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arxiv_datasets_llm

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Event-level Knowledge Editing

Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, Juanzi Li

Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge triplets. However, natural knowledge updates in the real world come from the occurrences of new events rather than direct changes in factual triplets. In this paper, we propose a new task setting: event-level knowledge editing, which directly edits new events into LLMs and improves over conventional triplet-level editing on (1) Efficiency. A single event edit leads to updates in multiple entailed knowledge triplets. (2) Completeness. Beyond updating factual knowledge, event-level editing also requires considering the event influences and updating LLMs' knowledge about future trends. We construct a high-quality event-level editing benchmark ELKEN, consisting of 1,515 event edits, 6,449 questions about factual knowledge, and 10,150 questions about future tendencies. We systematically evaluate the performance of various knowledge editing methods and LLMs on this benchmark. We find that ELKEN poses significant challenges to existing knowledge editing approaches. Our codes and dataset are publicly released to facilitate further research.

http://arxiv.org/abs/2402.13093v2

cs.CL, cs.AI

Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, Yuxian Gu, Xin Cheng, Xun Wang, Si-Qing Chen, Li Dong, Wei Lu, Zhifang Sui, Benyou Wang, Wai Lam, Furu Wei

We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on seed examples or existing datasets to construct instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates large-scale synthetic instruction data across all disciplines. Specifically, inspired by the systematic structure in human education system, we build the taxonomy by decomposing human knowledge and capabilities to various fields, sub-fields and ultimately, distinct disciplines semi-automatically, facilitated by LLMs. Subsequently, we generate a comprehensive list of subjects for every discipline and proceed to design a syllabus tailored to each subject, again utilizing LLMs. With the fine-grained key concepts detailed in every class session of the syllabus, we are able to generate diverse instructions with a broad coverage across the entire spectrum of human knowledge and skills. Extensive experiments on large language models (e.g., Mistral) demonstrate that GLAN excels in multiple dimensions from mathematical reasoning, coding, academic exams, logical reasoning to general instruction following without using task-specific training data of these tasks. In addition, GLAN allows for easy customization and new fields or skills can be added by simply incorporating a new node into our taxonomy.

http://arxiv.org/abs/2402.13064v1

cs.CL

Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries

Seanie Lee, Jianpeng Cheng, Joris Driesen, Alexandru Coca, Anders Johannsen

Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning. Previous works use raw dialogue context as search keys and queries, and a retriever is fine-tuned with annotated dialogues to achieve superior performance. However, the approach is less suited for scaling to new domains or new annotation languages, where fine-tuning data is unavailable. To address this problem, we handle the task of conversation retrieval based on text summaries of the conversations. A LLM-based conversation summarizer is adopted for query and key generation, which enables effective maximum inner product search. To avoid the extra inference cost brought by LLM-based conversation summarization, we further distill a light-weight conversation encoder which produces query embeddings without decoding summaries for test conversations. We validate our retrieval approach on MultiWOZ datasets with GPT-Neo-2.7B and LLaMA-7B/30B. The experimental results show a significant improvement over relevant baselines in real few-shot DST settings.

http://arxiv.org/abs/2402.13043v3

cs.CL

SiLLM: Large Language Models for Simultaneous Machine Translation

Shoutao Guo, Shaolei Zhang, Zhengrui Ma, Min Zhang, Yang Feng

Simultaneous Machine Translation (SiMT) generates translations while reading the source sentence, necessitating a policy to determine the optimal timing for reading and generating words. Despite the remarkable performance achieved by Large Language Models (LLM) across various NLP tasks, existing SiMT methods predominantly focus on conventional transformers, employing a single model to concurrently determine the policy and generate the translations. However, given the complexity of SiMT, it is challenging to effectively address both tasks with a single model. Therefore, there is a need to decouple the SiMT task into policy-decision and translation sub-tasks. We propose SiLLM, which delegates the two sub-tasks to separate agents, thereby incorporating LLM into SiMT. The policy-decision agent is managed by a conventional SiMT model, responsible for determining the translation policy. The translation agent, leveraging the capabilities of LLM, generates translation using the partial source sentence. The two agents collaborate to accomplish SiMT. To facilitate the application of token-level policies determined by conventional SiMT models to LLM, we propose a word-level policy adapted for LLM. Experiments on two datasets demonstrate that, with a small amount of data for fine-tuning LLM, SiLLM attains state-of-the-art performance.

http://arxiv.org/abs/2402.13036v1

cs.CL

Learning to Check: Unleashing Potentials for Self-Correction in Large Language Models

Che Zhang, Zhenyang Xiao, Chengcheng Han, Yixin Lian, Yuejian Fang

Large language models (LLMs) have made significant strides in reasoning capabilities, with ongoing efforts to refine their reasoning through self-correction. However, recent studies suggest that self-correction can be limited or even counterproductive without external accurate knowledge, raising questions about the limits and effectiveness of self-correction. In this paper, we aim to enhance LLM's self-checking capabilities by meticulously designing training data, thereby improving the accuracy of self-correction. We conduct a detailed analysis of error types in mathematical reasoning and develop a tailored prompt, termed "Step CoT Check". Then we construct a checking-correction dataset for training models. After integrating the original CoT data and checking-correction data for training, we observe that models could improve their self-checking capabilities, thereby enhancing their self-correction capacity and eliminating the need for external feedback or ground truth labels to ascertain the endpoint of correction. We compare the performance of models fine-tuned with the "Step CoT Check" prompt against those refined using other promps within the context of checking-correction data. The "Step CoT Check" outperforms the other two check formats in model with lager parameters, providing more precise feedback thus achieving a higher rate of correctness. For reproducibility, all the datasets and codes are provided in https://github.com/bammt/Learn-to-check.

http://arxiv.org/abs/2402.13035v2

cs.CL, cs.AI

Understanding the effects of language-specific class imbalance in multilingual fine-tuning

Vincent Jung, Lonneke van der Plas

We study the effect of one type of imbalance often present in real-life multilingual classification datasets: an uneven distribution of labels across languages. We show evidence that fine-tuning a transformer-based Large Language Model (LLM) on a dataset with this imbalance leads to worse performance, a more pronounced separation of languages in the latent space, and the promotion of uninformative features. We modify the traditional class weighing approach to imbalance by calculating class weights separately for each language and show that this helps mitigate those detrimental effects. These results create awareness of the negative effects of language-specific class imbalance in multilingual fine-tuning and the way in which the model learns to rely on the separation of languages to perform the task.

http://arxiv.org/abs/2402.13016v1

cs.CL

Large Language Model-based Human-Agent Collaboration for Complex Task Solving

Xueyang Feng, Zhi-Yuan Chen, Yujia Qin, Yankai Lin, Xu Chen, Zhiyuan Liu, Ji-Rong Wen

In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential. In addition, we propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process. We construct a human-agent collaboration dataset to train this policy model in an offline reinforcement learning environment. Our validation tests confirm the model's effectiveness. The results demonstrate that the synergistic efforts of humans and LLM-based agents significantly improve performance in complex tasks, primarily through well-planned, limited human intervention. Datasets and code are available at: https://github.com/XueyangFeng/ReHAC.

http://arxiv.org/abs/2402.12914v1

cs.CL, cs.HC

PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning

Gyeongman Kim, Doohyuk Jang, Eunho Yang

Recent advancements in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. While knowledge distillation (KD) is a prominent method for this, research on KD for generative language models like LLMs is relatively sparse, and the approach of distilling student-friendly knowledge, which has shown promising performance in KD for classification models, remains unexplored in generative language models. To explore this approach, we propose PromptKD, a simple yet effective method that utilizes prompt tuning - for the first time in KD - to enable generative language models to transfer student-friendly knowledge. Unlike previous works in classification that require fine-tuning the entire teacher model for extracting student-friendly knowledge, PromptKD achieves similar effects by adding a small number of prompt tokens and tuning only the prompt with student guidance. Extensive experiments on instruction-following datasets using the GPT-2 model family show that PromptKD achieves state-of-the-art performance while adding only 0.0007% of the teacher's parameters as prompts. Further analysis suggests that distilling student-friendly knowledge alleviates exposure bias effectively throughout the entire training process, leading to performance enhancements.

http://arxiv.org/abs/2402.12842v1

cs.CL, cs.AI, cs.LG

PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs

An Liu, Zonghan Yang, Zhenhe Zhang, Qingyuan Hu, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-specific state-of-the-art models. One potential approach to enhance domain-specific capabilities of LLMs involves fine-tuning them using corresponding datasets. However, this method can be both resource and time-intensive, and not applicable to closed-source commercial LLMs. In this paper, we propose Preference Adaptation for Enhancing Domain-specific Abilities of LLMs (PANDA), a method designed to augment the domain-specific capabilities of LLMs by leveraging insights from the response preference of expert models without requiring fine-tuning. Our experimental results reveal that PANDA significantly enhances the domain-specific ability of LLMs on text classification and interactive decision tasks. Moreover, LLM with PANDA even outperforms the expert model that being learned on 4 tasks of ScienceWorld. This finding highlights the potential of exploring tuning-free approaches to achieve weak-to-strong generalization.

http://arxiv.org/abs/2402.12835v1

cs.CL, cs.AI

Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model

Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho D. Choi, Jie Zhou, Fei Liu

Factual inconsistency poses a significant hurdle for the commercial deployment of abstractive summarizers. Under this Large Language Model (LLM) era, this work focuses around two important questions: what is the best way to leverage LLM for factual inconsistency detection, and how could we distill a smaller LLM with both high efficiency and efficacy? Three zero-shot paradigms are firstly proposed and evaluated across five diverse datasets: direct inference on the entire summary or each summary window; entity verification through question generation and answering. Experiments suggest that LLM itself is capable to resolve this task train-free under the proper paradigm design, surpassing strong trained baselines by 2.8% on average. To further promote practical utility, we then propose training strategies aimed at distilling smaller open-source LLM that learns to score the entire summary at once with high accuracy, which outperforms the zero-shot approaches by much larger LLM, serving as an effective and efficient ready-to-use scorer.

http://arxiv.org/abs/2402.12821v1

cs.CL, cs.LG

Chain-of-Specificity: An Iteratively Refining Method for Eliciting Knowledge from Large Language Models

Kaiwen Wei, Jingyuan Zhang, Hongzhi Zhang, Fuzheng Zhang, Di Zhang, Li Jin, Yue Yu

Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that LLMs sometimes struggle with adhering to specific constraints (e.g., in specific place or at specific time), at times even overlooking them, which leads to responses that are either too generic or not fully satisfactory. Existing approaches attempted to address this issue by decomposing or rewriting input instructions, yet they fall short in adequately emphasizing specific constraints and in unlocking the underlying knowledge (e.g., programming within the context of software development). In response, this paper proposes a simple yet effective method named Chain-of-Specificity (CoS). Specifically, CoS iteratively emphasizes the specific constraints in the input instructions, unlocks knowledge within LLMs, and refines responses. Experiments conducted on publicly available and self-build complex datasets demonstrate that CoS outperforms existing methods in enhancing generated content especially for the specificity. Besides, as the number of specific constraints increase, other baselines falter, while CoS still performs well. Moreover, we show that distilling responses generated by CoS effectively enhances the ability of smaller models to follow the constrained instructions. Resources of this paper will be released for further research.

http://arxiv.org/abs/2402.15526v1

cs.AI, cs.LG

Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations

Guan-Ting Lin, Cheng-Han Chiang, Hung-yi Lee

In spoken dialogue, even if two current turns are the same sentence, their responses might still differ when they are spoken in different styles. The spoken styles, containing paralinguistic and prosodic information, mark the most significant difference between text and speech modality. When using text-only LLMs to model spoken dialogue, text-only LLMs cannot give different responses based on the speaking style of the current turn. In this paper, we focus on enabling LLMs to listen to the speaking styles and respond properly. Our goal is to teach the LLM that "even if the sentences are identical if they are spoken in different styles, their corresponding responses might be different". Since there is no suitable dataset for achieving this goal, we collect a speech-to-speech dataset, StyleTalk, with the following desired characteristics: when two current speeches have the same content but are spoken in different styles, their responses will be different. To teach LLMs to understand and respond properly to the speaking styles, we propose the Spoken-LLM framework that can model the linguistic content and the speaking styles. We train Spoken-LLM using the StyleTalk dataset and devise a two-stage training pipeline to help the Spoken-LLM better learn the speaking styles. Based on extensive experiments, we show that Spoken-LLM outperforms text-only baselines and prior speech LLMs methods.

