be input to the model +simultaneously, and then the model extracts all entities by parallel +predictions on the slots. To assign labels for the slots during training, we +design a dynamic template filling mechanism that uses the extended bipartite +graph matching between prompts and the ground-truth entities. We conduct +experiments in various settings, including resource-rich flat and nested NER +datasets and low-resource in-domain and cross-domain datasets. Experimental +results show that the proposed model achieves a significant performance +improvement, especially in the cross-domain few-shot setting, which outperforms +the state-of-the-art model by +7.7% on average. +" +Large Language and Text-to-3D Models for Engineering Design Optimization,Thiago Rios,http://arxiv.org/pdf/2307.01230v1.pdf,2023-07-03,"['cs.cl', 'cs.lg', 'cs.ne']",2307.01230v1.pdf," The current advances in generative AI for learning large neural network +models with the capability to produce essays, images, music and even 3D assets +from text prompts create opportunities for a manifold of disciplines. In the +present paper, we study the potential of deep text-to-3D models in the +engineering domain, with focus on the chances and challenges when integrating +and interacting with 3D assets in computational simulation-based design +optimization. In contrast to traditional design optimization of 3D geometries +that often searches for the optimum designs using numerical representations, +such as B-Spline surface or deformation parameters in vehicle aerodynamic +optimization, natural language challenges the optimization framework by +requiring a different interpretation of variation operators while at the same +time may ease and motivate the human user interaction. Here, we propose and +realize a fully automated evolutionary design optimization framework using +Shap-E, a recently published text-to-3D asset network by OpenAI, in the context +of aerodynamic vehicle optimization. For representing text prompts in the +evolutionary optimization, we evaluate (a) a bag-of-words approach based on +prompt templates and Wordnet samples, and (b) a tokenisation approach based on +prompt templates and the byte pair encoding method from GPT4. Our main findings +from the optimizations indicate that, first, it is important to ensure that the +designs generated from prompts are within the object class of application, i.e. +diverse and novel designs need to be realistic, and, second, that more research +is required to develop methods where the strength of text prompt variations and +the resulting variations of the 3D designs share causal relations to some +degree to improve the optimization. +" +Zero-shot information extraction from radiological reports using ChatGPT,Danqing Hu,http://arxiv.org/pdf/2309.01398v2.pdf,2023-09-04,['cs.cl'],2309.01398v2.pdf," Electronic health records contain an enormous amount of valuable information, +but many are recorded in free text. Information extraction is the strategy to +transform the sequence of characters into structured data, which can be +employed for secondary analysis. However, the traditional information +extraction components, such as named entity recognition and relation +extraction, require annotated data to optimize the model parameters, which has +become one of the major bottlenecks in building information extraction systems. +With the large language models achieving good performances on various +downstream NLP tasks without parameter tuning, it becomes possible to use large +language models for zero-shot information extraction. In this study, we aim to +explore whether the most popular large language model, ChatGPT, can extract +useful information from the radiological reports. We first design the prompt +template for the interested information in the CT reports. Then, we generate +the prompts by combining the prompt template with the CT reports as the inputs +of ChatGPT to obtain the responses. A post-processing module is developed to +transform the responses into structured extraction results. We conducted the +experiments with 847 CT reports collected from Peking University Cancer +Hospital. The experimental results indicate that ChatGPT can achieve +competitive performances for some extraction tasks compared with the baseline +information extraction system, but some limitations need to be further +improved. +" +Can Language Models be Biomedical Knowledge Bases?,Mujeen Sung,http://arxiv.org/pdf/2109.07154v1.pdf,2021-09-15,['cs.cl'],2109.07154v1.pdf," Pre-trained language models (LMs) have become ubiquitous in solving various +natural language processing (NLP) tasks. There has been increasing interest in +what knowledge these LMs contain and how we can extract that knowledge, +treating LMs as knowledge bases (KBs). While there has been much work on +probing LMs in the general domain, there has been little attention to whether +these powerful LMs can be used as domain-specific KBs. To this end, we create +the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge +triples for probing biomedical LMs. We find that biomedical LMs with recently +proposed probing methods can achieve up to 18.51% Acc@5 on retrieving +biomedical knowledge. Although this seems promising given the task difficulty, +our detailed analyses reveal that most predictions are highly correlated with +prompt templates without any subjects, hence producing similar results on each +relation and hindering their capabilities to be used as domain-specific KBs. We +hope that BioLAMA can serve as a challenging benchmark for biomedical factual +probing. +" +HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing,Sonish Sivarajkumar,http://arxiv.org/pdf/2203.05061v1.pdf,2022-03-09,"['cs.cl', 'cs.ai', 'cs.ir']",2203.05061v1.pdf," Deep learning algorithms are dependent on the availability of large-scale +annotated clinical text datasets. The lack of such publicly available datasets +is the biggest bottleneck for the development of clinical Natural Language +Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep +learning models to classify instances from new classes of which no training +data have been seen before. Prompt-based learning is an emerging ZSL technique +where we define task-based templates for NLP tasks. We developed a novel +prompt-based clinical NLP framework called HealthPrompt and applied the +paradigm of prompt-based learning on clinical texts. In this technique, rather +than fine-tuning a Pre-trained Language Model(PLM), the task definitions are +tuned by defining a prompt template. We performed an in-depth analysis of +HealthPrompt on six different PLMs in a no-data setting. Our experiments prove +that prompts effectively capture the context of clinical texts and perform +remarkably well without any training data. +" +RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction,Yew Ken Chia,http://arxiv.org/pdf/2203.09101v1.pdf,2022-03-17,['cs.cl'],2203.09101v1.pdf," Despite the importance of relation extraction in building and representing +knowledge, less research is focused on generalizing to unseen relations types. +We introduce the task setting of Zero-Shot Relation Triplet Extraction +(ZeroRTE) to encourage further research in low-resource relation extraction +methods. Given an input sentence, each extracted triplet consists of the head +entity, relation label, and tail entity where the relation label is not seen at +the training stage. To solve ZeroRTE, we propose to synthesize relation +examples by prompting language models to generate structured texts. Concretely, +we unify language model prompts and structured text approaches to design a +structured prompt template for generating synthetic relation samples when +conditioning on relation label prompts (RelationPrompt). To overcome the +limitation for extracting multiple relation triplets in a sentence, we design a +novel Triplet Search Decoding method. Experiments on FewRel and Wiki-ZSL +datasets show the efficacy of RelationPrompt for the ZeroRTE task and zero-shot +relation classification. Our code and data are available at +github.com/declare-lab/RelationPrompt. +" +CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction,Jiaju Lin,http://arxiv.org/pdf/2205.00498v2.pdf,2022-05-01,['cs.cl'],2205.00498v2.pdf," Implicit event argument extraction (EAE) aims to identify arguments that +could scatter over the document. Most previous work focuses on learning the +direct relations between arguments and the given trigger, while the implicit +relations with long-range dependency are not well studied. Moreover, recent +neural network based approaches rely on a large amount of labeled data for +training, which is unavailable due to the high labelling cost. In this paper, +we propose a Curriculum learning based Prompt tuning (CUP) approach, which +resolves implicit EAE by four learning stages. The stages are defined according +to the relations with the trigger node in a semantic graph, which well captures +the long-range dependency between arguments and the trigger. In addition, we +integrate a prompt-based encoder-decoder model to elicit related knowledge from +pre-trained language models (PLMs) in each stage, where the prompt templates +are adapted with the learning progress to enhance the reasoning for arguments. +Experimental results on two well-known benchmark datasets show the great +advantages of our proposed approach. In particular, we outperform the +state-of-the-art models in both fully-supervised and low-data scenarios. +" +Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation,Sirui Wang,http://arxiv.org/pdf/2209.00455v1.pdf,2022-08-31,"['cs.lg', 'cs.ai']",2209.00455v1.pdf," Demonstration learning aims to guide the prompt prediction via providing +answered demonstrations in the few shot settings. Despite achieving promising +results, existing work only concatenates the answered examples as +demonstrations to the prompt template (including the raw context) without any +additional operation, neglecting the prompt-demonstration dependencies. +Besides, prior research found that randomly replacing the labels of +demonstrations marginally hurts performance, illustrating that the model could +not properly learn the knowledge brought by the demonstrations. Inspired by the +human learning process, in this paper, we introduce Imitation DEMOnstration +Learning (Imitation-Demo) to strengthen demonstration learning via explicitly +imitating human review behaviour, which includes: (1) contrastive learning +mechanism to concentrate on the similar demonstrations. (2) demonstration-label +re-prediction method to consolidate known knowledge. Experiment results show +that our proposed method achieves state-of-the-art performance on 11 out of 14 +classification corpora. Further studies also prove that Imitation-Demo +strengthen the association between prompt and demonstrations, which could +provide the basis for exploring how demonstration learning works. +" +A Few-shot Approach to Resume Information Extraction via Prompts,Chengguang Gan,http://arxiv.org/pdf/2209.09450v2.pdf,2022-09-20,['cs.cl'],2209.09450v2.pdf," Prompt learning's fine-tune performance on text classification tasks has +attracted the NLP community. This paper applies it to resume information +extraction, improving existing methods for this task. We created manual +templates and verbalizers tailored to resume texts and compared the performance +of Masked Language Model (MLM) and Seq2Seq PLMs. Also, we enhanced the +verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt +template design across NLP tasks. We present the Manual Knowledgeable +Verbalizer (MKV), a rule for constructing verbalizers for specific +applications. Our tests show that MKV rules yield more effective, robust +templates and verbalizers than existing methods. Our MKV approach resolved +sample imbalance, surpassing current automatic prompt methods. This study +underscores the value of tailored prompt learning for resume extraction, +stressing the importance of custom-designed templates and verbalizers. +" +Distilling Task-specific Logical Rules from Large Pre-trained Models,Tao Chen,http://arxiv.org/pdf/2210.02768v1.pdf,2022-10-06,['cs.cl'],2210.02768v1.pdf," Logical rules, both transferable and explainable, are widely used as weakly +supervised signals for many downstream tasks such as named entity tagging. To +reduce the human effort of writing rules, previous researchers adopt an +iterative approach to automatically learn logical rules from several seed +rules. However, obtaining more seed rules can only be accomplished by extra +human annotation with heavy costs. Limited by the size and quality of the seed +rules, the model performance of previous systems is bounded. In this paper, we +develop a novel framework STREAM to distill task-specific logical rules from +large pre-trained models. Specifically, we borrow recent prompt-based language +models as the knowledge expert to yield initial seed rules, and based on the +formed high-quality instance pool that acts as an intermediary role, we keep +teaching the expert to fit our task and learning task-specific logical rules. +Experiments on three public named entity tagging benchmarks demonstrate the +effectiveness of our proposed framework. With several predefined prompt +templates, our system has gained significant improvements over previous +state-of-the-art methods. +" +CLIP model is an Efficient Continual Learner,Vishal Thengane,http://arxiv.org/pdf/2210.03114v1.pdf,2022-10-06,['cs.cv'],2210.03114v1.pdf," The continual learning setting aims to learn new tasks over time without +forgetting the previous ones. The literature reports several significant +efforts to tackle this problem with limited or no access to previous task data. +Among such efforts, typical solutions offer sophisticated techniques involving +memory replay, knowledge distillation, model regularization, and dynamic +network expansion. The resulting methods have a retraining cost at each +learning task, dedicated memory requirements, and setting-specific design +choices. In this work, we show that a frozen CLIP (Contrastive Language-Image +Pretraining) model offers astounding continual learning performance without any +fine-tuning (zero-shot evaluation). We evaluate CLIP under a variety of +settings including class-incremental, domain-incremental and task-agnostic +incremental learning on five popular benchmarks (ImageNet-100 & 1K, CORe50, +CIFAR-100, and TinyImageNet). Without any bells and whistles, the CLIP model +outperforms the state-of-the-art continual learning approaches in the majority +of the settings. We show the effect on the CLIP model's performance by varying +text inputs with simple prompt templates. To the best of our knowledge, this is +the first work to report the CLIP zero-shot performance in a continual setting. +We advocate the use of this strong yet embarrassingly simple baseline for +future comparisons in the continual learning tasks. +" +A Unified Framework for Multi-intent Spoken Language Understanding with prompting,Feifan Song,http://arxiv.org/pdf/2210.03337v1.pdf,2022-10-07,"['cs.cl', 'cs.ai']",2210.03337v1.pdf," Multi-intent Spoken Language Understanding has great potential for widespread +implementation. Jointly modeling Intent Detection and Slot Filling in it +provides a channel to exploit the correlation between intents and slots. +However, current approaches are apt to formulate these two sub-tasks +differently, which leads to two issues: 1) It hinders models from effective +extraction of shared features. 2) Pretty complicated structures are involved to +enhance expression ability while causing damage to the interpretability of +frameworks. In this work, we describe a Prompt-based Spoken Language +Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into +the same form by offering a common pre-trained Seq2Seq model. In detail, ID and +SF are completed by concisely filling the utterance into task-specific prompt +templates as input, and sharing output formats of key-value pairs sequence. +Furthermore, variable intents are predicted first, then naturally embedded into +prompts to guide slot-value pairs inference from a semantic perspective. +Finally, we are inspired by prevalent multi-task learning to introduce an +auxiliary sub-task, which helps to learn relationships among provided labels. +Experiment results show that our framework outperforms several state-of-the-art +baselines on two public datasets. +" +UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?,Dennis Aumiller,http://arxiv.org/pdf/2301.01764v2.pdf,2023-01-04,['cs.cl'],2301.01764v2.pdf," Previous state-of-the-art models for lexical simplification consist of +complex pipelines with several components, each of which requires deep +technical knowledge and fine-tuned interaction to achieve its full potential. +As an alternative, we describe a frustratingly simple pipeline based on +prompted GPT-3 responses, beating competing approaches by a wide margin in +settings with few training instances. Our best-performing submission to the +English language track of the TSAR-2022 shared task consists of an ``ensemble'' +of six different prompt templates with varying context levels. As a +late-breaking result, we further detail a language transfer technique that +allows simplification in languages other than English. Applied to the Spanish +and Portuguese subset, we achieve state-of-the-art results with only minor +modification to the original prompts. Aside from detailing the implementation +and setup, we spend the remainder of this work discussing the particularities +of prompting and implications for future work. Code for the experiments is +available online at https://github.com/dennlinger/TSAR-2022-Shared-Task +" +Prompting Large Language Model for Machine Translation: A Case Study,Biao Zhang,http://arxiv.org/pdf/2301.07069v2.pdf,2023-01-17,"['cs.cl', 'cs.lg']",2301.07069v2.pdf," Research on prompting has shown excellent performance with little or even no +supervised training across many tasks. However, prompting for machine +translation is still under-explored in the literature. We fill this gap by +offering a systematic study on prompting strategies for translation, examining +various factors for prompt template and demonstration example selection. We +further explore the use of monolingual data and the feasibility of +cross-lingual, cross-domain, and sentence-to-document transfer learning in +prompting. Extensive experiments with GLM-130B (Zeng et al., 2022) as the +testbed show that 1) the number and the quality of prompt examples matter, +where using suboptimal examples degenerates translation; 2) several features of +prompt examples, such as semantic similarity, show significant Spearman +correlation with their prompting performance; yet, none of the correlations are +strong enough; 3) using pseudo parallel prompt examples constructed from +monolingual data via zero-shot prompting could improve translation; and 4) +improved performance is achievable by transferring knowledge from prompt +examples selected in other settings. We finally provide an analysis on the +model outputs and discuss several problems that prompting still suffers from. +" +Global Constraints with Prompting for Zero-Shot Event Argument Classification,Zizheng Lin,http://arxiv.org/pdf/2302.04459v1.pdf,2023-02-09,['cs.cl'],2302.04459v1.pdf," Determining the role of event arguments is a crucial subtask of event +extraction. Most previous supervised models leverage costly annotations, which +is not practical for open-domain applications. In this work, we propose to use +global constraints with prompting to effectively tackles event argument +classification without any annotation and task-specific training. Specifically, +given an event and its associated passage, the model first creates several new +passages by prefix prompts and cloze prompts, where prefix prompts indicate +event type and trigger span, and cloze prompts connect each candidate role with +the target argument span. Then, a pre-trained language model scores the new +passages, making the initial prediction. Our novel prompt templates can easily +adapt to all events and argument types without manual effort. Next, the model +regularizes the prediction by global constraints exploiting cross-task, +cross-argument, and cross-event relations. Extensive experiments demonstrate +our model's effectiveness: it outperforms the best zero-shot baselines by 12.5% +and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1, +respectively, without given argument spans. We have made our code publicly +available. +" +Large Language Models Are State-of-the-Art Evaluators of Translation Quality,Tom Kocmi,http://arxiv.org/pdf/2302.14520v2.pdf,2023-02-28,['cs.cl'],2302.14520v2.pdf," We describe GEMBA, a GPT-based metric for assessment of translation quality, +which works both with a reference translation and without. In our evaluation, +we focus on zero-shot prompting, comparing four prompt variants in two modes, +based on the availability of the reference. We investigate nine versions of GPT +models, including ChatGPT and GPT-4. We show that our method for translation +quality assessment only works with GPT~3.5 and larger models. Comparing to +results from WMT22's Metrics shared task, our method achieves state-of-the-art +accuracy in both modes when compared to MQM-based human labels. Our results are +valid on the system level for all three WMT22 Metrics shared task language +pairs, namely English into German, English into Russian, and Chinese into +English. This provides a first glimpse into the usefulness of pre-trained, +generative large language models for quality assessment of translations. We +publicly release all our code and prompt templates used for the experiments +described in this work, as well as all corresponding scoring results, to allow +for external validation and reproducibility. +" +The Prompt Artists,Minsuk Chang,http://arxiv.org/pdf/2303.12253v1.pdf,2023-03-22,['cs.hc'],2303.12253v1.pdf," This paper examines the art practices, artwork, and motivations of prolific +users of the latest generation of text-to-image models. Through interviews, +observations, and a user survey, we present a sampling of the artistic styles +and describe the developed community of practice around generative AI. We find +that: 1) the text prompt and the resulting image can be considered collectively +as an art piece prompts as art and 2) prompt templates (prompts with ``slots'' +for others to fill in with their own words) are developed to create generative +art styles. We discover that the value placed by this community on unique +outputs leads to artists seeking specialized vocabulary to produce distinctive +art pieces (e.g., by reading architectural blogs to find phrases to describe +images). We also find that some artists use ""glitches"" in the model that can be +turned into artistic styles of their own right. From these findings, we outline +specific implications for design regarding future prompting and image editing +options. +" +WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation,Jongheon Jeong,http://arxiv.org/pdf/2303.14814v1.pdf,2023-03-26,"['cs.cv', 'cs.ai', 'cs.cl']",2303.14814v1.pdf," Visual anomaly classification and segmentation are vital for automating +industrial quality inspection. The focus of prior research in the field has +been on training custom models for each quality inspection task, which requires +task-specific images and annotation. In