http://arxiv.org/abs/2402.12786v1

cs.CL, eess.AS

Me LLaMA: Foundation Large Language Models for Medical Applications

Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Huan He, Lucila Ohno-Machado, Yonghui Wu, Hua Xu, Jiang Bian

Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA have hinted at their potential to revolutionize medical applications, yet their application in clinical settings often reveals limitations due to a lack of specialized training on medical-specific data. In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets. Our methodology leverages a comprehensive domain-specific data suite, including a large-scale, continual pre-training dataset with 129B tokens, an instruction tuning dataset with 214k samples, and a new medical evaluation benchmark (MIBE) across six critical medical tasks with 12 datasets. Our extensive evaluation using the MIBE shows that Me-LLaMA models achieve overall better performance than existing open-source medical LLMs in zero-shot, few-shot and supervised learning abilities. With task-specific instruction tuning, Me-LLaMA models outperform ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. In addition, we investigated the catastrophic forgetting problem, and our results show that Me-LLaMA models outperform other open-source medical LLMs in mitigating this issue. Me-LLaMA is one of the largest open-source medical foundation LLMs that use both biomedical and clinical data. It exhibits superior performance across both general and medical tasks compared to other open-source medical LLMs, rendering it an attractive choice for medical AI applications. We release our models, datasets, and evaluation scripts at: https://github.com/BIDS-Xu-Lab/Me-LLaMA.

http://arxiv.org/abs/2402.12749v4

cs.CL, cs.AI

Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering

Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha, Pai Zheng, Xiao Huang

Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attempts have been proposed to leverage large language models (LLMs) as an implicit knowledge source, it remains challenging since LLMs may generate hallucinations. Moreover, multiple knowledge sources, e.g., images, KGs and LLMs, cannot be readily aligned for complex scenarios. To tackle these, we present a novel modality-aware integration with LLMs for KVQA (MAIL). It carefully leverages multimodal knowledge for both image understanding and knowledge reasoning. Specifically, (i) we propose a two-stage prompting strategy with LLMs to densely embody the image into a scene graph with detailed visual features; (ii) We construct a coupled concept graph by linking the mentioned entities with external facts. (iii) A tailored pseudo-siamese graph medium fusion is designed for sufficient multimodal fusion. We utilize the shared mentioned entities in two graphs as mediums to bridge a tight inter-modal exchange, while maximally preserving insightful intra-modal learning by constraining the fusion within mediums. Extensive experiments on two benchmark datasets show the superiority of MAIL with 24x less resources.

http://arxiv.org/abs/2402.12728v2

cs.CV, cs.AI, cs.CL, cs.IR, cs.LG

Are Large Language Models Rational Investors?

Yuhang Zhou, Yuchen Ni, Xiang Liu, Jian Zhang, Sen Liu, Guangnan Ye, Hongfeng Chai

Large Language Models (LLMs) are progressively being adopted in financial analysis to harness their extensive knowledge base for interpreting complex market data and trends. However, their application in the financial domain is challenged by intrinsic biases (i.e., risk-preference bias) and a superficial grasp of market intricacies, underscoring the need for a thorough assessment of their financial insight. This study introduces a novel framework, Financial Bias Indicators (FBI), to critically evaluate the financial rationality of LLMs, focusing on their ability to discern and navigate the subtleties of financial information and to identify any irrational biases that might skew market analysis. Our research adopts an innovative methodology to measure financial rationality, integrating principles of behavioral finance to scrutinize the biases and decision-making patterns of LLMs. We conduct a comprehensive evaluation of 19 leading LLMs, considering factors such as model scale, training datasets, input strategies, etc. The findings reveal varying degrees of financial irrationality among the models, influenced by their design and training. Models trained specifically on financial datasets might exhibit greater irrationality, and it's possible that even larger financial language models (FinLLMs) could display more biases than smaller, more generalized models. This outcomes provide profound insights into how these elements affect the financial rationality of LLMs, indicating that targeted training and structured input methods could improve model performance. This work enriches our understanding of LLMs' strengths and weaknesses in financial applications, laying the groundwork for the development of more dependable and rational financial analysis tools.

http://arxiv.org/abs/2402.12713v1

cs.CL

SQL-CRAFT: Text-to-SQL through Interactive Refinement and Enhanced Reasoning

Hanchen Xia, Feng Jiang, Naihao Deng, Cunxiang Wang, Guojiang Zhao, Rada Mihalcea, Yue Zhang

Modern LLMs have become increasingly powerful, but they are still facing challenges in specialized tasks such as Text-to-SQL. We propose SQL-CRAFT, a framework to advance LLMs' SQL generation Capabilities through inteRActive reFinemenT and enhanced reasoning. We leverage an Interactive Correction Loop (IC-Loop) for LLMs to interact with databases automatically, as well as Python-enhanced reasoning. We conduct experiments on two Text-to-SQL datasets, Spider and Bird, with performance improvements of up to 5.7% compared to the naive prompting method. Moreover, our method surpasses the current state-of-the-art on the Spider Leaderboard, demonstrating the effectiveness of our framework.

http://arxiv.org/abs/2402.14851v1

cs.CL, cs.AI, cs.DB

FormulaQA: A Question Answering Dataset for Formula-Based Numerical Reasoning

Xiao Li, Sichen Liu, Bolin Zhu, Yin Zhu, Yiwei Liu, Gong Cheng

The application of formulas is a fundamental ability of humans when addressing numerical reasoning problems. However, existing numerical reasoning datasets seldom explicitly indicate the formulas employed during the reasoning steps. To bridge this gap, we propose a question answering dataset for formula-based numerical reasoning called FormulaQA, from junior high school physics examinations. We further conduct evaluations on LLMs with size ranging from 7B to over 100B parameters utilizing zero-shot and few-shot chain-of-thoughts methods and we explored the approach of using retrieval-augmented LLMs when providing an external formula database. We also fine-tune on smaller models with size not exceeding 2B. Our empirical findings underscore the significant potential for improvement in existing models when applied to our complex, formula-driven FormulaQA.

http://arxiv.org/abs/2402.12692v2

cs.CL

The FinBen: An Holistic Financial Benchmark for Large Language Models

Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, Yijing Xu, Haoqiang Kang, Ziyan Kuang, Chenhan Yuan, Kailai Yang, Zheheng Luo, Tianlin Zhang, Zhiwei Liu, Guojun Xiong, Zhiyang Deng, Yuechen Jiang, Zhiyuan Yao, Haohang Li, Yangyang Yu, Gang Hu, Jiajia Huang, Xiao-Yang Liu, Alejandro Lopez-Lira, Benyou Wang, Yanzhao Lai, Hao Wang, Min Peng, Sophia Ananiadou, Jimin Huang

LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of thorough evaluations and the complexity of financial tasks. This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs. In this paper, we introduce FinBen, the first comprehensive open-sourced evaluation benchmark, specifically designed to thoroughly assess the capabilities of LLMs in the financial domain. FinBen encompasses 35 datasets across 23 financial tasks, organized into three spectrums of difficulty inspired by the Cattell-Horn-Carroll theory, to evaluate LLMs' cognitive abilities in inductive reasoning, associative memory, quantitative reasoning, crystallized intelligence, and more. Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals insights into their strengths and limitations within the financial domain. The findings indicate that GPT-4 leads in quantification, extraction, numerical reasoning, and stock trading, while Gemini shines in generation and forecasting; however, both struggle with complex extraction and forecasting, showing a clear need for targeted enhancements. Instruction tuning boosts simple task performance but falls short in improving complex reasoning and forecasting abilities. FinBen seeks to continuously evaluate LLMs in finance, fostering AI development with regular updates of tasks and models.

http://arxiv.org/abs/2402.12659v1

cs.CL, cs.AI, cs.CE

Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM

Hejie Cui, Xinyu Fang, Ran Xu, Xuan Kan, Joyce C. Ho, Carl Yang

Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous information, such as structural data in tabular form and unstructured data in textual notes. Different types of information in EHRs can complement each other and provide a more complete picture of the health status of a patient. While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied. This is mostly because of the complex medical coding systems used and the noise and redundancy present in the written notes. In this work, we propose a new framework called MINGLE, which integrates both structures and semantics in EHR effectively. Our framework uses a two-level infusion strategy to combine medical concept semantics and clinical note semantics into hypergraph neural networks, which learn the complex interactions between different types of data to generate visit representations for downstream prediction. Experiment results on two EHR datasets, the public MIMIC-III and private CRADLE, show that MINGLE can effectively improve predictive performance by 11.83% relatively, enhancing semantic integration as well as multimodal fusion for structural and textual EHR data.

http://arxiv.org/abs/2403.08818v1

cs.LG, cs.AI, cs.CL

Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection

Ruibo Chen, Yihan Wu, Lichang Chen, Guodong Liu, Qi He, Tianyi Xiong, Chenxi Liu, Junfeng Guo, Heng Huang

Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a new and unexplored research area for vision-language models (VLMs). Existing data selection approaches on LLMs either rely on single unreliable scores, or use downstream tasks for selection, which is time-consuming and can lead to potential over-fitting on the chosen evaluation datasets. To address this challenge, we introduce a novel dataset selection method, Self-Filter, that utilizes the VLM itself as a filter. This approach is inspired by the observation that VLMs benefit from training with the most challenging instructions. Self-Filter operates in two stages. In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM. In the second stage, we use the trained score net to measure the difficulty of each instruction, select the most challenging samples, and penalize similar samples to encourage diversity. Comprehensive experiments on LLaVA and MiniGPT-4 show that Self-Filter can reach better results compared to full data settings with merely about 15% samples, and can achieve superior performance against competitive baselines.

http://arxiv.org/abs/2402.12501v1

cs.CL

Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?

Nishant Balepur, Abhilasha Ravichander, Rachel Rudinger

Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with choices-only prompts, where models must select the correct answer only from the choices. In three MCQA datasets and four LLMs, this prompt bests a majority baseline in 11/12 cases, with up to 0.33 accuracy gain. To help explain this behavior, we conduct an in-depth, black-box analysis on memorization, choice dynamics, and question inference. Our key findings are threefold. First, we find no evidence that the choices-only accuracy stems from memorization alone. Second, priors over individual choices do not fully explain choices-only accuracy, hinting that LLMs use the group dynamics of choices. Third, LLMs have some ability to infer a relevant question from choices, and surprisingly can sometimes even match the original question. We hope to motivate the use of stronger baselines in MCQA benchmarks, the design of robust MCQA datasets, and further efforts to explain LLM decision-making.

http://arxiv.org/abs/2402.12483v1

cs.CL

A synthetic data approach for domain generalization of NLI models

Mohammad Javad Hosseini, Andrey Petrov, Alex Fabrikant, Annie Louis

Natural Language Inference (NLI) remains an important benchmark task for LLMs. NLI datasets are a springboard for transfer learning to other semantic tasks, and NLI models are standard tools for identifying the faithfulness of model-generated text. There are several large scale NLI datasets today, and models have improved greatly by hill-climbing on these collections. Yet their realistic performance on out-of-distribution/domain data is less well-understood. We present an in-depth exploration of the problem of domain generalization of NLI models. We demonstrate a new approach for generating synthetic NLI data in diverse domains and lengths, so far not covered by existing training sets. The resulting examples have meaningful premises, the hypotheses are formed in creative ways rather than simple edits to a few premise tokens, and the labels have high accuracy. We show that models trained on this data ($685$K synthetic examples) have the best generalization to completely new downstream test settings. On the TRUE benchmark, a T5-small model trained with our data improves around $7\%$ on average compared to training on the best alternative dataset. The improvements are more pronounced for smaller models, while still meaningful on a T5 XXL model. We also demonstrate gains on test sets when in-domain training data is augmented with our domain-general synthetic data.

http://arxiv.org/abs/2402.12368v1

cs.CL

DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models

Berkay Berabi, Alexey Gronskiy, Veselin Raychev, Gishor Sivanrupan, Victor Chibotaru, Martin Vechev