this paper we move away from this +regime, addressing zero-shot and few-normal-shot anomaly classification and +segmentation. Recently CLIP, a vision-language model, has shown revolutionary +generality with competitive zero-/few-shot performance in comparison to +full-supervision. But CLIP falls short on anomaly classification and +segmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a +compositional ensemble on state words and prompt templates and (2) efficient +extraction and aggregation of window/patch/image-level features aligned with +text. We also propose its few-normal-shot extension WinCLIP+, which uses +complementary information from normal images. In MVTec-AD (and VisA), without +further tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AUROC in zero-shot +anomaly classification and segmentation while WinCLIP+ does 93.1%/95.2% +(83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins. +" +MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification,Hongyuan Dong,http://arxiv.org/pdf/2306.08892v1.pdf,2023-06-15,['cs.cl'],2306.08892v1.pdf," Prompting methods have shown impressive performance in a variety of text +mining tasks and applications, especially few-shot ones. Despite the promising +prospects, the performance of prompting model largely depends on the design of +prompt template and verbalizer. In this work, we propose MetricPrompt, which +eases verbalizer design difficulty by reformulating few-shot text +classification task into text pair relevance estimation task. MetricPrompt +adopts prompting model as the relevance metric, further bridging the gap +between Pre-trained Language Model's (PLM) pre-training objective and text +classification task, making possible PLM's smooth adaption. Taking a training +sample and a query one simultaneously, MetricPrompt captures cross-sample +relevance information for accurate relevance estimation. We conduct experiments +on three widely used text classification datasets across four few-shot +settings. Results show that MetricPrompt outperforms manual verbalizer and +other automatic verbalizer design methods across all few-shot settings, +achieving new state-of-the-art (SOTA) performance. +" +TrustGPT: A Benchmark for Trustworthy and Responsible Large Language Models,Yue Huang,http://arxiv.org/pdf/2306.11507v1.pdf,2023-06-20,"['cs.cl', 'cs.ai']",2306.11507v1.pdf," Large Language Models (LLMs) such as ChatGPT, have gained significant +attention due to their impressive natural language processing capabilities. It +is crucial to prioritize human-centered principles when utilizing these models. +Safeguarding the ethical and moral compliance of LLMs is of utmost importance. +However, individual ethical issues have not been well studied on the latest +LLMs. Therefore, this study aims to address these gaps by introducing a new +benchmark -- TrustGPT. TrustGPT provides a comprehensive evaluation of LLMs in +three crucial areas: toxicity, bias, and value-alignment. Initially, TrustGPT +examines toxicity in language models by employing toxic prompt templates +derived from social norms. It then quantifies the extent of bias in models by +measuring quantifiable toxicity values across different groups. Lastly, +TrustGPT assesses the value of conversation generation models from both active +value-alignment and passive value-alignment tasks. Through the implementation +of TrustGPT, this research aims to enhance our understanding of the performance +of conversation generation models and promote the development of language +models that are more ethical and socially responsible. +" +DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification,Wei Xiang,http://arxiv.org/pdf/2307.09813v1.pdf,2023-07-19,['cs.cl'],2307.09813v1.pdf," Event Causality Identification (ECI) aims at determining whether there is a +causal relation between two event mentions. Conventional prompt learning +designs a prompt template to first predict an answer word and then maps it to +the final decision. Unlike conventional prompts, we argue that predicting an +answer word may not be a necessary prerequisite for the ECI task. Instead, we +can first make a deterministic assumption on the existence of causal relation +between two events and then evaluate its rationality to either accept or reject +the assumption. The design motivation is to try the most utilization of the +encyclopedia-like knowledge embedded in a pre-trained language model. In light +of such considerations, we propose a deterministic assumption prompt learning +model, called DAPrompt, for the ECI task. In particular, we design a simple +deterministic assumption template concatenating with the input event pair, +which includes two masks as predicted events' tokens. We use the probabilities +of predicted events to evaluate the assumption rationality for the final event +causality decision. Experiments on the EventStoryLine corpus and +Causal-TimeBank corpus validate our design objective in terms of significant +performance improvements over the state-of-the-art algorithms. +" +DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models,Michael Shenoda,http://arxiv.org/pdf/2309.00248v1.pdf,2023-09-01,"['cs.cv', 'cs.ai']",2309.00248v1.pdf," Generating high-quality labeled image datasets is crucial for training +accurate and robust machine learning models in the field of computer vision. +However, the process of manually labeling real images is often time-consuming +and costly. To address these challenges associated with dataset generation, we +introduce ""DiffuGen,"" a simple and adaptable approach that harnesses the power +of stable diffusion models to create labeled image datasets efficiently. By +leveraging stable diffusion models, our approach not only ensures the quality +of generated datasets but also provides a versatile solution for label +generation. In this paper, we present the methodology behind DiffuGen, which +combines the capabilities of diffusion models with two distinct labeling +techniques: unsupervised and supervised. Distinctively, DiffuGen employs prompt +templating for adaptable image generation and textual inversion to enhance +diffusion model capabilities. +" +Mitigating Word Bias in Zero-shot Prompt-based Classifiers,Adian Liusie,http://arxiv.org/pdf/2309.04992v1.pdf,2023-09-10,['cs.cl'],2309.04992v1.pdf," Prompt-based classifiers are an attractive approach for zero-shot +classification. However, the precise choice of the prompt template and label +words can largely influence performance, with semantically equivalent settings +often showing notable performance difference. This discrepancy can be partly +attributed to word biases, where the classifier may be biased towards classes. +To address this problem, it is possible to optimise classification thresholds +on a labelled data set, however, this mitigates some of the advantages of +prompt-based classifiers. This paper instead approaches this problem by +examining the expected marginal probabilities of the classes. Here, +probabilities are reweighted to have a uniform prior over classes, in an +unsupervised fashion. Further, we draw a theoretical connection between the +class priors and the language models' word prior, and offer the ability to set +a threshold in a zero-resource fashion. We show that matching class priors +correlates strongly with the oracle upper bound performance and demonstrate +large consistent performance gains for prompt settings over a range of NLP +tasks. +" +Prompt-Enhanced Self-supervised Representation Learning for Remote Sensing Image Understanding,Mingming Zhang,http://arxiv.org/pdf/2310.00022v1.pdf,2023-09-28,['cs.cv'],2310.00022v1.pdf," Learning representations through self-supervision on a large-scale, unlabeled +dataset has proven to be highly effective for understanding diverse images, +such as those used in remote sensing image analysis. However, remote sensing +images often have complex and densely populated scenes, with multiple land +objects and no clear foreground objects. This intrinsic property can lead to +false positive pairs in contrastive learning, or missing contextual information +in reconstructive learning, which can limit the effectiveness of existing +self-supervised learning methods. To address these problems, we propose a +prompt-enhanced self-supervised representation learning method that uses a +simple yet efficient pre-training pipeline. Our approach involves utilizing +original image patches as a reconstructive prompt template, and designing a +prompt-enhanced generative branch that provides contextual information through +semantic consistency constraints. We collected a dataset of over 1.28 million +remote sensing images that is comparable to the popular ImageNet dataset, but +without specific temporal or geographical constraints. Our experiments show +that our method outperforms fully supervised learning models and +state-of-the-art self-supervised learning methods on various downstream tasks, +including land cover classification, semantic segmentation, object detection, +and instance segmentation. These results demonstrate that our approach learns +impressive remote sensing representations with high generalization and +transferability. +" +LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation,Zixi Zhang,http://arxiv.org/pdf/2310.04535v1.pdf,2023-10-06,"['cs.lg', 'cs.ar']",2310.04535v1.pdf," Test stimuli generation has been a crucial but labor-intensive task in +hardware design verification. In this paper, we revolutionize this process by +harnessing the power of large language models (LLMs) and present a novel +benchmarking framework, LLM4DV. This framework introduces a prompt template for +interactively eliciting test stimuli from the LLM, along with four innovative +prompting improvements to support the pipeline execution and further enhance +its performance. We compare LLM4DV to traditional constrained-random testing +(CRT), using three self-designed design-under-test (DUT) modules. Experiments +demonstrate that LLM4DV excels in efficiently handling straightforward DUT +scenarios, leveraging its ability to employ basic mathematical reasoning and +pre-trained knowledge. While it exhibits reduced efficiency in complex task +settings, it still outperforms CRT in relative terms. The proposed framework +and the DUT modules used in our experiments will be open-sourced upon +publication. +" +Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data,Shiladitya Dutta,http://arxiv.org/pdf/2310.09926v1.pdf,2023-10-15,['cs.ai'],2310.09926v1.pdf," Foundation models are trained on vast amounts of data at scale using +self-supervised learning, enabling adaptation to a wide range of downstream +tasks. At test time, these models exhibit zero-shot capabilities through which +they can classify previously unseen (user-specified) categories. In this paper, +we address the problem of quantifying uncertainty in these zero-shot +predictions. We propose a heuristic approach for uncertainty estimation in +zero-shot settings using conformal prediction with web data. Given a set of +classes at test time, we conduct zero-shot classification with CLIP-style +models using a prompt template, e.g., ""an image of a "", and use the +same template as a search query to source calibration data from the open web. +Given a web-based calibration set, we apply conformal prediction with a novel +conformity score that accounts for potential errors in retrieved web data. We +evaluate the utility of our proposed method in Biomedical foundation models; +our preliminary results show that web-based conformal prediction sets achieve +the target coverage with satisfactory efficiency on a variety of biomedical +datasets. +" +Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels,Honglei