The automated program repair field has attracted substantial interest over the years, but despite significant research efforts, creating a system that works well for complex semantic bugs such as security vulnerabilities has proven difficult. A promising direction to solve this challenge is by leveraging large language models (LLMs), which are increasingly used to solve various programming tasks. In this paper, we investigate the effectiveness of LLMs for solving code-repair task. We show that the task is difficult as it requires the model to learn long-range code relationships, a task that inherently relies on extensive amounts of training data. At the same time, creating a large, clean dataset for complex program bugs and their corresponding fixes is non-trivial. We propose a technique to address these challenges with a new approach for querying and fine-tuning LLMs. The idea is to use program analysis to limit the LLM's attention mechanism on the portions of code needed to perform the fix, drastically reducing the amount of required training data. Concretely, for training and inference, rather than feeding the entire program to the LLM, we reduce its code to a much shorter snippet that contains the reported defect together with the necessary context - and use that instead. Our evaluation shows that this code reduction approach substantially improves available models such as GPT-4 using few-shot learning, as well as fine-tuning models. To train and evaluate our system, we created a comprehensive code fixing dataset by extensively labeling 156 bug patterns (including 40 security rules), requiring complex interprocedural dataflow to discover. Our best system with Mixtral-8x7B can remove more than 80% of the reported defects while exactly matching the human fix in between 10 and 50% of cases, outperforming baselines based on GPT-3.5 and GPT-4, or based on window-based models like TFix.

http://arxiv.org/abs/2402.13291v2

cs.CR, cs.LG, cs.PL, cs.SE

Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge

Julien Delile, Srayanta Mukherjee, Anton Van Pamel, Leonid Zhukov

Large language models (LLMs) are transforming the way information is retrieved with vast amounts of knowledge being summarized and presented via natural language conversations. Yet, LLMs are prone to highlight the most frequently seen pieces of information from the training set and to neglect the rare ones. In the field of biomedical research, latest discoveries are key to academic and industrial actors and are obscured by the abundance of an ever-increasing literature corpus (the information overload problem). Surfacing new associations between biomedical entities, e.g., drugs, genes, diseases, with LLMs becomes a challenge of capturing the long-tail knowledge of the biomedical scientific production. To overcome this challenge, Retrieval Augmented Generation (RAG) has been proposed to alleviate some of the shortcomings of LLMs by augmenting the prompts with context retrieved from external datasets. RAG methods typically select the context via maximum similarity search over text embeddings. In this study, we show that RAG methods leave out a significant proportion of relevant information due to clusters of over-represented concepts in the biomedical literature. We introduce a novel information-retrieval method that leverages a knowledge graph to downsample these clusters and mitigate the information overload problem. Its retrieval performance is about twice better than embedding similarity alternatives on both precision and recall. Finally, we demonstrate that both embedding similarity and knowledge graph retrieval methods can be advantageously combined into a hybrid model that outperforms both, enabling potential improvements to biomedical question-answering models.

http://arxiv.org/abs/2402.12352v1

cs.CL, cs.IR

Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!

Zhanhui Zhou, Jie Liu, Zhichen Dong, Jiaheng Liu, Chao Yang, Wanli Ouyang, Yu Qiao

Large language models (LLMs) need to undergo safety alignment to ensure safe conversations with humans. However, this paper introduces an inference-time attack method, demonstrating that safety alignment can be easily reversed to produce harmful language models without additional training. Specifically, this reversal is achieved by contrasting the output token distribution of a safety-aligned language model (e.g., Llama-2-chat) against its pre-trained version (e.g., Llama-2) so that the token predictions are shifted towards the opposite direction of alignment. We name this method emulated disalignment (ED) because it uses pure sampling to provably emulate (or "approximate") the result of fine-tuning the pre-trained model to minimize a safety reward. Our experiments with ED across three evaluation datasets and four model families (Llama-1, Llama-2, Mistral, and Alpaca) show that ED doubles the harmfulness of pre-trained models and outperforms strong baselines, achieving the highest harmful rate in 43 out of 48 evaluation subsets by a large margin. Eventually, given ED's need for language model output token distributions, which particularly compromises open-source models, our findings highlight the importance of reevaluating the practice of open-sourcing language models even after safety alignment.

http://arxiv.org/abs/2402.12343v3

cs.CL, cs.AI, cs.LG

Large Language Model for Mental Health: A Systematic Review

Zhijun Guo, Alvina Lai, Johan Hilge Thygesen, Joseph Farrington, Thomas Keen, Kezhi Li

Large language models (LLMs) have received much attention and shown their potential in digital health, while their application in mental health is subject to ongoing debate. This systematic review aims to summarize and characterize the use of LLMs in mental health by investigating the strengths and limitations of the latest work in LLMs and discusses the challenges and opportunities for early screening, digital interventions, and other clinical applications in mental health. Following PRISMA guidelines, we examined English articles from PubMed, DBLP Computer Science Bibliography, and IEEE Xplore, published between 1 January 2017, and 1 September 2023, focusing on mental health and LLMs. The review analyzed 32 articles, including mental health analysis using social media datasets (n=13), mental health chatbots (n=10), and other mental health applications (n=9). Findings reveal LLMs' effectiveness in mental health issue detection and the enhancement of telepsychological services through personalised healthcare. Nonetheless, risks like text inconsistencies, hallucinatory content, and the lack of an ethical framework raise concerns about their clinical use. Despite these challenges, the advancement of LLMs underscores their potential as innovative clinical tools, necessitating further research and development. The review emphasizes that LLMs should complement, not replace, professional mental health services.

http://arxiv.org/abs/2403.15401v1

cs.CY, cs.AI, cs.CL

Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports

Felix J. Dorfner, Liv Jürgensen, Leonhard Donle, Fares Al Mohamad, Tobias R. Bodenmann, Mason C. Cleveland, Felix Busch, Lisa C. Adams, James Sato, Thomas Schultz, Albert E. Kim, Jameson Merkow, Keno K. Bressem, Christopher P. Bridge

Introduction: With the rapid advances in large language models (LLMs), there have been numerous new open source as well as commercial models. While recent publications have explored GPT-4 in its application to extracting information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to different leading open-source models. Materials and Methods: Two different and independent datasets were used. The first dataset consists of 540 chest x-ray reports that were created at the Massachusetts General Hospital between July 2019 and July 2021. The second dataset consists of 500 chest x-ray reports from the ImaGenome dataset. We then compared the commercial models GPT-3.5 Turbo and GPT-4 from OpenAI to the open-source models Mistral-7B, Mixtral-8x7B, Llama2-13B, Llama2-70B, QWEN1.5-72B and CheXbert and CheXpert-labeler in their ability to accurately label the presence of multiple findings in x-ray text reports using different prompting techniques. Results: On the ImaGenome dataset, the best performing open-source model was Llama2-70B with micro F1-scores of 0.972 and 0.970 for zero- and few-shot prompts, respectively. GPT-4 achieved micro F1-scores of 0.975 and 0.984, respectively. On the institutional dataset, the best performing open-source model was QWEN1.5-72B with micro F1-scores of 0.952 and 0.965 for zero- and few-shot prompting, respectively. GPT-4 achieved micro F1-scores of 0.975 and 0.973, respectively. Conclusion: In this paper, we show that while GPT-4 is superior to open-source models in zero-shot report labeling, the implementation of few-shot prompting can bring open-source models on par with GPT-4. This shows that open-source models could be a performant and privacy preserving alternative to GPT-4 for the task of radiology report classification.

http://arxiv.org/abs/2402.12298v1

cs.CL, cs.AI

Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from Large Language Models

Puxuan Yu, Daniel Cohen, Hemank Lamba, Joel Tetreault, Alex Jaimes

The process of scale calibration in ranking systems involves adjusting the outputs of rankers to correspond with significant qualities like click-through rates or relevance, crucial for mirroring real-world value and thereby boosting the system's effectiveness and reliability. Although there has been research on calibrated ranking losses within learning-to-rank models, the particular issue of adjusting the scale for neural rankers, which excel in handling textual information, has not been thoroughly examined. Neural ranking models are adept at processing text data, yet the application of existing scale calibration techniques to these models poses significant challenges due to their complexity and the intensive training they require, often resulting in suboptimal outcomes. This study delves into the potential of large language models (LLMs) to provide uncertainty measurements for a query and document pair that correlate with the scale-calibrated scores. By employing Monte Carlo sampling to gauge relevance probabilities from LLMs and incorporating natural language explanations (NLEs) to articulate this uncertainty, we carry out comprehensive tests on two major document ranking datasets. Our findings reveal that the approach leveraging NLEs outperforms existing calibration methods under various training scenarios, leading to better calibrated neural rankers.

http://arxiv.org/abs/2402.12276v1

cs.IR

Uncertainty quantification in fine-tuned LLMs using LoRA ensembles

Oleksandr Balabanov, Hampus Linander

Fine-tuning large language models can improve task specific performance, although a general understanding of what the fine-tuned model has learned, forgotten and how to trust its predictions is still missing. We derive principled uncertainty quantification for fine-tuned LLMs with posterior approximations using computationally efficient low-rank adaptation ensembles. We analyze three common multiple-choice datasets using low-rank adaptation ensembles based on Mistral-7b, and draw quantitative and qualitative conclusions on their perceived complexity and model efficacy on the different target domains during and after fine-tuning. In particular, backed by the numerical experiments, we hypothesise about signals from entropic uncertainty measures for data domains that are inherently difficult for a given architecture to learn.

http://arxiv.org/abs/2402.12264v1

cs.LG, cs.AI, cs.CL, stat.ML

Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships

Sebastian Koch, Narunas Vaskevicius, Mirco Colosi, Pedro Hermosilla, Timo Ropinski

Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. We present Open3DSG, an alternative approach to learn 3D scene graph prediction in an open world without requiring labeled scene graph data. We co-embed the features from a 3D scene graph prediction backbone with the feature space of powerful open world 2D vision language foundation models. This enables us to predict 3D scene graphs from 3D point clouds in a zero-shot manner by querying object classes from an open vocabulary and predicting the inter-object relationships from a grounded LLM with scene graph features and queried object classes as context. Open3DSG is the first 3D point cloud method to predict not only explicit open-vocabulary object classes, but also open-set relationships that are not limited to a predefined label set, making it possible to express rare as well as specific objects and relationships in the predicted 3D scene graph. Our experiments show that Open3DSG is effective at predicting arbitrary object classes as well as their complex inter-object relationships describing spatial, supportive, semantic and comparative relationships.

http://arxiv.org/abs/2402.12259v2

cs.CV

Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models

Mosh Levy, Alon Jacoby, Yoav Goldberg

This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs). Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood. We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of input length. We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types and locations. Our findings show a notable degradation in LLMs' reasoning performance at much shorter input lengths than their technical maximum. We show that the degradation trend appears in every version of our dataset, although at different intensities. Additionally, our study reveals that traditional perplexity metrics do not correlate with performance of LLMs' in long input reasoning tasks. We analyse our results and identify failure modes that can serve as useful guides for future research, potentially informing strategies to address the limitations observed in LLMs.

http://arxiv.org/abs/2402.14848v1

cs.CL, cs.AI

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yugang Jiang, Xipeng Qiu

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages. We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs. Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/

http://arxiv.org/abs/2402.12226v3

cs.CL, cs.AI, cs.CV, cs.LG

Reformatted Alignment

Run-Ze Fan, Xuefeng Li, Haoyang Zou, Junlong Li, Shwai He, Ethan Chern, Jiewen Hu, Pengfei Liu

The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone to factual errors caused by LLM hallucinations. This paper explores elevating the quality of existing instruction data to better align with human values, introducing a simple and effective approach named ReAlign, which reformats the responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence. This approach minimizes human annotation, hallucination, and the difficulty in scaling, remaining orthogonal to existing alignment techniques. Experimentally, ReAlign significantly boosts the general alignment ability, math reasoning, factuality, and readability of the LLMs. Encouragingly, without introducing any additional data or advanced training techniques, and merely by reformatting the response, LLaMA-2-13B's mathematical reasoning ability on GSM8K can be improved from 46.77% to 56.63% in accuracy. Additionally, a mere 5% of ReAlign data yields a 67% boost in general alignment ability measured by the Alpaca dataset. This work highlights the need for further research into the science and mechanistic interpretability of LLMs. We have made the associated code and data publicly accessible to support future studies at https://github.com/GAIR-NLP/ReAlign.

http://arxiv.org/abs/2402.12219v2

cs.CL, cs.AI, cs.LG

A Chinese Dataset for Evaluating the Safeguards in Large Language Models

Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Lizhi Lin, Zhenxuan Zhang, Jingru Zhao, Preslav Nakov, Timothy Baldwin

Many studies have demonstrated that large language models (LLMs) can produce harmful responses, exposing users to unexpected risks when LLMs are deployed. Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs. However, the focus has been almost exclusively on English, and little has been explored for other languages. Here we aim to bridge this gap. We first introduce a dataset for the safety evaluation of Chinese LLMs, and then extend it to two other scenarios that can be used to better identify false negative and false positive examples in terms of risky prompt rejections. We further present a set of fine-grained safety assessment criteria for each risk type, facilitating both manual annotation and automatic evaluation in terms of LLM response harmfulness. Our experiments on five LLMs show that region-specific risks are the prevalent type of risk, presenting the major issue with all Chinese LLMs we experimented with. Warning: this paper contains example data that may be offensive, harmful, or biased.