Zhuang,http://arxiv.org/pdf/2310.14122v2.pdf,2023-10-21,['cs.ir'],2310.14122v2.pdf," Zero-shot text rankers powered by recent LLMs achieve remarkable ranking +performance by simply prompting. Existing prompts for pointwise LLM rankers +mostly ask the model to choose from binary relevance labels like ""Yes"" and +""No"". However, the lack of intermediate relevance label options may cause the +LLM to provide noisy or biased answers for documents that are partially +relevant to the query. We propose to incorporate fine-grained relevance labels +into the prompt for LLM rankers, enabling them to better differentiate among +documents with different levels of relevance to the query and thus derive a +more accurate ranking. We study two variants of the prompt template, coupled +with different numbers of relevance levels. Our experiments on 8 BEIR data sets +show that adding fine-grained relevance labels significantly improves the +performance of LLM rankers. +" +"Large Language Models can Share Images, Too!",Young-Jun Lee,http://arxiv.org/pdf/2310.14804v1.pdf,2023-10-23,"['cs.cv', 'cs.ai', 'cs.cl']",2310.14804v1.pdf," This paper explores the image-sharing capability of Large Language Models +(LLMs), such as InstructGPT, ChatGPT, and GPT-4, in a zero-shot setting, +without the help of visual foundation models. Inspired by the two-stage process +of image-sharing in human dialogues, we propose a two-stage framework that +allows LLMs to predict potential image-sharing turns and generate related image +descriptions using our effective restriction-based prompt template. With +extensive experiments, we unlock the \textit{image-sharing} capability of LLMs +in zero-shot prompting, with GPT-4 achieving the best performance. +Additionally, we uncover the emergent \textit{image-sharing} ability in +zero-shot prompting, demonstrating the effectiveness of restriction-based +prompts in both stages of our framework. Based on this framework, we augment +the PhotoChat dataset with images generated by Stable Diffusion at predicted +turns, namely PhotoChat++. To our knowledge, this is the first study to assess +the image-sharing ability of LLMs in a zero-shot setting without visual +foundation models. The source code and the dataset will be released after +publication. +" +KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction,Xiang Chen,http://arxiv.org/pdf/2104.07650v7.pdf,2021-04-15,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2104.07650v7.pdf," Recently, prompt-tuning has achieved promising results for specific few-shot +classification tasks. The core idea of prompt-tuning is to insert text pieces +(i.e., templates) into the input and transform a classification task into a +masked language modeling problem. However, for relation extraction, determining +an appropriate prompt template requires domain expertise, and it is cumbersome +and time-consuming to obtain a suitable label word. Furthermore, there exists +abundant semantic and prior knowledge among the relation labels that cannot be +ignored. To this end, we focus on incorporating knowledge among relation labels +into prompt-tuning for relation extraction and propose a Knowledge-aware +Prompt-tuning approach with synergistic optimization (KnowPrompt). +Specifically, we inject latent knowledge contained in relation labels into +prompt construction with learnable virtual type words and answer words. Then, +we synergistically optimize their representation with structured constraints. +Extensive experimental results on five datasets with standard and low-resource +settings demonstrate the effectiveness of our approach. Our code and datasets +are available in https://github.com/zjunlp/KnowPrompt for reproducibility. +" +Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation,Jiaying Gong,http://arxiv.org/pdf/2112.04539v2.pdf,2021-12-08,['cs.cl'],2112.04539v2.pdf," In relation triplet extraction (RTE), recognizing unseen (new) relations for +which there are no training instances is a challenging task. Efforts have been +made to recognize unseen relations based on question-answering models or +relation descriptions. However, these approaches miss the semantic information +about connections between seen and unseen relations. In this paper, We propose +a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize +unseen relations under the zero-shot setting. We present a new word-level +analogy-based sentence translation rule and generate augmented instances with +unseen relations from instances with seen relations using that new rule. We +design prompts with weighted virtual label construction based on an external +knowledge graph to integrate semantic knowledge information learned from seen +relations. Instead of using the actual label sets in the prompt template, we +construct weighted virtual label words. We learn the representations of both +seen and unseen relations with augmented instances and prompts. We then +calculate the distance between the generated representations using prototypical +networks to predict unseen relations. Extensive experiments conducted on three +public datasets FewRel, Wiki-ZSL, and NYT, show that ZS-SKA outperforms +state-of-the-art methods under the zero-shot scenarios. Our experimental +results also demonstrate the effectiveness and robustness of ZS-SKA. +" +DynaMaR: Dynamic Prompt with Mask Token Representation,Xiaodi Sun,http://arxiv.org/pdf/2206.02982v1.pdf,2022-06-07,"['cs.cl', 'cs.lg']",2206.02982v1.pdf," Recent research has shown that large language models pretrained using +unsupervised approaches can achieve significant performance improvement on many +downstream tasks. Typically when adapting these language models to downstream +tasks, like a classification or regression task, we employ a fine-tuning +paradigm in which the sentence representation from the language model is input +to a task-specific head; the model is then fine-tuned end-to-end. However, with +the emergence of models like GPT-3, prompt-based fine-tuning has been proven to +be a successful approach for few-shot tasks. Inspired by this work, we study +discrete prompt technologies in practice. There are two issues that arise with +the standard prompt approach. First, it can overfit on the prompt template. +Second, it requires manual effort to formulate the downstream task as a +language model problem. In this paper, we propose an improvement to +prompt-based fine-tuning that addresses these two issues. We refer to our +approach as DynaMaR -- Dynamic Prompt with Mask Token Representation. Results +show that DynaMaR can achieve an average improvement of 10% in few-shot +settings and improvement of 3.7% in data-rich settings over the standard +fine-tuning approach on four e-commerce applications. +" +Rethinking the Event Coding Pipeline with Prompt Entailment,Clément Lefebvre,http://arxiv.org/pdf/2210.05257v2.pdf,2022-10-11,"['cs.cl', 'cs.hc', 'cs.lg']",2210.05257v2.pdf," For monitoring crises, political events are extracted from the news. The +large amount of unstructured full-text event descriptions makes a case-by-case +analysis unmanageable, particularly for low-resource humanitarian aid +organizations. This creates a demand to classify events into event types, a +task referred to as event coding. Typically, domain experts craft an event type +ontology, annotators label a large dataset and technical experts develop a +supervised coding system. In this work, we propose PR-ENT, a new event coding +approach that is more flexible and resource-efficient, while maintaining +competitive accuracy: first, we extend an event description such as ""Military +injured two civilians'' by a template, e.g. ""People were [Z]"" and prompt a +pre-trained (cloze) language model to fill the slot Z. Second, we select answer +candidates Z* = {""injured'', ""hurt""...} by treating the event description as +premise and the filled templates as hypothesis in a textual entailment task. +This allows domain experts to draft the codebook directly as labeled prompts +and interpretable answer candidates. This human-in-the-loop process is guided +by our interactive codebook design tool. We evaluate PR-ENT in several +robustness checks: perturbing the event description and prompt template, +restricting the vocabulary and removing contextual information. +" +Visual Prompting for Adversarial Robustness,Aochuan Chen,http://arxiv.org/pdf/2210.06284v4.pdf,2022-10-12,"['cs.cv', 'cs.cr', 'cs.lg']",2210.06284v4.pdf," In this work, we leverage visual prompting (VP) to improve adversarial +robustness of a fixed, pre-trained model at testing time. Compared to +conventional adversarial defenses, VP allows us to design universal (i.e., +data-agnostic) input prompting templates, which have plug-and-play capabilities +at testing time to achieve desired model performance without introducing much +computation overhead. Although VP has been successfully applied to improving +model generalization, it remains elusive whether and how it can be used to +defend against adversarial attacks. We investigate this problem and show that +the vanilla VP approach is not effective in adversarial defense since a +universal input prompt lacks the capacity for robust learning against +sample-specific adversarial perturbations. To circumvent it, we propose a new +VP method, termed Class-wise Adversarial Visual Prompting (C-AVP), to generate +class-wise visual prompts so as to not only leverage the strengths of ensemble +prompts but also optimize their interrelations to improve model robustness. Our +experiments show that C-AVP outperforms the conventional VP method, with 2.1X +standard accuracy gain and 2X robust accuracy gain. Compared to classical +test-time defenses, C-AVP also yields a 42X inference time speedup. +" +Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing,Yibo Wang,http://arxiv.org/pdf/2211.02483v1.pdf,2022-11-04,['cs.cl'],2211.02483v1.pdf," The explosion of e-commerce has caused the need for processing and analysis +of product titles, like entity typing in product titles. However, the rapid +activity in e-commerce has led to the rapid emergence of new entities, which is +difficult to be solved by general entity typing. Besides, product titles in +e-commerce have very different language styles from text data in general +domain. In order to handle new entities in product titles and address the +special language styles problem of product titles in e-commerce domain, we +propose our textual entailment model with continuous prompt tuning based +hypotheses and fusion embeddings for e-commerce entity typing. First, we +reformulate the entity typing task into a textual entailment problem to handle +new entities that are not present during training. Second, we design a model to +automatically generate textual entailment hypotheses using a continuous prompt +tuning method, which can generate better textual entailment hypotheses without +manual design. Third, we utilize the fusion embeddings of BERT embedding and +CharacterBERT embedding with a two-layer MLP classifier to solve the problem +that the language styles of product titles in e-commerce are different from +that of general domain. To analyze the effect of each contribution, we compare +the performance of entity typing and textual entailment model, and conduct +ablation studies on continuous prompt tuning and fusion embeddings. We also +evaluate the impact of different prompt template initialization for the +continuous prompt tuning. We show our proposed model improves the average F1 +score by around 2% compared to the baseline BERT entity typing model. +" +Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt,Zhichao Yang,http://arxiv.org/pdf/2211.13813v2.pdf,2022-11-24,"['cs.cl', 'cs.ai']",2211.13813v2.pdf," Automatic International Classification of Diseases (ICD) coding aims to +assign multiple ICD codes to a medical note with an average of 3,000+ tokens. +This task is challenging due to the high-dimensional space of multi-label +assignment (155,000+ ICD code candidates) and the long-tail challenge - Many +ICD codes are infrequently assigned yet infrequent ICD codes are important +clinically. This study addresses the long-tail challenge by transforming this +multi-label classification task into an autoregressive generation task. +Specifically, we first introduce a novel pretraining objective to generate free +text diagnoses and procedure using the SOAP structure, the medical logic +physicians use for note documentation. Second, instead of directly predicting +the high dimensional space of ICD codes, our model generates the lower +dimension of text descriptions, which then infer ICD codes. Third, we designed +a novel prompt template for multi-label classification. We evaluate our +Generation with Prompt model with the benchmark of all code assignment +(MIMIC-III-full) and few shot ICD code assignment evaluation benchmark +(MIMIC-III-few). Experiments on MIMIC-III-few show that our model performs with +a marco F1 30.2, which substantially outperforms the previous MIMIC-III-full +SOTA model (marco F1 4.3) and the model specifically designed for few/zero shot +setting (marco F1 18.7). Finally, we design a novel ensemble learner, a cross +attention reranker with prompts, to integrate previous SOTA and our best +few-shot coding predictions. Experiments on MIMIC-III-full show that our +ensemble learner substantially improves both macro and micro F1, from 10.4 to +14.6 and from 58.2 to 59.1, respectively. +" +LabelPrompt: Effective Prompt-based Learning for Relation Classification,Wenjie Zhang,http://arxiv.org/pdf/2302.08068v2.pdf,2023-02-16,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2302.08068v2.pdf," Recently, prompt-based learning has gained popularity across many natural +language processing (NLP) tasks by reformulating them into a cloze-style format +to better align pre-trained language models (PLMs) with downstream tasks. +However, applying this approach to relation classification poses unique +challenges. Specifically, associating natural language words that fill the +masked token with semantic relation labels (\textit{e.g.} +\textit{``org:founded\_by}'') is difficult. To address this challenge, this +paper presents a novel prompt-based learning method, namely LabelPrompt, for +the relation classification task. Motivated by the intuition to ``GIVE MODEL +CHOICES!'', we first define additional tokens to represent relation labels, +which regard these tokens as the verbaliser with semantic initialisation and +explicitly construct them with a prompt template method. Then, to mitigate +inconsistency between predicted relations and given entities, we implement an +entity-aware module with contrastive learning. Last, we conduct an attention +query strategy within the self-attention layer to differentiates prompt tokens +and sequence tokens. Together, these strategies enhance the adaptability of +prompt-based learning, especially when only small labelled datasets is +available. Comprehensive experiments on benchmark datasets demonstrate the +superiority of our method, particularly in the few-shot scenario. +" +Adapting Prompt for Few-shot Table-to-Text Generation,Zhixin Guo,http://arxiv.org/pdf/2302.12468v2.pdf,2023-02-24,['cs.cl'],2302.12468v2.pdf," Pretrained language models (PLMs) have made remarkable progress in +table-to-text generation tasks. However, the lack of domain-specific knowledge +makes it challenging to bridge the topological gap between tabular data and +text, especially in real-world applications with limited resources. To mitigate +the limitation of insufficient labeled data, we propose a novel framework: +Adapt-Prompt-to-Generate (AdaPTGen). The core insight of AdaPTGen is to adapt +prompt templates of domain-specific knowledge into the model, which brings at +least three benefits: (1) it injects representation of normal table-related +descriptions to bridge the topological gap between tabular data and texts; (2) +it enables us to use large amounts of unlabeled domain-specific knowledge +fully, which can alleviate the PLMs' inherent shortcomings of lacking domain +knowledge; (3) it allows us to design various tasks to explore the +domain-specific knowledge. Extensive experiments and analyses are conducted on +three open-domain few-shot natural language generation (NLG) data sets: Humans, +Songs, and Books. Compared to previous state-of-the-art approaches, our model +achieves superior performance in terms of both fluency and accuracy. +" +Model-tuning Via Prompts Makes NLP Models Adversarially Robust,Mrigank Raman,http://arxiv.org/pdf/2303.07320v1.pdf,2023-03-13,"['cs.cl', 'cs.lg']",2303.07320v1.pdf," In recent years, NLP practitioners have converged on the following practice: +(i) import an off-the-shelf pretrained (masked) language model; (ii) append a +multilayer perceptron atop the CLS token's hidden representation (with randomly +initialized weights); and (iii) fine-tune the entire model on a downstream task +(MLP). This procedure has produced massive gains on standard NLP benchmarks, +but these models remain brittle, even to mild adversarial perturbations, such +as word-level synonym substitutions. In this work, we demonstrate surprising +gains in adversarial robustness enjoyed by Model-tuning Via Prompts (MVP), an +alternative method of adapting to downstream tasks. Rather than modifying the +model (by appending an MLP head), MVP instead modifies the input (by appending +a prompt template). Across three classification datasets, MVP improves +performance against adversarial word-level synonym substitutions by an average +of 8% over standard methods and even outperforms adversarial training-based +state-of-art defenses by 3.5%. By combining MVP with adversarial training, we +achieve further improvements in robust accuracy while maintaining clean +accuracy. Finally, we conduct ablations to investigate the mechanism underlying +these gains. Notably, we find that the main causes of vulnerability of MLP can +be attributed to the misalignment between pre-training and fine-tuning tasks, +and the randomly initialized MLP parameters. Code is available at +https://github.com/acmi-lab/mvp +" +"PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models",Aditi Mishra,http://arxiv.org/pdf/2304.01964v2.pdf,2023-04-04,['cs.hc'],2304.01964v2.pdf," Large Language Models (LLMs) have gained widespread popularity due to their +ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple +natural language prompt. Part of the appeal for LLMs is their approachability +to the general public, including individuals with no prior technical experience +in NLP techniques. However, natural language prompts can vary significantly in +terms of their linguistic structure, context, and other semantics. Modifying +one or more of these aspects can result in significant differences in task +performance. Non-expert users may find it challenging to identify the changes +needed to improve a prompt, especially when they lack domain-specific knowledge +and lack appropriate feedback. To address this challenge, we present PromptAid, +a visual analytics system designed to interactively create, refine, and test +prompts through exploration, perturbation, testing, and iteration. PromptAid +uses multiple, coordinated visualizations which allow users to improve prompts +by using the three strategies: keyword perturbations, paraphrasing +perturbations, and obtaining the best set of in-context few-shot examples. +PromptAid was designed through an iterative prototyping process involving NLP +experts and was evaluated through quantitative and qualitative assessments for +LLMs. Our findings indicate that PromptAid helps users to iterate over prompt +template alterations with less cognitive overhead, generate diverse prompts +with help of recommendations, and analyze the performance of the generated +prompts while surpassing existing state-of-the-art prompting interfaces in +performance. +" +FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training,Yunpeng Han,http://arxiv.org/pdf/2304.05051v1.pdf,2023-04-11,"['cs.cv', 'cs.cl']",2304.05051v1.pdf," Fashion vision-language pre-training models have shown efficacy for a wide +range of downstream tasks. However, general vision-language pre-training models +pay less attention to fine-grained domain features, while these features are +important in distinguishing the specific domain tasks from general tasks. We +propose a method for fine-grained fashion vision-language pre-training based on +fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained +multi-modalities fashion attributes and characteristics. Firstly, we propose +the fashion symbols, a novel abstract fashion concept layer, to represent +different fashion items and to generalize various kinds of fine-grained fashion +features, making modelling fine-grained attributes more effective. Secondly, +the attributes prompt method is proposed to make the model learn specific +attributes of fashion items explicitly. We design proper prompt templates +according to the format of fashion data. Comprehensive experiments are +conducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, and +FashionSAP gets SOTA performances for four popular fashion tasks. The ablation +study also shows the proposed abstract fashion symbols, and the attribute +prompt method enables the model to acquire fine-grained semantics in the +fashion domain effectively. The obvious performance gains from FashionSAP +provide a new baseline for future fashion task research. +" +"A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair",Jialun Cao,http://arxiv.org/pdf/2304.08191v1.pdf,2023-04-17,['cs.se'],2304.08191v1.pdf," ChatGPT has revolutionized many research and industrial fields. ChatGPT has +shown great potential in software engineering to boost various traditional +tasks such as program repair, code understanding, and code generation. However, +whether automatic program repair (APR) applies to deep learning (DL) programs +is still unknown. DL programs, whose decision logic is not explicitly encoded +in the source code, have posed unique challenges to APR. While to repair DL +programs, an APR approach needs to not only parse the source code syntactically +but also needs to understand the code intention. With the best prior work, the +performance of fault localization is still far less than satisfactory (only +about 30\%). Therefore, in this paper, we explore ChatGPT's capability for DL +program repair by asking three research questions. (1) Can ChatGPT debug DL +programs effectively? (2) How can ChatGPT's repair performance be improved by +prompting? (3) In which way can dialogue help facilitate the repair? On top of +that, we categorize the common aspects useful for prompt design for DL program +repair. Also, we propose various prompt templates to facilitate the performance +and summarize the advantages and disadvantages of ChatGPT's abilities such as +detecting bad code smell, code refactoring, and detecting API +misuse/deprecation. +" +Prompt-Learning for Cross-Lingual Relation Extraction,Chiaming Hsu,http://arxiv.org/pdf/2304.10354v1.pdf,2023-04-20,['cs.cl'],2304.10354v1.pdf," Relation Extraction (RE) is a crucial task in Information Extraction, which +entails predicting relationships between entities within a given sentence. +However, extending pre-trained RE models to other languages is challenging, +particularly in real-world scenarios where Cross-Lingual Relation Extraction +(XRE) is required. Despite recent advancements in Prompt-Learning, which +involves transferring