http://arxiv.org/abs/2402.12193v1

cs.CL

Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning

Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber

Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically involves a pre-trained embedding model, which converts queries and passages into vectors to capture their semantics. However, a standard pre-trained embedding model may exhibit sub-optimal performance when applied to specific domain knowledge, necessitating fine-tuning. This paper addresses scenarios where the embeddings are only available from a black-box model. We introduce Model augmented fine-tuning (Mafin) -- a novel approach for fine-tuning a black-box embedding model by augmenting it with a trainable embedding model. Our results demonstrate that Mafin significantly enhances the performance of the black-box embeddings by only requiring the training of a small augmented model. We validate the effectiveness of our method on both labeled and unlabeled datasets, illustrating its broad applicability and efficiency.

http://arxiv.org/abs/2402.12177v4

cs.LG, cs.AI, cs.CL

BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence

Jiajie Jin, Yutao Zhu, Yujia Zhou, Zhicheng Dou

Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy. However, inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM's answer quality. This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning (SFT), and preference alignment. We train BIDER by learning from crafting KSE, while maximizing its output to align with LLM's information acquisition preferences through reinforcement learning. Evaluations across five datasets show BIDER boosts LLMs' answer quality by 7% while reducing input content length in retrieval documents by 80%, outperforming existing methods. The proposed KSE simulation effectively equips LLMs with essential information for accurate question answering.

http://arxiv.org/abs/2402.12174v1

cs.CL

Transformer-based Causal Language Models Perform Clustering

Xinbo Wu, Lav R. Varshney

Even though large language models (LLMs) have demonstrated remarkable capability in solving various natural language tasks, the capability of an LLM to follow human instructions is still a concern. Recent works have shown great improvements in the instruction-following capability via additional training for instruction-following tasks. However, the mechanisms responsible for effective instruction-following capabilities remain inadequately understood. Here, we introduce a simplified instruction-following task and use synthetic datasets to analyze a Transformer-based causal language model. Our findings suggest that the model learns task-specific information by clustering data within its hidden space, with this clustering process evolving dynamically during learning. We also demonstrate how this phenomenon assists the model in handling unseen instances, and validate our results in a more realistic setting. Furthermore, we present inspired applications regarding pre-training and alignment.

http://arxiv.org/abs/2402.12151v2

cs.CL, cs.AI

Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One

Tianlin Li, Xiaoyu Zhang, Chao Du, Tianyu Pang, Qian Liu, Qing Guo, Chao Shen, Yang Liu

The widespread adoption of large language models (LLMs) underscores the urgent need to ensure their fairness. However, LLMs frequently present dominant viewpoints while ignoring alternative perspectives from minority parties, resulting in potential biases. We hypothesize that these fairness-violating behaviors occur because LLMs express their viewpoints using a human personality that represents the majority of training data. In response to this, we validate that prompting LLMs with specific roles can allow LLMs to express diverse viewpoints. Building on this insight and observation, we develop FairThinking, a pipeline designed to automatically generate roles that enable LLMs to articulate diverse perspectives for fair expressions. To evaluate FairThinking, we create a dataset with a thousand items covering three fairness-related topics and conduct experiments on GPT-3.5, GPT-4, Llama2, and Mistral to demonstrate its superior performance.

http://arxiv.org/abs/2402.12150v1

cs.CL, cs.AI, I.2; J.4

Do Large Language Models Understand Logic or Just Mimick Context?

Junbing Yan, Chengyu Wang, Jun Huang, Wei Zhang

Over the past few years, the abilities of large language models (LLMs) have received extensive attention, which have performed exceptionally well in complicated scenarios such as logical reasoning and symbolic inference. A significant factor contributing to this progress is the benefit of in-context learning and few-shot prompting. However, the reasons behind the success of such models using contextual reasoning have not been fully explored. Do LLMs have understand logical rules to draw inferences, or do they ``guess'' the answers by learning a type of probabilistic mapping through context? This paper investigates the reasoning capabilities of LLMs on two logical reasoning datasets by using counterfactual methods to replace context text and modify logical concepts. Based on our analysis, it is found that LLMs do not truly understand logical rules; rather, in-context learning has simply enhanced the likelihood of these models arriving at the correct answers. If one alters certain words in the context text or changes the concepts of logical terms, the outputs of LLMs can be significantly disrupted, leading to counter-intuitive responses. This work provides critical insights into the limitations of LLMs, underscoring the need for more robust mechanisms to ensure reliable logical reasoning in LLMs.

http://arxiv.org/abs/2402.12091v1

cs.CL, cs.AI

LVCHAT: Facilitating Long Video Comprehension

Yu Wang, Zeyuan Zhang, Julian McAuley, Zexue He

Enabling large language models (LLMs) to read videos is vital for multimodal LLMs. Existing works show promise on short videos whereas long video (longer than e.g.~1 minute) comprehension remains challenging. The major problem lies in the over-compression of videos, i.e., the encoded video representations are not enough to represent the whole video. To address this issue, we propose Long Video Chat (LVChat), where Frame-Scalable Encoding (FSE) is introduced to dynamically adjust the number of embeddings in alignment with the duration of the video to ensure long videos are not overly compressed into a few embeddings. To deal with long videos whose length is beyond videos seen during training, we propose Interleaved Frame Encoding (IFE), repeating positional embedding and interleaving multiple groups of videos to enable long video input, avoiding performance degradation due to overly long videos. Experimental results show that LVChat significantly outperforms existing methods by up to 27\% in accuracy on long-video QA datasets and long-video captioning benchmarks. Our code is published at https://github.com/wangyu-ustc/LVChat.

http://arxiv.org/abs/2402.12079v1

cs.CV, cs.CL

EmoBench: Evaluating the Emotional Intelligence of Large Language Models

Sahand Sabour, Siyang Liu, Zheyuan Zhang, June M. Liu, Jinfeng Zhou, Alvionna S. Sunaryo, Juanzi Li, Tatia M. C. Lee, Rada Mihalcea, Minlie Huang

Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major shortcomings: first, they mainly focus on emotion recognition, neglecting essential EI capabilities such as emotion regulation and thought facilitation through emotion understanding; second, they are primarily constructed from existing datasets, which include frequent patterns, explicit information, and annotation errors, leading to unreliable evaluation. We propose EmoBench, a benchmark that draws upon established psychological theories and proposes a comprehensive definition for machine EI, including Emotional Understanding and Emotional Application. EmoBench includes a set of 400 hand-crafted questions in English and Chinese, which are meticulously designed to require thorough reasoning and understanding. Our findings reveal a considerable gap between the EI of existing LLMs and the average human, highlighting a promising direction for future research. Our code and data will be publicly available from https://github.com/Sahandfer/EmoBench.

http://arxiv.org/abs/2402.12071v1

cs.CL, cs.AI

Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs

Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo, Kun Fang, Ji-Rong Wen

The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies. However, determining the knowledge that an LLM already possesses and the knowledge that requires the help of a search engine remains an unresolved issue. Most existing methods solve this problem through the results of preliminary answers or reasoning done by the LLM itself, but this incurs excessively high computational costs. This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in LLMs with a slim proxy model, to enhance the LLM's knowledge acquisition process. We employ a proxy model which has far fewer parameters, and take its answers as heuristic answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM. We only conduct retrieval for the missing knowledge in questions that the LLM does not know. Extensive experimental results on five datasets with two LLMs demonstrate a notable improvement in the end-to-end performance of LLMs in question-answering tasks, achieving or surpassing current state-of-the-art models with lower LLM inference costs.

http://arxiv.org/abs/2402.12052v2

cs.CL

Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations

Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot

Incorporating natural language rationales in the prompt and In-Context Learning (ICL) has led to a significant improvement of Large Language Models (LLMs) performance. However, rationales currently require human-annotation or the use of auxiliary proxy models to target promising samples or generate high-quality rationales. In this work, we propose Self-AMPLIFY to generate automatically rationales from post hoc explanation methods applied to Small Language Models (SLMs) to improve their own performance. Self-AMPLIFY is a 3-step method that targets samples, generates rationales and builds a final prompt to leverage ICL. Self-AMPLIFY performance is evaluated on two SLMs and two datasets requiring reasoning abilities: these experiments show that Self-AMPLIFY achieves good results against competitors. Self-AMPLIFY is the first method to apply post hoc explanation methods to SLM to generate rationales to improve their own performance in a fully automated manner.

http://arxiv.org/abs/2402.12038v2

cs.LG, cs.CL

EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs

Song Guo, Fan Wu, Lei Zhang, Xiawu Zheng, Shengchuan Zhang, Fei Chao, Yiyu Shi, Rongrong Ji

Existing methods for fine-tuning sparse LLMs often suffer from resource-intensive requirements and high retraining costs. Additionally, many fine-tuning methods often rely on approximations or heuristic optimization strategies, which may lead to suboptimal solutions. To address these issues, we propose an efficient and fast framework for fine-tuning sparse LLMs based on minimizing reconstruction error. Our approach involves sampling a small dataset for calibration and utilizing backpropagation to iteratively optimize block-wise reconstruction error, on a block-by-block basis, aiming for optimal solutions. Extensive experiments on various benchmarks consistently demonstrate the superiority of our method over other baselines. For instance, on the Wikitext2 dataset with LlamaV1-7B at 70% sparsity, our proposed EBFT achieves a perplexity of 16.88, surpassing the state-of-the-art DSnoT with a perplexity of 75.14. Moreover, with a structured sparsity ratio of 26\%, EBFT achieves a perplexity of 16.27, outperforming LoRA (perplexity 16.44). Furthermore, the fine-tuning process of EBFT for LlamaV1-7B only takes approximately 30 minutes, and the entire framework can be executed on a single 16GB GPU. The source code is available at https://github.com/sunggo/EBFT.

http://arxiv.org/abs/2402.12419v1

cs.LG, cs.AI, cs.CL

Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models

Himanshu Beniwal, Kowsik Nandagopan D, Mayank Singh

Large Language Models (LLMs) are increasingly becoming ubiquitous, yet their ability to reason about and retain temporal information remains limited. This hinders their application in real-world scenarios where understanding the sequential nature of events is crucial. This paper experiments with state-of-the-art models on a novel, large-scale temporal dataset, \textbf{TempUN}, to reveal significant limitations in temporal retention and reasoning abilities. Interestingly, closed-source models indicate knowledge gaps more frequently, potentially suggesting a trade-off between uncertainty awareness and incorrect responses. Further, exploring various fine-tuning approaches yielded no major performance improvements. The associated dataset and code are available at the following URL (https://github.com/lingoiitgn/TempUN).

http://arxiv.org/abs/2402.11997v1

cs.CL, cs.AI, cs.LG

Structure Guided Large Language Model for SQL Generation

Qinggang Zhang, Junnan Dong, Hao Chen, Wentao Li, Feiran Huang, Xiao Huang

Generating accurate Structured Querying Language (SQL) is a long-standing problem, especially in matching users' semantic queries with structured databases and then generating structured SQL. Existing models typically input queries and database schemas into the LLM and rely on the LLM to perform semantic-structure matching and generate structured SQL. However, such solutions overlook the structural information within user queries and databases, which can be utilized to enhance the generation of structured SQL. This oversight can lead to inaccurate or unexecutable SQL generation. To fully exploit the structure, we propose a structure-to-SQL framework, which leverages the inherent structure information to improve the SQL generation of LLMs. Specifically, we introduce our Structure Guided SQL~(SGU-SQL) generation model. SGU-SQL first links user queries and databases in a structure-enhanced manner. It then decomposes complicated linked structures with grammar trees to guide the LLM to generate the SQL step by step. Extensive experiments on two benchmark datasets illustrate that SGU-SQL can outperform sixteen SQL generation baselines.