knowledge from Multilingual Pre-trained Language Models +(PLMs) to diverse downstream tasks, there is limited research on the effective +use of multilingual PLMs with prompts to improve XRE. In this paper, we present +a novel XRE algorithm based on Prompt-Tuning, referred to as Prompt-XRE. To +evaluate its effectiveness, we design and implement several prompt templates, +including hard, soft, and hybrid prompts, and empirically test their +performance on competitive multilingual PLMs, specifically mBART. Our extensive +experiments, conducted on the low-resource ACE05 benchmark across multiple +languages, demonstrate that our Prompt-XRE algorithm significantly outperforms +both vanilla multilingual PLMs and other existing models, achieving +state-of-the-art performance in XRE. To further show the generalization of our +Prompt-XRE on larger data scales, we construct and release a new XRE dataset- +WMT17-EnZh XRE, containing 0.9M English-Chinese pairs extracted from WMT 2017 +parallel corpus. Experiments on WMT17-EnZh XRE also show the effectiveness of +our Prompt-XRE against other competitive baselines. The code and newly +constructed dataset are freely available at +\url{https://github.com/HSU-CHIA-MING/Prompt-XRE}. +" +CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers,Avishek Lahiri,http://arxiv.org/pdf/2304.12730v2.pdf,2023-04-25,['cs.cl'],2304.12730v2.pdf," Citations in scientific papers not only help us trace the intellectual +lineage but also are a useful indicator of the scientific significance of the +work. Citation intents prove beneficial as they specify the role of the +citation in a given context. In this paper, we present CitePrompt, a framework +which uses the hitherto unexplored approach of prompt-based learning for +citation intent classification. We argue that with the proper choice of the +pretrained language model, the prompt template, and the prompt verbalizer, we +can not only get results that are better than or comparable to those obtained +with the state-of-the-art methods but also do it with much less exterior +information about the scientific document. We report state-of-the-art results +on the ACL-ARC dataset, and also show significant improvement on the SciCite +dataset over all baseline models except one. As suitably large labelled +datasets for citation intent classification can be quite hard to find, in a +first, we propose the conversion of this task to the few-shot and zero-shot +settings. For the ACL-ARC dataset, we report a 53.86% F1 score for the +zero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and +10-shot settings, respectively. +" +Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner,Zhengxiang Shi,http://arxiv.org/pdf/2305.01711v4.pdf,2023-05-02,['cs.cl'],2305.01711v4.pdf," Language models (LMs) trained on vast quantities of unlabelled data have +greatly advanced the field of natural language processing (NLP). In this study, +we re-visit the widely accepted notion in NLP that continued pre-training LMs +on task-related texts improves the performance of fine-tuning (FT) in +downstream tasks. Through experiments on eight single-sentence tasks and eight +sentence-pair tasks in both semi-supervised and fully-supervised settings, we +find that conventional continued pre-training does not consistently provide +benefits and can even be detrimental for sentence-pair tasks or when +prompt-based FT is used. To tackle these issues, we propose Prompt-based +Continued Pre-training (PCP), which combines the idea of instruction tuning +with conventional continued pre-training. Our approach aims to improve the +performance of prompt-based FT by presenting both task-related texts and prompt +templates to LMs through unsupervised pre-training objectives before +fine-tuning for the target task. Our empirical evaluations on 21 benchmarks +demonstrate that the PCP consistently improves the performance of +state-of-the-art prompt-based FT approaches (up to 20.1% absolute) in both +semi-supervised and fully-supervised settings, even with only hundreds of +unlabelled examples. Additionally, prompt-based FT with the PCP outperforms +state-of-the-art semi-supervised approaches with greater simplicity, +eliminating the need for an iterative process and extra data augmentation. Our +further analysis explores the performance lower bound of the PCP and reveals +that the advantages of PCP persist across different sizes of models and +datasets. +" +Large Language Models are Zero-Shot Rankers for Recommender Systems,Yupeng Hou,http://arxiv.org/pdf/2305.08845v1.pdf,2023-05-15,"['cs.ir', 'cs.cl']",2305.08845v1.pdf," Recently, large language models (LLMs) (e.g. GPT-4) have demonstrated +impressive general-purpose task-solving abilities, including the potential to +approach recommendation tasks. Along this line of research, this work aims to +investigate the capacity of LLMs that act as the ranking model for recommender +systems. To conduct our empirical study, we first formalize the recommendation +problem as a conditional ranking task, considering sequential interaction +histories as conditions and the items retrieved by the candidate generation +model as candidates. We adopt a specific prompting approach to solving the +ranking task by LLMs: we carefully design the prompting template by including +the sequential interaction history, the candidate items, and the ranking +instruction. We conduct extensive experiments on two widely-used datasets for +recommender systems and derive several key findings for the use of LLMs in +recommender systems. We show that LLMs have promising zero-shot ranking +abilities, even competitive to or better than conventional recommendation +models on candidates retrieved by multiple candidate generators. We also +demonstrate that LLMs struggle to perceive the order of historical interactions +and can be affected by biases like position bias, while these issues can be +alleviated via specially designed prompting and bootstrapping strategies. The +code to reproduce this work is available at +https://github.com/RUCAIBox/LLMRank. +" +TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition,Wei Xiang,http://arxiv.org/pdf/2305.10866v1.pdf,2023-05-18,['cs.cl'],2305.10866v1.pdf," Implicit Discourse Relation Recognition (IDRR) aims at classifying the +relation sense between two arguments without an explicit connective. Recently, +the ConnPrompt~\cite{Wei.X:et.al:2022:COLING} has leveraged the powerful prompt +learning for IDRR based on the fusion of multi-prompt decisions from three +different yet much similar connective prediction templates. Instead of +multi-prompt ensembling, we propose to design auxiliary tasks with enlightened +prompt learning for the IDRR task. Although an auxiliary task is not used to +directly output final prediction, we argue that during the joint training some +of its learned features can be useful to boost the main task. In light of such +motivations, we propose a task enlightenment prompt learning model, called +TEPrompt, to fuse learned features from three related tasks for IDRR. In +particular, the TEPrompt contains three tasks, viz., Discourse Relation +Recognition (DRR), Sense Semantics Classification (SSC) and Annotated +Connective Prediction (ACP), each with a unique prompt template and an answer +space. In the training phase, we jointly train three prompt learning tasks with +shared argument representation. In the testing phase, we only take the DRR +output with fused features as the final IDRR decision. Experiments with the +same conditions have shown that the proposed TEPrompt outperforms the +ConnPrompt. This can be attributed to the promoted decision features and +language models benefited from joint-training of auxiliary tasks. +" +Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge,Jinyuan Li,http://arxiv.org/pdf/2305.12212v2.pdf,2023-05-20,['cs.cl'],2305.12212v2.pdf," Multimodal Named Entity Recognition (MNER) on social media aims to enhance +textual entity prediction by incorporating image-based clues. Existing studies +mainly focus on maximizing the utilization of pertinent image information or +incorporating external knowledge from explicit knowledge bases. However, these +methods either neglect the necessity of providing the model with external +knowledge, or encounter issues of high redundancy in the retrieved knowledge. +In this paper, we present PGIM -- a two-stage framework that aims to leverage +ChatGPT as an implicit knowledge base and enable it to heuristically generate +auxiliary knowledge for more efficient entity prediction. Specifically, PGIM +contains a Multimodal Similar Example Awareness module that selects suitable +examples from a small number of predefined artificial samples. These examples +are then integrated into a formatted prompt template tailored to the MNER and +guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired +knowledge is integrated with the original text and fed into a downstream model +for further processing. Extensive experiments show that PGIM outperforms +state-of-the-art methods on two classic MNER datasets and exhibits a stronger +robustness and generalization capability. +" +"Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool",Jingwei Ni,http://arxiv.org/pdf/2306.15518v1.pdf,2023-06-27,['cs.cl'],2306.15518v1.pdf," This paper introduces a novel approach to enhance Large Language Models +(LLMs) with expert knowledge to automate the analysis of corporate +sustainability reports by benchmarking them against the Task Force for +Climate-Related Financial Disclosures (TCFD) recommendations. Corporate +sustainability reports are crucial in assessing organizations' environmental +and social risks and impacts. However, analyzing these reports' vast amounts of +information makes human analysis often too costly. As a result, only a few +entities worldwide have the resources to analyze these reports, which could +lead to a lack of transparency. While AI-powered tools can automatically +analyze the data, they are prone to inaccuracies as they lack domain-specific +expertise. This paper introduces a novel approach to enhance LLMs with expert +knowledge to automate the analysis of corporate sustainability reports. We +christen our tool CHATREPORT, and apply it in a first use case to assess +corporate climate risk disclosures following the TCFD recommendations. +CHATREPORT results from collaborating with experts in climate science, finance, +economic policy, and computer science, demonstrating how domain experts can be +involved in developing AI tools. We make our prompt templates, generated data, +and scores available to the public to encourage transparency. +" +TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation,Paul Grimal,http://arxiv.org/pdf/2307.05134v1.pdf,2023-07-11,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2307.05134v1.pdf," The progress in the generation of synthetic images has made it crucial to +assess their quality. While several metrics have been proposed to assess the +rendering of images, it is crucial for Text-to-Image (T2I) models, which +generate images based on a prompt, to consider additional aspects such as to +which extent the generated image matches the important content of the prompt. +Moreover, although the generated images usually result from a random starting +point, the influence of this one is generally not considered. In this article, +we propose a new metric based on prompt templates to study the alignment +between the content specified in the prompt and the corresponding generated +images. It allows us to better characterize the alignment in terms of the type +of the specified objects, their number, and their color. We conducted a study +on several recent T2I models about various aspects. An