http://arxiv.org/abs/2402.13284v2

cs.DB, cs.AI, cs.CL

Automatic Evaluation for Mental Health Counseling using LLMs

Anqi Li, Yu Lu, Nirui Song, Shuai Zhang, Lizhi Ma, Zhenzhong Lan

High-quality psychological counseling is crucial for mental health worldwide, and timely evaluation is vital for ensuring its effectiveness. However, obtaining professional evaluation for each counseling session is expensive and challenging. Existing methods that rely on self or third-party manual reports to assess the quality of counseling suffer from subjective biases and limitations of time-consuming. To address above challenges, this paper proposes an innovative and efficient automatic approach using large language models (LLMs) to evaluate the working alliance in counseling conversations. We collected a comprehensive counseling dataset and conducted multiple third-party evaluations based on therapeutic relationship theory. Our LLM-based evaluation, combined with our guidelines, shows high agreement with human evaluations and provides valuable insights into counseling scripts. This highlights the potential of LLMs as supervisory tools for psychotherapists. By integrating LLMs into the evaluation process, our approach offers a cost-effective and dependable means of assessing counseling quality, enhancing overall effectiveness.

http://arxiv.org/abs/2402.11958v1

cs.CL

LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation

Keyang Xuan, Li Yi, Fan Yang, Ruochen Wu, Yi R. Fung, Heng Ji

The rise of multimodal misinformation on social platforms poses significant challenges for individuals and societies. Its increased credibility and broader impact compared to textual misinformation make detection complex, requiring robust reasoning across diverse media types and profound knowledge for accurate verification. The emergence of Large Vision Language Model (LVLM) offers a potential solution to this problem. Leveraging their proficiency in processing visual and textual information, LVLM demonstrates promising capabilities in recognizing complex information and exhibiting strong reasoning skills. In this paper, we first investigate the potential of LVLM on multimodal misinformation detection. We find that even though LVLM has a superior performance compared to LLMs, its profound reasoning may present limited power with a lack of evidence. Based on these observations, we propose LEMMA: LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation. LEMMA leverages LVLM intuition and reasoning capabilities while augmenting them with external knowledge to enhance the accuracy of misinformation detection. Our method improves the accuracy over the top baseline LVLM by 7% and 13% on Twitter and Fakeddit datasets respectively.

http://arxiv.org/abs/2402.11943v1

cs.CL

MRKE: The Multi-hop Reasoning Evaluation of LLMs by Knowledge Edition

Jian Wu, Linyi Yang, Manabu Okumura, Yue Zhang

Although Large Language Models (LLMs) have shown strong performance in Multi-hop Question Answering (MHQA) tasks, their real reasoning ability remains exploration. Current LLM QA evaluation benchmarks have shown limitations, including 1) data contamination, the evaluation data are potentially exposed to LLMs during the pretraining stage; and 2) ignoration of the reasoning chain evaluation. Thus we introduce an LLM MHQA evaluation benchmark, the first QA benchmark based on the new, unprecedented knowledge by editing the off-the-shelf HotpotQA dataset; Besides, we also annotate and evaluate the reasoning chain in the form of sub-questions and intermediate answers corresponding to the multi-hop questions. Specifically, based on the observation, 1) LLMs show a performance gap between the original HotpotQA and our edited data, deeming that current MHQA benchmarks have the potential risk of data contamination that hard to evaluate LLMs' performance objectively and scientifically; 2) LLMs only get a small percentage of the right reasoning chain, e.g. GPT-4 only gets 36.3\% right reasoning chain. We believe this new Multi-hop QA evaluation benchmark and novel evaluation methods will facilitate the development of trustworthy LLM evaluation on the MHQA task.

http://arxiv.org/abs/2402.11924v2

cs.CL

Enhancing Large Language Models for Text-to-Testcase Generation

Saranya Alagarsamy, Chakkrit Tantithamthavorn, Chetan Arora, Aldeida Aleti

Context: Test-driven development (TDD) is a widely employed software development practice that involves developing test cases based on requirements prior to writing the code. Although various methods for automated test case generation have been proposed, they are not specifically tailored for TDD, where requirements instead of code serve as input. Objective: In this paper, we introduce a text-to-testcase generation approach based on a large language model (GPT-3.5) that is fine-tuned on our curated dataset with an effective prompt design. Method: Our approach involves enhancing the capabilities of basic GPT-3.5 for text-to-testcase generation task that is fine-tuned on our curated dataset with an effective prompting design. We evaluated the effectiveness of our approach using a span of five large-scale open-source software projects. Results: Our approach generated 7k test cases for open source projects, achieving 78.5% syntactic correctness, 67.09% requirement alignment, and 61.7% code coverage, which substantially outperforms all other LLMs (basic GPT-3.5, Bloom, and CodeT5). In addition, our ablation study demonstrates the substantial performance improvement of the fine-tuning and prompting components of the GPT-3.5 model. Conclusions: These findings lead us to conclude that fine-tuning and prompting should be considered in the future when building a language model for the text-to-testcase generation task

http://arxiv.org/abs/2402.11910v1

cs.SE

Learning to Edit: Aligning LLMs with Knowledge Editing

Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang

Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods predominantly rely on memorizing the updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions. To this end, we propose a Learning to Edit (LTE) framework, focusing on teaching LLMs to apply updated knowledge into input questions, inspired by the philosophy of "Teach a man to fish." LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits while preserving out-of-scope information and linguistic proficiency; and (ii) the Inference Phase, which employs a retrieval-based mechanism for real-time and mass knowledge editing. By comparing our approach with seven advanced baselines across four popular knowledge editing benchmarks and two LLM architectures, we demonstrate LTE's superiority in knowledge editing performance, robustness in both batch and sequential editing, minimal interference on general tasks, and rapid editing speeds. The data and code are available at https://github.com/YJiangcm/LTE.

http://arxiv.org/abs/2402.11905v1

cs.CL

SoLA: Solver-Layer Adaption of LLM for Better Logic Reasoning

Yu Zhang, Hui-Ling Zhen, Zehua Pei, Yingzhao Lian, Lihao Yin, Mingxuan Yuan, Bei Yu

Considering the challenges faced by large language models (LLMs) on logical reasoning, prior efforts have sought to transform problem-solving through tool learning. While progress has been made on small-scale problems, solving industrial cases remains difficult due to their large scale and intricate expressions. In this paper, we propose a novel solver-layer adaptation (SoLA) method, where we introduce a solver as a new layer of the LLM to differentially guide solutions towards satisfiability. In SoLA, LLM aims to comprehend the search space described in natural language and identify local solutions of the highest quality, while the solver layer focuses solely on constraints not satisfied by the initial solution. Leveraging MaxSAT as a bridge, we define forward and backward transfer gradients, enabling the final model to converge to a satisfied solution or prove unsatisfiability. The backdoor theory ensures that SoLA can obtain accurate solutions within polynomial loops. We evaluate the performance of SoLA on various datasets and empirically demonstrate its consistent outperformance against existing symbolic solvers (including Z3 and Kissat) and tool-learning methods in terms of efficiency in large-scale problem-solving.

http://arxiv.org/abs/2402.11903v1

cs.CL, cs.AI

SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning

Zhihao Wen, Jie Zhang, Yuan Fang

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straight-forward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate over-smoothing and enhance performance. Extensive experiments on 22 benchmark datasets demonstrate that SIBO significantly enhances the performance of various strong baselines, achieving up to 15.7% and 23.5% improvement over existing PEFT methods on the arithmetic and commonsense reasoning tasks, respectively.

http://arxiv.org/abs/2402.11896v1

cs.CL

Have Seen Me Before? Automating Dataset Updates Towards Reliable and Timely Evaluation

Jiahao Ying, Yixin Cao, Bo Wang, Wei Tang, Yizhe Yang, Shuicheng Yan

Due to the expanding capabilities and pre-training data, Large Language Models (LLMs) are facing increasingly serious evaluation challenges. On one hand, the data leakage issue cause over-estimation on existing benchmarks. On the other hand, periodically curating datasets manually is costly. In this paper, we propose to automate dataset updates for reliable and timely evaluation. The basic idea is to generate unseen and high-quality testing samples based on existing ones to mitigate leakage issues. In specific, we propose two strategies with systematically verification. First, the mimicking strategy employs LLMs to create new samples resembling existing ones, to the maximum extent preserving the stylistic of the original dataset. Our experiments demonstrate its evaluation stability across multiple instantiations and its effectiveness in dealing with data leakage issues in most cases. Second, for the cases that mimicking dataset works poorly, we design an extending strategy that adjusts the difficulty of the generated samples according to varying cognitive levels. This not only makes our evaluation more systematic, but also, with a balanced difficulty, even discern model capabilities better at fine-grained levels.

http://arxiv.org/abs/2402.11894v2

cs.CL

FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation

Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang, Guido Zuccon

Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing uptake of Retrieval-Augmented Generation (RAG) pipelines, federated search can play a pivotal role in sourcing relevant information across heterogeneous data sources to generate informed responses. However, existing datasets, such as those developed in the past TREC FedWeb tracks, predate the RAG paradigm shift and lack representation of modern information retrieval challenges. To bridge this gap, we present FeB4RAG, a novel dataset specifically designed for federated search within RAG frameworks. This dataset, derived from 16 sub-collections of the widely used \beir benchmarking collection, includes 790 information requests (akin to conversational queries) tailored for chatbot applications, along with top results returned by each resource and associated LLM-derived relevance judgements. Additionally, to support the need for this collection, we demonstrate the impact on response generation of a high quality federated search system for RAG compared to a naive approach to federated search. We do so by comparing answers generated through the RAG pipeline through a qualitative side-by-side comparison. Our collection fosters and supports the development and evaluation of new federated search methods, especially in the context of RAG pipelines.

http://arxiv.org/abs/2402.11891v1

cs.IR, cs.CL

The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth

Shir Lissak, Nitay Calderon, Geva Shenkman, Yaakov Ophir, Eyal Fruchter, Anat Brunstein Klomek, Roi Reichart

Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation. Hindered by negative stigma, they often avoid seeking help and rely on online resources, which may provide incompatible information. Although access to a supportive environment and reliable information is invaluable, many queer youth worldwide have no access to such support. However, this could soon change due to the rapid adoption of Large Language Models (LLMs) such as ChatGPT. This paper aims to comprehensively explore the potential of LLMs to revolutionize emotional support for queers. To this end, we conduct a qualitative and quantitative analysis of LLM's interactions with queer-related content. To evaluate response quality, we develop a novel ten-question scale that is inspired by psychological standards and expert input. We apply this scale to score several LLMs and human comments to posts where queer youth seek advice and share experiences. We find that LLM responses are supportive and inclusive, outscoring humans. However, they tend to be generic, not empathetic enough, and lack personalization, resulting in nonreliable and potentially harmful advice. We discuss these challenges, demonstrate that a dedicated prompt can improve the performance, and propose a blueprint of an LLM-supporter that actively (but sensitively) seeks user context to provide personalized, empathetic, and reliable responses. Our annotated dataset is available for further research.

http://arxiv.org/abs/2402.11886v1

cs.CL, cs.AI

NOTE: Notable generation Of patient Text summaries through Efficient approach based on direct preference optimization

Imjin Ahn, Hansle Gwon, Young-Hak Kim, Tae Joon Jun, Sanghyun Park

The discharge summary is a one of critical documents in the patient journey, encompassing all events experienced during hospitalization, including multiple visits, medications, tests, surgery/procedures, and admissions/discharge. Providing a summary of the patient's progress is crucial, as it significantly influences future care and planning. Consequently, clinicians face the laborious and resource-intensive task of manually collecting, organizing, and combining all the necessary data for a discharge summary. Therefore, we propose "NOTE", which stands for "Notable generation Of patient Text summaries through an Efficient approach based on direct preference optimization". NOTE is based on Medical Information Mart for Intensive Care- III dataset and summarizes a single hospitalization of a patient. Patient events are sequentially combined and used to generate a discharge summary for each hospitalization. In the present circumstances, large language models' application programming interfaces (LLMs' APIs) are widely available, but importing and exporting medical data presents significant challenges due to privacy protection policies in healthcare institutions. Moreover, to ensure optimal performance, it is essential to implement a lightweight model for internal server or program within the hospital. Therefore, we utilized DPO and parameter efficient fine tuning (PEFT) techniques to apply a fine-tuning method that guarantees superior performance. To demonstrate the practical application of the developed NOTE, we provide a webpage-based demonstration software. In the future, we will aim to deploy the software available for actual use by clinicians in hospital. NOTE can be utilized to generate various summaries not only discharge summaries but also throughout a patient's journey, thereby alleviating the labor-intensive workload of clinicians and aiming for increased efficiency.