additional interesting +result we obtained with our approach is that image quality can vary drastically +depending on the latent noise used as a seed for the images. We also quantify +the influence of the number of concepts in the prompt, their order as well as +their (color) attributes. Finally, our method allows us to identify some latent +seeds that produce better images than others, opening novel directions of +research on this understudied topic. +" +LLM-FuncMapper: Function Identification for Interpreting Complex Clauses in Building Codes via LLM,Zhe Zheng,http://arxiv.org/pdf/2308.08728v1.pdf,2023-08-17,"['cs.ai', 'cs.cl']",2308.08728v1.pdf," As a vital stage of automated rule checking (ARC), rule interpretation of +regulatory texts requires considerable effort. However, interpreting regulatory +clauses with implicit properties or complex computational logic is still +challenging due to the lack of domain knowledge and limited expressibility of +conventional logic representations. Thus, LLM-FuncMapper, an approach to +identifying predefined functions needed to interpret various regulatory clauses +based on the large language model (LLM), is proposed. First, by systematically +analysis of building codes, a series of atomic functions are defined to capture +shared computational logics of implicit properties and complex constraints, +creating a database of common blocks for interpreting regulatory clauses. Then, +a prompt template with the chain of thought is developed and further enhanced +with a classification-based tuning strategy, to enable common LLMs for +effective function identification. Finally, the proposed approach is validated +with statistical analysis, experiments, and proof of concept. Statistical +analysis reveals a long-tail distribution and high expressibility of the +developed function database, with which almost 100% of computer-processible +clauses can be interpreted and represented as computer-executable codes. +Experiments show that LLM-FuncMapper achieve promising results in identifying +relevant predefined functions for rule interpretation. Further proof of concept +in automated rule interpretation also demonstrates the possibility of +LLM-FuncMapper in interpreting complex regulatory clauses. To the best of our +knowledge, this study is the first attempt to introduce LLM for understanding +and interpreting complex regulatory clauses, which may shed light on further +adoption of LLM in the construction domain. +" +Prompt-Based Length Controlled Generation with Reinforcement Learning,Renlong Jie,http://arxiv.org/pdf/2308.12030v2.pdf,2023-08-23,"['cs.cl', 'cs.ai', 'cs.lg']",2308.12030v2.pdf," Large language models (LLMs) like ChatGPT and GPT-4 have attracted great +attention given their surprising performance on a wide range of NLP tasks. +Length controlled generation of LLMs emerges as an important topic, which +enables users to fully leverage the capability of LLMs in more real-world +scenarios like generating a proper answer or essay of a desired length. In +addition, the autoregressive generation in LLMs is extremely time-consuming, +while the ability of controlling this generated length can reduce the inference +cost by limiting the length. Therefore, we propose a prompt-based length +control method to achieve high-accuracy length controlled generation. In +particular, we adopt reinforcement learning with the reward signal given by +either trainable or rule-based reward models, which further enhances the +length-control ability of LLMs by rewarding outputs that follows pre-defined +control instruction. To enable rule-based inference, we also introduce standard +prompt extractor to collect the standard control information from users' input. +Experiments show that our method significantly improves the accuracy of +prompt-based length control for summarization task on popular datasets like +CNNDM and NYT. Both the standard prompt extractor and the RL-tuned model have +show strong generalization ability to unseen control prompt templates. +" +LLM Powered Sim-to-real Transfer for Traffic Signal Control,Longchao Da,http://arxiv.org/pdf/2308.14284v3.pdf,2023-08-28,"['cs.ai', 'h.4.0']",2308.14284v3.pdf," Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks +aiming to provide efficient transportation and mitigate congestion waste. In +recent, promising results have been attained by Reinforcement Learning (RL) +methods through trial and error in simulators, bringing confidence in solving +cities' congestion headaches. However, there still exist performance gaps when +simulator-trained policies are deployed to the real world. This issue is mainly +introduced by the system dynamic difference between the training simulator and +the real-world environments. The Large Language Models (LLMs) are trained on +mass knowledge and proved to be equipped with astonishing inference abilities. +In this work, we leverage LLMs to understand and profile the system dynamics by +a prompt-based grounded action transformation. Accepting the cloze prompt +template, and then filling in the answer based on accessible context, the +pre-trained LLM's inference ability is exploited and applied to understand how +weather conditions, traffic states, and road types influence traffic dynamics, +being aware of this, the policies' action is taken and grounded based on +realistic dynamics, thus help the agent learn a more realistic policy. We +conduct experiments using DQN to show the effectiveness of the proposed +PromptGAT's ability in mitigating the performance gap from simulation to +reality (sim-to-real). +" +AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization,Hanqiu Deng,http://arxiv.org/pdf/2308.15939v1.pdf,2023-08-30,['cs.cv'],2308.15939v1.pdf," Contrastive Language-Image Pre-training (CLIP) models have shown promising +performance on zero-shot visual recognition tasks by learning visual +representations under natural language supervision. Recent studies attempt the +use of CLIP to tackle zero-shot anomaly detection by matching images with +normal and abnormal state prompts. However, since CLIP focuses on building +correspondence between paired text prompts and global image-level +representations, the lack of patch-level vision to text alignment limits its +capability on precise visual anomaly localization. In this work, we introduce a +training-free adaptation (TFA) framework of CLIP for zero-shot anomaly +localization. In the visual encoder, we innovate a training-free value-wise +attention mechanism to extract intrinsic local tokens of CLIP for patch-level +local description. From the perspective of text supervision, we particularly +design a unified domain-aware contrastive state prompting template. On top of +the proposed TFA, we further introduce a test-time adaptation (TTA) mechanism +to refine anomaly localization results, where a layer of trainable parameters +in the adapter is optimized using TFA's pseudo-labels and synthetic +noise-corrupted tokens. With both TFA and TTA adaptation, we significantly +exploit the potential of CLIP for zero-shot anomaly localization and +demonstrate the effectiveness of our proposed methods on various datasets. +" +Investigating the Applicability of Self-Assessment Tests for Personality Measurement of Large Language Models,Akshat Gupta,http://arxiv.org/pdf/2309.08163v1.pdf,2023-09-15,"['cs.cl', 'cs.ai']",2309.08163v1.pdf," As large language models (LLM) evolve in their capabilities, various recent +studies have tried to quantify their behavior using psychological tools created +to study human behavior. One such example is the measurement of ""personality"" +of LLMs using personality self-assessment tests. In this paper, we take three +such studies on personality measurement of LLMs that use personality +self-assessment tests created to study human behavior. We use the prompts used +in these three different papers to measure the personality of the same LLM. We +find that all three prompts lead very different personality scores. This simple +test reveals that personality self-assessment scores in LLMs depend on the +subjective choice of the prompter. Since we don't know the ground truth value +of personality scores for LLMs as there is no correct answer to such questions, +there's no way of claiming if one prompt is more or less correct than the +other. We then introduce the property of option order symmetry for personality +measurement of LLMs. Since most of the self-assessment tests exist in the form +of multiple choice question (MCQ) questions, we argue that the scores should +also be robust to not just the prompt template but also the order in which the +options are presented. This test unsurprisingly reveals that the answers to the +self-assessment tests are not robust to the order of the options. These simple +tests, done on ChatGPT and Llama2 models show that self-assessment personality +tests created for humans are not appropriate for measuring personality in LLMs. +" +InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists,Yulu Gan,http://arxiv.org/pdf/2310.00390v1.pdf,2023-09-30,['cs.cv'],2310.00390v1.pdf," Recent advances in generative diffusion models have enabled text-controlled +synthesis of realistic and diverse images with impressive quality. Despite +these remarkable advances, the application of text-to-image generative models +in computer vision for standard visual recognition tasks remains limited. The +current de facto approach for these tasks is to design model architectures and +loss functions that are tailored to the task at hand. In this paper, we develop +a unified language interface for computer vision tasks that abstracts away +task-specific design choices and enables task execution by following natural +language instructions. Our approach involves casting multiple computer vision +tasks as text-to-image generation problems. Here, the text represents an +instruction describing the task, and the resulting image is a visually-encoded +task output. To train our model, we pool commonly-used computer vision datasets +covering a range of tasks, including segmentation, object detection, depth +estimation, and classification. We then use a large language model to +paraphrase prompt templates that convey the specific tasks to be conducted on +each image, and through this process, we create a multi-modal and multi-task +training dataset comprising input and output images along with annotated +instructions. Following the InstructPix2Pix architecture, we apply +instruction-tuning to a text-to-image diffusion model using our constructed +dataset, steering its functionality from a generative model to an +instruction-guided multi-task vision learner. Experiments demonstrate that our +model, dubbed InstructCV, performs competitively compared to other generalist +and task-specific vision models. Moreover, it exhibits compelling +generalization capabilities to unseen data, categories, and user instructions. +" +Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task,Guanting Dong,http://arxiv.org/pdf/2310.06504v1.pdf,2023-10-10,"['cs.cl', 'cs.ai', 'cs.lg']",2310.06504v1.pdf," With the increasing capabilities of large language models (LLMs), these +high-performance models have achieved state-of-the-art results on a wide range +of natural language processing (NLP) tasks. However, the models' performance on +commonly-used benchmark datasets often fails to accurately reflect their +reliability and robustness when applied to real-world noisy data. To address +these challenges, we propose a unified robustness evaluation framework based on +the slot-filling task to systematically evaluate the dialogue understanding +capability of LLMs in diverse input perturbation scenarios. Specifically, we +construct a input perturbation evaluation dataset, Noise-LLM, which contains +five types of single perturbation and four types of mixed perturbation data. +Furthermore, we utilize a multi-level data augmentation method (character, +word, and sentence levels) to construct a candidate data pool, and carefully +design two ways of automatic task demonstration construction strategies +(instance-level and entity-level) with various prompt templates. Our aim is to +assess how well various robustness methods of LLMs perform in real-world noisy +scenarios. The experiments have demonstrated that the current open-source LLMs +generally achieve limited perturbation robustness performance. Based on these +experimental observations, we make some forward-looking suggestions to fuel the +research in this direction. +" +Do Language Models Learn about Legal Entity Types during Pretraining?,Claire Barale,http://arxiv.org/pdf/2310.13092v1.pdf,2023-10-19,['cs.cl'],2310.13092v1.pdf," Language Models (LMs) have proven their ability to acquire diverse linguistic +knowledge during the pretraining phase, potentially serving as a valuable +source of incidental supervision for downstream tasks. However, there has been +limited research conducted on the retrieval of domain-specific knowledge, and +specifically legal knowledge. We propose to explore the task of Entity Typing, +serving as a proxy for evaluating legal knowledge as an essential aspect of +text comprehension, and a foundational task to numerous downstream legal NLP +applications. Through systematic evaluation and analysis and two types of +prompting (cloze sentences and QA-based templates) and to clarify the nature of +these acquired cues, we compare diverse types and lengths of entities both +general and domain-specific entities, semantics or syntax signals, and +different LM pretraining corpus (generic and legal-oriented) and architectures +(encoder BERT-based and decoder-only with Llama2). We show that (1) Llama2 +performs well on certain entities and exhibits potential for substantial +improvement with optimized prompt templates, (2) law-oriented LMs show +inconsistent performance, possibly due to variations in their training corpus, +(3) LMs demonstrate the ability to type entities even in the case of +multi-token entities, (4) all models struggle with entities belonging to +sub-domains of the law (5) Llama2 appears to frequently overlook syntactic +cues, a shortcoming less present in BERT-based architectures. +" +LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking,Zhenrui Yue,http://arxiv.org/pdf/2311.02089v1.pdf,2023-10-25,"['cs.ir', 'cs.ai', 'cs.cl']",2311.02089v1.pdf," Recently, large language models (LLMs) have exhibited significant progress in +language understanding and generation. By leveraging textual features, +customized LLMs are also applied for recommendation and demonstrate +improvements across diverse recommendation scenarios. Yet the majority of +existing methods perform training-free recommendation that heavily relies on +pretrained knowledge (e.g., movie recommendation). In addition, inference on +LLMs is slow due to autoregressive generation, rendering existing methods less +effective for real-time recommendation. As such, we propose a two-stage +framework using large language models for ranking-based recommendation +(LlamaRec). In particular, we use small-scale sequential recommenders to +retrieve candidates based on the user interaction history. Then, both history +and retrieved items are fed to the LLM in text via a carefully designed prompt +template. Instead of generating next-item titles, we adopt a verbalizer-based +approach that transforms output logits into probability distributions over the +candidate items. Therefore, the proposed LlamaRec can efficiently rank items +without generating long text. To validate the effectiveness of the proposed +framework, we compare against state-of-the-art baseline methods on benchmark +datasets. Our experimental results demonstrate the performance of LlamaRec, +which consistently achieves superior performance in both recommendation +performance and efficiency. +" +Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages,Zheng-Xin Yong,http://arxiv.org/pdf/2303.13592v4.pdf,2023-03-23,"['cs.cl', 'cs.ai']",2303.13592v4.pdf," While code-mixing is a common linguistic practice in many parts of the world, +collecting high-quality and low-cost code-mixed data remains a challenge for +natural language processing (NLP) research. The recent proliferation of Large +Language Models (LLMs) compels one to ask: how capable are these systems in +generating code-mixed data? In this paper, we explore prompting multilingual +LLMs in a zero-shot manner to generate code-mixed data for seven languages in +South East Asia (SEA), namely Indonesian, Malay, Chinese, Tagalog, Vietnamese, +Tamil, and Singlish. We find that publicly available multilingual +instruction-tuned models such as BLOOMZ and Flan-T5-XXL are incapable of +producing texts with phrases or clauses from different languages. ChatGPT +exhibits inconsistent capabilities in generating code-mixed texts, wherein its +performance varies depending on the prompt template and language pairing. For +instance, ChatGPT generates fluent and natural Singlish texts (an English-based +creole spoken in Singapore), but for English-Tamil language pair, the system +mostly produces grammatically incorrect or semantically meaningless utterances. +Furthermore, it may erroneously introduce languages not specified in the +prompt. Based on our investigation, existing multilingual LLMs exhibit a wide +range of proficiency in code-mixed data generation for SEA languages. As such, +we advise against using LLMs in this context without extensive human checks. +" +"Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency",Zhihan Liu,http://arxiv.org/pdf/2309.17382v2.pdf,2023-09-29,"['cs.ai', 'cs.lg']",2309.17382v2.pdf," Large language models (LLMs) demonstrate impressive reasoning abilities, but +translating reasoning into actions in the real world remains challenging. In +particular, it remains unclear how to complete a given task provably within a +minimum number of interactions with the external environment, e.g., through an +internal mechanism of reasoning. To this end, we propose a principled framework +with provable regret guarantees to orchestrate reasoning and acting, which we +call ""reason for future, act for now"" (\texttt{RAFA}). Specifically, we design +a prompt template for reasoning that learns from the memory buffer and plans a +future trajectory over a long horizon (""reason for future""). At each step, the +LLM agent takes the initial action of the planned trajectory (""act for now""), +stores the collected feedback in the memory buffer, and reinvokes the reasoning +routine to replan the future trajectory from the new state. + The key idea is to cast reasoning in LLMs as learning and planning in +Bayesian adaptive Markov decision processes (MDPs). Correspondingly, we prompt +LLMs to form an updated posterior of the unknown environment from the memory +buffer (learning) and generate an optimal trajectory for multiple future steps +that maximizes a value function (planning). The learning and planning +subroutines are performed in an ""in-context"" manner to emulate the actor-critic +update for MDPs. Our theoretical analysis proves that the novel combination of +long-term reasoning and short-term acting achieves a $\sqrt{T}$ regret. In +particular, the regret bound highlights an intriguing interplay between the +prior knowledge obtained through pretraining and the uncertainty reduction +achieved by reasoning and acting. Our empirical validation shows that it +outperforms various existing frameworks and achieves nearly perfect scores on a +few benchmarks. +" +ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction,Jianghao Lin,http://arxiv.org/pdf/2310.09234v2.pdf,2023-10-13,"['cs.ir', 'cs.ai']",2310.09234v2.pdf," Click-through rate (CTR) prediction has become increasingly indispensable for +various Internet applications. Traditional CTR models convert the multi-field +categorical data into ID features via one-hot encoding, and extract the +collaborative signals among features. Such a paradigm suffers from the problem +of semantic information loss. Another line of research explores the potential +of pretrained language models (PLMs) for CTR prediction by converting input +data into textual sentences through hard prompt templates. Although semantic +signals are preserved, they generally fail to capture the collaborative +information (e.g., feature interactions, pure ID features), not to mention the +unacceptable inference overhead brought by the huge model size. In this paper, +we aim to model both the semantic knowledge and collaborative knowledge for +accurate CTR estimation, and meanwhile address the inference inefficiency +issue. To benefit from both worlds and close their gaps, we propose a novel +model-agnostic framework (i.e., ClickPrompt), where we incorporate CTR models +to generate interaction-aware soft prompts for PLMs. We design a +prompt-augmented masked language modeling (PA-MLM) pretraining task, where PLM +has to recover the masked tokens based on the language context, as well as the +soft prompts generated by CTR model. The collaborative and semantic knowledge +from ID and textual features would be explicitly aligned and interacted via the +prompt interface. Then, we can either tune the CTR model with PLM for superior +performance, or solely tune the CTR model without PLM for inference efficiency. +Experiments on four real-world datasets validate the effectiveness of +ClickPrompt compared with existing baselines. +" +ALT: Towards Fine-grained Alignment between Language and CTR Models for Click-Through Rate Prediction,Hangyu Wang,http://arxiv.org/pdf/2310.19453v1.pdf,2023-10-30,"['cs.ir', 'cs.ai']",2310.19453v1.pdf," Click-through rate (CTR) prediction plays as a core function module in +various personalized online services. According to the data modality and input +format, the models for CTR prediction can be mainly classified into two +categories. The first one is the traditional CTR models that take as inputs the +one-hot encoded ID features of tabular modality, which aims to capture the +collaborative signals via feature interaction modeling. The second category +takes as inputs the sentences of textual modality obtained by hard prompt +templates, where pretrained language models (PLMs) are adopted to extract the +semantic knowledge. These two lines of research generally focus on different +characteristics of the same input data (i.e., textual and tabular modalities), +forming a distinct complementary relationship with each other. Therefore, in +this paper, we propose to conduct fine-grained feature-level Alignment between +Language and CTR models (ALT) for CTR prediction. Apart from the common +CLIP-like instance-level contrastive learning, we further design a novel joint +reconstruction pretraining task for both masked language and tabular modeling. +Specifically, the masked data of one modality (i.e., tokens or features) has to +be recovered with the help of the other modality, which establishes the +feature-level interaction and alignment via sufficient mutual information +extraction between dual modalities. Moreover, we propose three different +finetuning strategies with the option to train the aligned language and CTR +models separately or jointly for downstream CTR prediction tasks, thus +accommodating the varying efficacy and efficiency requirements for industrial +applications. Extensive experiments on three real-world datasets demonstrate +that ALT outperforms SOTA baselines, and is highly compatible for various +language and CTR models. +"