http://arxiv.org/abs/2402.11882v1

cs.CV, J.3

Modularized Networks for Few-shot Hateful Meme Detection

Rui Cao, Roy Ka-Wei Lee, Jing Jiang

In this paper, we address the challenge of detecting hateful memes in the low-resource setting where only a few labeled examples are available. Our approach leverages the compositionality of Low-rank adaptation (LoRA), a widely used parameter-efficient tuning technique. We commence by fine-tuning large language models (LLMs) with LoRA on selected tasks pertinent to hateful meme detection, thereby generating a suite of LoRA modules. These modules are capable of essential reasoning skills for hateful meme detection. We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance. The model's learnable parameters are directly proportional to the number of LoRA modules. This modularized network, underpinned by LLMs and augmented with LoRA modules, exhibits enhanced generalization in the context of hateful meme detection. Our evaluation spans three datasets designed for hateful meme detection in a few-shot learning context. The proposed method demonstrates superior performance to traditional in-context learning, which is also more computationally intensive during inference.We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance. The model's learnable parameters are directly proportional to the number of LoRA modules. This modularized network, underpinned by LLMs and augmented with LoRA modules, exhibits enhanced generalization in the context of hateful meme detection. Our evaluation spans three datasets designed for hateful meme detection in a few-shot learning context. The proposed method demonstrates superior performance to traditional in-context learning, which is also more computationally intensive during inference.

http://arxiv.org/abs/2402.11845v1

cs.CL, cs.CV

Where It Really Matters: Few-Shot Environmental Conservation Media Monitoring for Low-Resource Languages

Sameer Jain, Sedrick Scott Keh, Shova Chettri, Karun Dewan, Pablo Izquierdo, Johanna Prussman, Pooja Shreshtha, Cesar Suarez, Zheyuan Ryan Shi, Lei Li, Fei Fang

Environmental conservation organizations routinely monitor news content on conservation in protected areas to maintain situational awareness of developments that can have an environmental impact. Existing automated media monitoring systems require large amounts of data labeled by domain experts, which is only feasible at scale for high-resource languages like English. However, such tools are most needed in the global south where news of interest is mainly in local low-resource languages, and far fewer experts are available to annotate datasets sustainably. In this paper, we propose NewsSerow, a method to automatically recognize environmental conservation content in low-resource languages. NewsSerow is a pipeline of summarization, in-context few-shot classification, and self-reflection using large language models (LLMs). Using at most 10 demonstration example news articles in Nepali, NewsSerow significantly outperforms other few-shot methods and achieves comparable performance with models fully fine-tuned using thousands of examples. The World Wide Fund for Nature (WWF) has deployed NewsSerow for media monitoring in Nepal, significantly reducing their operational burden, and ensuring that AI tools for conservation actually reach the communities that need them the most. NewsSerow has also been deployed for countries with other languages like Colombia.

http://arxiv.org/abs/2402.11818v1

cs.CL, cs.AI, cs.CY

HU at SemEval-2024 Task 8A: Can Contrastive Learning Learn Embeddings to Detect Machine-Generated Text?

Shubhashis Roy Dipta, Sadat Shahriar

This paper describes our system developed for SemEval-2024 Task 8, ``Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection'' Machine-generated texts have been one of the main concerns due to the use of large language models (LLM) in fake text generation, phishing, cheating in exams, or even plagiarizing copyright materials. A lot of systems have been developed to detect machine-generated text. Nonetheless, the majority of these systems rely on the text-generating model. This limitation is impractical in real-world scenarios, as it's often impossible to know which specific model the user has used for text generation. In this work, we propose a $\textbf{single}$ model based on contrastive learning, which uses $\textbf{$\approx$40% of the baseline's parameters}$ (149M vs. 355M) but shows a comparable performance on the test dataset $(\textbf{21st out of 137 participants})$. Our key finding is that even without an ensemble of multiple models, a single base model can have comparable performance with the help of data augmentation and contrastive learning. Our code is publicly available at https://github.com/dipta007/SemEval24-Task8.

http://arxiv.org/abs/2402.11815v2

cs.CL, cs.AI, cs.LG

FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema

Junru Lu, Siyu An, Min Zhang, Yulan He, Di Yin, Xing Sun

In the quest to facilitate the deep intelligence of Large Language Models (LLMs) accessible in final-end user-bot interactions, the art of prompt crafting emerges as a critical yet complex task for the average user. Contrast to previous model-oriented yet instruction-agnostic Automatic Prompt Optimization methodologies, yielding polished results for predefined target models while suffering rapid degradation with out-of-box models, we present Free-form Instruction-oriented Prompt Optimization (FIPO). This approach is supported by our large-scale prompt preference dataset and employs a modular fine-tuning schema. The FIPO schema reimagines the optimization process into manageable modules, anchored by a meta prompt that dynamically adapts content. This allows for the flexible integration of the raw task instruction, the optional instruction response, and the optional ground truth to produce finely optimized task prompts. The FIPO preference dataset is meticulously constructed using the optimal and suboptimal LLMs, undergoing rigorous cross-verification by human experts and analytical models. Applying the insights from the data with Tulu2 models and fine-tuning strategies, we validate the efficacy of FIPO schema across five public benchmarks. Codes, data and scripts are here: https://github.com/LuJunru/FIPO_Project.

http://arxiv.org/abs/2402.11811v1

cs.CL

LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs

Kai Wang, Yuwei Xu, Zhiyong Wu, Siqiang Luo

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge of KG inductive reasoning is handling low-resource scenarios with scarcity in both textual and structural aspects. In this paper, we attempt to address this challenge with Large Language Models (LLMs). Particularly, we utilize the state-of-the-art LLMs to generate a graph-structural prompt to enhance the pre-trained Graph Neural Networks (GNNs), which brings us new methodological insights into the KG inductive reasoning methods, as well as high generalizability in practice. On the methodological side, we introduce a novel pretraining and prompting framework ProLINK, designed for low-resource inductive reasoning across arbitrary KGs without requiring additional training. On the practical side, we experimentally evaluate our approach on 36 low-resource KG datasets and find that ProLINK outperforms previous methods in three-shot, one-shot, and zero-shot reasoning tasks, exhibiting average performance improvements by 20%, 45%, and 147%, respectively. Furthermore, ProLINK demonstrates strong robustness for various LLM promptings as well as full-shot scenarios.

http://arxiv.org/abs/2402.11804v1

cs.AI, cs.CL, cs.SI

Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models

Zhou Yang, Zhaochun Ren, Wang Yufeng, Shizhong Peng, Haizhou Sun, Xiaofei Zhu, Xiangwen Liao

Empathetic response generation is increasingly significant in AI, necessitating nuanced emotional and cognitive understanding coupled with articulate response expression. Current large language models (LLMs) excel in response expression; however, they lack the ability to deeply understand emotional and cognitive nuances, particularly in pinpointing fine-grained emotions and their triggers. Conversely, small-scale empathetic models (SEMs) offer strength in fine-grained emotion detection and detailed emotion cause identification. To harness the complementary strengths of both LLMs and SEMs, we introduce a Hybrid Empathetic Framework (HEF). HEF regards SEMs as flexible plugins to improve LLM's nuanced emotional and cognitive understanding. Regarding emotional understanding, HEF implements a two-stage emotion prediction strategy, encouraging LLMs to prioritize primary emotions emphasized by SEMs, followed by other categories, substantially alleviates the difficulties for LLMs in fine-grained emotion detection. Regarding cognitive understanding, HEF employs an emotion cause perception strategy, prompting LLMs to focus on crucial emotion-eliciting words identified by SEMs, thus boosting LLMs' capabilities in identifying emotion causes. This collaborative approach enables LLMs to discern emotions more precisely and formulate empathetic responses. We validate HEF on the Empathetic-Dialogue dataset, and the findings indicate that our framework enhances the refined understanding of LLMs and their ability to convey empathetic responses.

http://arxiv.org/abs/2402.11801v1

cs.HC

What Evidence Do Language Models Find Convincing?

Alexander Wan, Eric Wallace, Dan Klein

Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as "is aspartame linked to cancer". To resolve these ambiguous queries, one must search through a large range of websites and consider "which, if any, of this evidence do I find convincing?". In this work, we study how LLMs answer this question. In particular, we construct ConflictingQA, a dataset that pairs controversial queries with a series of real-world evidence documents that contain different facts (e.g., quantitative results), argument styles (e.g., appeals to authority), and answers (Yes or No). We use this dataset to perform sensitivity and counterfactual analyses to explore which text features most affect LLM predictions. Overall, we find that current models rely heavily on the relevance of a website to the query, while largely ignoring stylistic features that humans find important such as whether a text contains scientific references or is written with a neutral tone. Taken together, these results highlight the importance of RAG corpus quality (e.g., the need to filter misinformation), and possibly even a shift in how LLMs are trained to better align with human judgements.

http://arxiv.org/abs/2402.11782v1

cs.CL, cs.LG

ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs

Pengrui Han, Rafal Kocielnik, Adhithya Saravanan, Roy Jiang, Or Sharir, Anima Anandkumar

Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is often challenging due to computational costs, data constraints, and potential degradation of multi-task language capabilities. This work introduces a novel approach utilizing ChatGPT to generate synthetic training data, aiming to enhance the debiasing of LLMs. We propose two strategies: Targeted Prompting, which provides effective debiasing for known biases but necessitates prior specification of bias in question; and General Prompting, which, while slightly less effective, offers debiasing across various categories. We leverage resource-efficient LLM debiasing using adapter tuning and compare the effectiveness of our synthetic data to existing debiasing datasets. Our results reveal that: (1) ChatGPT can efficiently produce high-quality training data for debiasing other LLMs; (2) data produced via our approach surpasses existing datasets in debiasing performance while also preserving internal knowledge of a pre-trained LLM; and (3) synthetic data exhibits generalizability across categories, effectively mitigating various biases, including intersectional ones. These findings underscore the potential of synthetic data in advancing the fairness of LLMs with minimal retraining cost.

http://arxiv.org/abs/2402.11764v1

cs.CL, cs.AI, cs.CY, 68T50, I.2.7; K.4.1

MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs

Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr

Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks. However, their tendency to produce inaccurate or misleading outputs poses a potential risk, particularly in high-stakes environments. Therefore, estimating the correctness of generative LLM outputs is an important task for enhanced reliability. Uncertainty Estimation (UE) in generative LLMs is an evolving domain, where SOTA probability-based methods commonly employ length-normalized scoring. In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods. MARS is a novel scoring function that considers the semantic contribution of each token in the generated sequence in the context of the question. We demonstrate that integrating MARS into UE methods results in a universal and significant improvement in UE performance. We conduct experiments using three distinct closed-book question-answering datasets across five popular pre-trained LLMs. Lastly, we validate the efficacy of MARS on a Medical QA dataset. Code can be found https://github.com/Ybakman/LLM_Uncertainity.

http://arxiv.org/abs/2402.11756v2

cs.CL, cs.LG

SPML: A DSL for Defending Language Models Against Prompt Attacks

Reshabh K Sharma, Vinayak Gupta, Dan Grossman

Large language models (LLMs) have profoundly transformed natural language applications, with a growing reliance on instruction-based definitions for designing chatbots. However, post-deployment the chatbot definitions are fixed and are vulnerable to attacks by malicious users, emphasizing the need to prevent unethical applications and financial losses. Existing studies explore user prompts' impact on LLM-based chatbots, yet practical methods to contain attacks on application-specific chatbots remain unexplored. This paper presents System Prompt Meta Language (SPML), a domain-specific language for refining prompts and monitoring the inputs to the LLM-based chatbots. SPML actively checks attack prompts, ensuring user inputs align with chatbot definitions to prevent malicious execution on the LLM backbone, optimizing costs. It also streamlines chatbot definition crafting with programming language capabilities, overcoming natural language design challenges. Additionally, we introduce a groundbreaking benchmark with 1.8k system prompts and 20k user inputs, offering the inaugural language and benchmark for chatbot definition evaluation. Experiments across datasets demonstrate SPML's proficiency in understanding attacker prompts, surpassing models like GPT-4, GPT-3.5, and LLAMA. Our data and codes are publicly available at: https://prompt-compiler.github.io/SPML/.

http://arxiv.org/abs/2402.11755v1

cs.LG, cs.CL, cs.CR, cs.PL

RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts

Mohammad Heydari Rad, Farhan Farsi, Shayan Bali, Romina Etezadi, Mehrnoush Shamsfard

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.

http://arxiv.org/abs/2402.14838v1

cs.CL, cs.AI, cs.LG

In-Context Learning Demonstration Selection via Influence Analysis

Vinay M. S., Minh-Hao Van, Xintao Wu

Large Language Models (LLMs) have demonstrated their In-Context Learning (ICL) capabilities which provides an opportunity to perform few shot learning without any gradient update. Despite its multiple benefits, ICL generalization performance is sensitive to the selected demonstrations. Selecting effective demonstrations for ICL is still an open research challenge. To address this challenge, we propose a demonstration selection method called InfICL which analyzes influences of training samples through influence functions. Identifying highly influential training samples can potentially aid in uplifting the ICL generalization performance. To limit the running cost of InfICL, we only employ the LLM to generate sample embeddings, and don't perform any costly fine tuning. We perform empirical study on multiple real-world datasets and show merits of our InfICL against state-of-the-art baselines.

http://arxiv.org/abs/2402.11750v1

cs.CL

Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic

Rishabh Bhardwaj, Do Duc Anh, Soujanya Poria

Aligned language models face a significant limitation as their fine-tuning often results in compromised safety. To tackle this, we propose a simple method RESTA that performs LLM safety realignment. RESTA stands for REstoring Safety through Task Arithmetic. At its core, it involves a simple arithmetic addition of a safety vector to the weights of the compromised model. We demonstrate the effectiveness of RESTA in both parameter-efficient and full fine-tuning, covering a wide range of downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math. We also showcase the generalizability of RESTA on three existing safety evaluation benchmarks and a multilingual benchmark dataset proposed as a part of this work, consisting of 550 harmful questions covering 11 categories, each with 5 sub-categories of harm. Overall, RESTA decreases the harmfulness of the compromised model from 18.6% to 5.1% and from 9.2% to 1.5% in parameter-efficient and full fine-tuning, respectively, while maintaining most of the model's performance on the task. We release the source codes at: https://github.com/declare-lab/resta.

http://arxiv.org/abs/2402.11746v1

cs.CL, cs.AI

Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges

Jiajia Wang, Jimmy X. Huang, Xinhui Tu, Junmei Wang, Angela J. Huang, Md Tahmid Rahman Laskar, Amran Bhuiyan

Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder representations from transformers (BERT) leads to a robust encoder for the transformer model that can understand the broader context and deliver state-of-the-art performance across various NLP tasks. This has inspired researchers and practitioners to apply BERT to practical problems, such as information retrieval (IR). A survey that focuses on a comprehensive analysis of prevalent approaches that apply pretrained transformer encoders like BERT to IR can thus be useful for academia and the industry. In light of this, we revisit a variety of BERT-based methods in this survey, cover a wide range of techniques of IR, and group them into six high-level categories: (i) handling long documents, (ii) integrating semantic information, (iii) balancing effectiveness and efficiency, (iv) predicting the weights of terms, (v) query expansion, and (vi) document expansion. We also provide links to resources, including datasets and toolkits, for BERT-based IR systems. A key highlight of our survey is the comparison between BERT's encoder-based models and the latest generative Large Language Models (LLMs), such as ChatGPT, which rely on decoders. Despite the popularity of LLMs, we find that for specific tasks, finely tuned BERT encoders still outperform, and at a lower deployment cost. Finally, we summarize the comprehensive outcomes of the survey and suggest directions for future research in the area.

http://arxiv.org/abs/2403.00784v1

cs.IR, cs.AI, cs.CL

Solving Data-centric Tasks using Large Language Models

Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu, Benjamin Zorn, José Cambronero, Andrew D. Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, Jack Williams

Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often interested in data-centric tasks, such as spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including the data. But how do we decide how much data and which data to include in the prompt? This paper makes two contributions towards answering this question. First, we create a dataset of real-world NL-to-code tasks manipulating tabular data, mined from StackOverflow posts. Second, we introduce a cluster-then-select prompting technique, which adds the most representative rows from the input data to the LLM prompt. Our experiments show that LLM performance is indeed sensitive to the amount of data passed in the prompt, and that for tasks with a lot of syntactic variation in the input table, our cluster-then-select technique outperforms a random selection baseline.

http://arxiv.org/abs/2402.11734v2

cs.PL, cs.AI, cs.SE

Large Language Models as Data Augmenters for Cold-Start Item Recommendation

Jianling Wang, Haokai Lu, James Caverlee, Ed Chi, Minmin Chen

The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant, conventional recommendation systems struggle to recommend cold-start items without historical interactions. To address this, we propose utilizing LLMs as data augmenters to bridge the knowledge gap on cold-start items during training. We employ LLMs to infer user preferences for cold-start items based on textual description of user historical behaviors and new item descriptions. The augmented training signals are then incorporated into learning the downstream recommendation models through an auxiliary pairwise loss. Through experiments on public Amazon datasets, we demonstrate that LLMs can effectively augment the training signals for cold-start items, leading to significant improvements in cold-start item recommendation for various recommendation models.

http://arxiv.org/abs/2402.11724v1

cs.IR

Modelling Political Coalition Negotiations Using LLM-based Agents

Farhad Moghimifar, Yuan-Fang Li, Robert Thomson, Gholamreza Haffari

Coalition negotiations are a cornerstone of parliamentary democracies, characterised by complex interactions and strategic communications among political parties. Despite its significance, the modelling of these negotiations has remained unexplored with the domain of Natural Language Processing (NLP), mostly due to lack of proper data. In this paper, we introduce coalition negotiations as a novel NLP task, and model it as a negotiation between large language model-based agents. We introduce a multilingual dataset, POLCA, comprising manifestos of European political parties and coalition agreements over a number of elections in these countries. This dataset addresses the challenge of the current scope limitations in political negotiation modelling by providing a diverse, real-world basis for simulation. Additionally, we propose a hierarchical Markov decision process designed to simulate the process of coalition negotiation between political parties and predict the outcomes. We evaluate the performance of state-of-the-art large language models (LLMs) as agents in handling coalition negotiations, offering insights into their capabilities and paving the way for future advancements in political modelling.

http://arxiv.org/abs/2402.11712v1

cs.CL

Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation

Kailun Jin, Chung-Yu Wang, Hung Viet Pham, Hadi Hemmati

Large language models (LLMs) have demonstrated notable proficiency in code generation, with numerous prior studies showing their promising capabilities in various development scenarios. However, these studies mainly provide evaluations in research settings, which leaves a significant gap in understanding how effectively LLMs can support developers in real-world. To address this, we conducted an empirical analysis of conversations in DevGPT, a dataset collected from developers' conversations with ChatGPT (captured with the Share Link feature on platforms such as GitHub). Our empirical findings indicate that the current practice of using LLM-generated code is typically limited to either demonstrating high-level concepts or providing examples in documentation, rather than to be used as production-ready code. These findings indicate that there is much future work needed to improve LLMs in code generation before they can be integral parts of modern software development.

http://arxiv.org/abs/2402.11702v2

cs.SE, cs.AI, cs.LG, I.2.2

Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning

Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang

Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data. Both challenges lead to issues such as poor generalizability, hallucination, and catastrophic forgetting. To address these challenges, we construct Vision-Flan, the most diverse publicly available visual instruction tuning dataset to date, comprising 187 diverse tasks and 1,664,261 instances sourced from academic datasets, and each task is accompanied by an expert-written instruction. In addition, we propose a two-stage instruction tuning framework, in which VLMs are firstly finetuned on Vision-Flan and further tuned on GPT-4 synthesized data. We find this two-stage tuning framework significantly outperforms the traditional single-stage visual instruction tuning framework and achieves the state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. Finally, we conduct in-depth analyses to understand visual instruction tuning and our findings reveal that: (1) GPT-4 synthesized data does not substantially enhance VLMs' capabilities but rather modulates the model's responses to human-preferred formats; (2) A minimal quantity (e.g., 1,000) of GPT-4 synthesized data can effectively align VLM responses with human-preference; (3) Visual instruction tuning mainly helps large-language models (LLMs) to understand visual features.

http://arxiv.org/abs/2402.11690v1

cs.CL, cs.CV

One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation

Tejpalsingh Siledar, Swaroop Nath, Sankara Sri Raghava Ravindra Muddu, Rupasai Rangaraju, Swaprava Nath, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Shekhar Singh, Muthusamy Chelliah, Nikesh Garera

Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human judgments. Recent studies suggest using Large Language Models (LLMs) as reference-free metrics for NLG evaluation, however, they remain unexplored for opinion summary evaluation. Moreover, limited opinion summary evaluation datasets inhibit progress. To address this, we release the SUMMEVAL-OP dataset covering 7 dimensions related to the evaluation of opinion summaries: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, and specificity. We investigate Op-I-Prompt a dimension-independent prompt, and Op-Prompts, a dimension-dependent set of prompts for opinion summary evaluation. Experiments indicate that Op-I-Prompt emerges as a good alternative for evaluating opinion summaries achieving an average Spearman correlation of 0.70 with humans, outperforming all previous approaches. To the best of our knowledge, we are the first to investigate LLMs as evaluators on both closed-source and open-source models in the opinion summarization domain.

http://arxiv.org/abs/2402.11683v1

cs.CL

Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs

Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, Suzan Verberne

Identifying user intents in information-seeking dialogs is crucial for a system to meet user's information needs. Intent prediction (IP) is challenging and demands sufficient dialogs with human-labeled intents for training. However, manually annotating intents is resource-intensive. While large language models (LLMs) have been shown to be effective in generating synthetic data, there is no study on using LLMs to generate intent-aware information-seeking dialogs. In this paper, we focus on leveraging LLMs for zero-shot generation of large-scale, open-domain, and intent-aware information-seeking dialogs. We propose SOLID, which has novel self-seeding and multi-intent self-instructing schemes. The former improves the generation quality by using the LLM's own knowledge scope to initiate dialog generation; the latter prompts the LLM to generate utterances sequentially, and mitigates the need for manual prompt design by asking the LLM to autonomously adapt its prompt instruction when generating complex multi-intent utterances. Furthermore, we propose SOLID-RL, which is further trained to generate a dialog in one step on the data generated by SOLID. We propose a length-based quality estimation mechanism to assign varying weights to SOLID-generated dialogs based on their quality during the training process of SOLID-RL. We use SOLID and SOLID-RL to generate more than 300k intent-aware dialogs, surpassing the size of existing datasets. Experiments show that IP methods trained on dialogs generated by SOLID and SOLID-RL achieve better IP quality than ones trained on human-generated dialogs.

http://arxiv.org/abs/2402.11633v1

cs.CL

Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Bias Detection

Valeria Pastorino, Jasivan A. Sivakumar, Nafise Sadat Moosavi

This work contributes to the expanding research on the applicability of LLMs in social sciences by examining the performance of GPT-3.5 Turbo, GPT-4, and Flan-T5 models in detecting framing bias in news headlines through zero-shot, few-shot, and explainable prompting methods. A key insight from our evaluation is the notable efficacy of explainable prompting in enhancing the reliability of these models, highlighting the importance of explainable settings for social science research on framing bias. GPT-4, in particular, demonstrated enhanced performance in few-shot scenarios when presented with a range of relevant, in-domain examples. FLAN-T5's poor performance indicates that smaller models may require additional task-specific fine-tuning for identifying framing bias detection. Our study also found that models, particularly GPT-4, often misinterpret emotional language as an indicator of framing bias, underscoring the challenge of distinguishing between reporting genuine emotional expression and intentionally use framing bias in news headlines. We further evaluated the models on two subsets of headlines where the presence or absence of framing bias was either clear-cut or more contested, with the results suggesting that these models' can be useful in flagging potential annotation inaccuracies within existing or new datasets. Finally, the study evaluates the models in real-world conditions ("in the wild"), moving beyond the initial dataset focused on U.S. Gun Violence, assessing the models' performance on framed headlines covering a broad range of topics.

http://arxiv.org/abs/2402.11621v2

cs.CL

Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?

Guijin Son, Sangwon Baek, Sangdae Nam, Ilgyun Jeong, Seungone Kim

Large language models (LLMs) are typically prompted to follow a single instruction per inference call. In this work, we analyze whether LLMs also hold the capability to handle multiple instructions simultaneously, denoted as Multi-Task Inference. For this purpose, we introduce the MTI Bench(Multi-Task Inference Benchmark), a comprehensive evaluation benchmark encompassing 5,000 instances across 25 tasks. Each task in the MTI Bench involves 2 to 3 sub-tasks. As expected, we first demonstrate that Multi-Task Inference reduces the total inference time by 1.46 times in average since it does not require multiple inference calls. Interestingly, contrary to the expectation that LLMs would perform better when tasks are divided, we find that state-of-the-art LLMs, such as Llama-2-Chat-70B and GPT-4, show up to 7.3% and 12.4% improved performance with Multi-Task Inference compared to Single-Task Inference on the MTI Bench. We release the MTI Bench dataset and our code at this link https://github.com/guijinSON/MTI-Bench.

http://arxiv.org/abs/2402.11597v1

cs.CL

KMMLU: Measuring Massive Multitask Language Understanding in Korean

Guijin Son, Hanwool Lee, Sungdong Kim, Seungone Kim, Niklas Muennighoff, Taekyoon Choi, Cheonbok Park, Kang Min Yoo, Stella Biderman

We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publically available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness.

http://arxiv.org/abs/2402.11548v1

cs.CL

ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation

Zihao Tang, Zheqi Lv, Shengyu Zhang, Fei Wu, Kun Kuang

The rapid advancement of Large Language Models (LLMs) has revolutionized various sectors by automating routine tasks, marking a step toward the realization of Artificial General Intelligence (AGI). However, they still struggle to accommodate the diverse and specific needs of users and simplify the utilization of AI models for the average user. In response, we propose ModelGPT, a novel framework designed to determine and generate AI models specifically tailored to the data or task descriptions provided by the user, leveraging the capabilities of LLMs. Given user requirements, ModelGPT is able to provide tailored models at most 270x faster than the previous paradigms (e.g. all-parameter or LoRA finetuning). Comprehensive experiments on NLP, CV, and Tabular datasets attest to the effectiveness of our framework in making AI models more accessible and user-friendly. Our code is available at https://github.com/IshiKura-a/ModelGPT.

http://arxiv.org/abs/2402.12408v1

cs.LG, cs.AI, cs.CL

Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning

Yang Zhao, Li Du, Xiao Ding, Kai Xiong, Zhouhao Sun, Jun Shi, Ting Liu, Bing Qin

Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance. However, the impact of each component of the pretraining corpus remains opaque. As a result, the organization of the pretraining corpus is still empirical and may deviate from the optimal. To address this issue, we systematically analyze the impact of 48 datasets from 5 major categories of pretraining data of LLMs and measure their impacts on LLMs using benchmarks about nine major categories of model capabilities. Our analyses provide empirical results about the contribution of multiple corpora on the performances of LLMs, along with their joint impact patterns, including complementary, orthogonal, and correlational relationships. We also identify a set of ``high-impact data'' such as Books that is significantly related to a set of model capabilities. These findings provide insights into the organization of data to support more efficient pretraining of LLMs.

http://arxiv.org/abs/2402.11537v2

cs.CL, cs.AI

Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models

Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long, Sudipta Kar, Abhinav Sethy, Joo-Kyung Kim, Dongyeop Kang

Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. However, most existing instruction datasets include only single instructions, and they struggle to follow complex instructions composed of multiple subtasks (Wang et al., 2023a). In this work, we propose a novel concept of compositional instructions called chain-of-instructions (CoI), where the output of one instruction becomes an input for the next like a chain. Unlike the conventional practice of solving single instruction tasks, our proposed method encourages a model to solve each subtask step by step until the final answer is reached. CoI-tuning (i.e., fine-tuning with CoI instructions) improves the model's ability to handle instructions composed of multiple subtasks. CoI-tuned models also outperformed baseline models on multilingual summarization, demonstrating the generalizability of CoI models on unseen composite downstream tasks.

http://arxiv.org/abs/2402.11532v1

cs.CL

Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network

Lin Chen, Fengli Xu, Nian Li, Zhenyu Han, Meng Wang, Yong Li, Pan Hui

Heterogeneous information networks (HIN) have gained increasing popularity for being able to capture complex relations between nodes of diverse types. Meta-structure was proposed to identify important patterns of relations on HIN, which has been proven effective for extracting rich semantic information and facilitating graph neural networks to learn expressive representations. However, hand-crafted meta-structures pose challenges for scaling up, which draws wide research attention for developing automatic meta-structure search algorithms. Previous efforts concentrate on searching for meta-structures with good empirical prediction performance, overlooking explainability. Thus, they often produce meta-structures prone to overfitting and incomprehensible to humans. To address this, we draw inspiration from the emergent reasoning abilities of large language models (LLMs). We propose a novel REasoning meta-STRUCTure search (ReStruct) framework that integrates LLM reasoning into the evolutionary procedure. ReStruct uses a grammar translator to encode meta-structures into natural language sentences, and leverages the reasoning power of LLMs to evaluate semantically feasible meta-structures. ReStruct also employs performance-oriented evolutionary operations. These two competing forces jointly optimize for semantic explainability and empirical performance of meta-structures. We also design a differential LLM explainer that can produce natural language explanations for the discovered meta-structures, and refine the explanation by reasoning through the search history. Experiments on five datasets demonstrate ReStruct achieve SOTA performance in node classification and link recommendation tasks. Additionally, a survey study involving 73 graduate students shows that the meta-structures and natural language explanations generated by ReStruct are substantially more comprehensible.

http://arxiv.org/abs/2402.11518v1

cs.LG, cs.CL

LEIA: Facilitating Cross-Lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation

Ikuya Yamada, Ryokan Ri

Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling. We assess LEIA on diverse question answering datasets using 7B-parameter LLMs, demonstrating significant performance gains across various non-English languages. The source code is available at https://github.com/studio-ousia/leia.

http://arxiv.org/abs/2402.11485v1

cs.CL, cs.AI, cs.LG

DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics

YiQiu Guo, Yuchen Yang, Ya Zhang, Yu Wang, Yanfeng Wang

Structured data offers a sophisticated mechanism for the organization of information. Existing methodologies for the text-serialization of structured data in the context of large language models fail to adequately address the heterogeneity inherent in key-value structured data. These methods are not ideal and frequently result in larger input sizes and poor adaptability to input changes. In this paper, we introduce DictLLM, an innovative framework designed to improve the modeling of key-value structured data, like medical laboratory reports, for generating medical diagnoses. DictLLM integrates three key components: (1) group positional encoding to maintain permutation invariance, (2) hierarchical attention bias to capture the inherent bias in structured data, and (3) an optimal transport alignment layer that aligns the embedding generated by the dictionary encoder with the LLM, thereby producing a sequence of fixed-length virtual tokens. We carry out experiments using various LLM models on a comprehensive real-world medical laboratory report dataset for automatic diagnosis generation, our findings illustrate that DictLLM significantly outperforms established baseline methods and few-shot GPT-4 implementations in terms of both Rouge-L and Knowledge F1 scores. Furthermore, our evaluation of the framework's scalability and robustness, through a series of experiments, underscores its exceptional capability in accurately modeling the complex key-value data structure of medical dictionary data.

http://arxiv.org/abs/2402.11481v1

cs.CL

In-Context Example Ordering Guided by Label Distributions

Zhichao Xu, Daniel Cohen, Bei Wang, Vivek Srikumar

By allowing models to predict without task-specific training, in-context learning (ICL) with pretrained LLMs has enormous potential in NLP. However, a number of problems persist in ICL. In particular, its performance is sensitive to the choice and order of in-context examples. Given the same set of in-context examples with different orderings, model performance may vary between near random to near state-of-the-art. In this work, we formulate in-context example ordering as an optimization problem. We examine three problem settings that differ in the assumptions they make about what is known about the task. Inspired by the idea of learning from label proportions, we propose two principles for in-context example ordering guided by model's probability predictions. We apply our proposed principles to thirteen text classification datasets and nine different autoregressive LLMs with 700M to 13B parameters. We demonstrate that our approach outperforms the baselines by improving the classification accuracy, reducing model miscalibration, and also by selecting better in-context examples.

http://arxiv.org/abs/2402.11447v1

cs.CL

Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation

Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei, Xuanjing Huang

This paper presents a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models (LLMs), aiming for a more accurate assessment of their capabilities and limitations. We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence that dynamically extend existing benchmarks. Towards a more scalable, robust and fine-grained evaluation, we implement six reframing operations to construct evolving instances testing LLMs against diverse queries, data noise and probing their problem-solving sub-abilities. With this framework, we extend benchmark datasets of four tasks. Experimental results show a general performance decline in most LLMs against their original results. This decline under our scalable and robust evaluations, alongside our fine-grained evaluation, more accurately reflect models' capabilities. Besides, our framework widens performance discrepancies both between different models and within the same model across various tasks, facilitating more informed model selection for specific tasks (Code and data are available at https://github.com/NanshineLoong/Self-Evolving-Benchmark).

http://arxiv.org/abs/2402.11443v1

cs.CL

Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei, Tat-Seng Chua, Yueting Zhuang, Siliang Tang

Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video modality, which are termed Video-LLMs. However, existing Video-LLMs can only capture the coarse-grained semantics and are unable to effectively handle tasks related to comprehension or localization of specific video segments. In light of these challenges, we propose Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks. To support the training of Momentor, we design an automatic data generation engine to construct Moment-10M, a large-scale video instruction dataset with segment-level instruction data. We train Momentor on Moment-10M, enabling it to perform segment-level reasoning and localization. Zero-shot evaluations on several tasks demonstrate that Momentor excels in fine-grained temporally grounded comprehension and localization.

http://arxiv.org/abs/2402.11435v1

cs.CV

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

Yinghui Li, Shang Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu

Recently, Large Language Models (LLMs) have been widely studied by researchers for their roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese Grammatical Error Correction (CGEC) aims to correct all potential grammatical errors in the input sentences. Previous studies have shown that LLMs' performance as correctors on CGEC remains unsatisfactory due to its challenging task focus. To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC. Considering the rich grammatical knowledge stored in LLMs and their powerful semantic understanding capabilities, we utilize LLMs as explainers to provide explanation information for the CGEC small models during error correction to enhance performance. We also use LLMs as evaluators to bring more reasonable CGEC evaluations, thus alleviating the troubles caused by the subjectivity of the CGEC task. In particular, our work is also an active exploration of how LLMs and small models better collaborate in downstream tasks. Extensive experiments and detailed analyses on widely used datasets verify the effectiveness of our thinking intuition and the proposed methods.

http://arxiv.org/abs/2402.11420v1

cs.CL

Multi-dimensional Evaluation of Empathetic Dialog Responses

Zhichao Xu, Jiepu Jiang

Empathy is critical for effective and satisfactory conversational communication. Prior efforts to measure conversational empathy mostly focus on expressed communicative intents -- that is, the way empathy is expressed. Yet, these works ignore the fact that conversation is also a collaboration involving both speakers and listeners. In contrast, we propose a multi-dimensional empathy evaluation framework to measure both expressed intents from the speaker's perspective and perceived empathy from the listener's perspective. We apply our proposed framework to analyze our internal customer-service dialogue. We find the two dimensions (expressed intent types and perceived empathy) are inter-connected, and perceived empathy has a high correlation with dialogue satisfaction levels. To reduce the annotation cost, we explore different options to automatically measure conversational empathy: prompting LLMs and training language model-based classifiers. Our experiments show that prompting methods with even popular models like GPT-4 and Flan family models perform relatively poorly on both public and our internal datasets. In contrast, instruction-finetuned classifiers based on Flan-T5 family models outperform prior works and competitive baselines. We conduct a detailed ablation study to give more insights into instruction finetuning method's strong performance.

http://arxiv.org/abs/2402.11409v2

cs.CL

Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection

Min Zhang, Jianfeng He, Taoran Ji, Chang-Tien Lu

The fairness and trustworthiness of Large Language Models (LLMs) are receiving increasing attention. Implicit hate speech, which employs indirect language to convey hateful intentions, occupies a significant portion of practice. However, the extent to which LLMs effectively address this issue remains insufficiently examined. This paper delves into the capability of LLMs to detect implicit hate speech (Classification Task) and express confidence in their responses (Calibration Task). Our evaluation meticulously considers various prompt patterns and mainstream uncertainty estimation methods. Our findings highlight that LLMs exhibit two extremes: (1) LLMs display excessive sensitivity towards groups or topics that may cause fairness issues, resulting in misclassifying benign statements as hate speech. (2) LLMs' confidence scores for each method excessively concentrate on a fixed range, remaining unchanged regardless of the dataset's complexity. Consequently, the calibration performance is heavily reliant on primary classification accuracy. These discoveries unveil new limitations of LLMs, underscoring the need for caution when optimizing models to ensure they do not veer towards extremes. This serves as a reminder to carefully consider sensitivity and confidence in the pursuit of model fairness.

http://arxiv.org/abs/2402.11406v2

cs.CL

CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulness

Jiayi Liu, Tinghan Yang, Jennifer Neville

Large language models (LLMs) have become pivotal in recent research. However, during the inference process, LLMs still require substantial resources. In this paper, we propose CliqueParcel, a method designed to improve the efficiency of LLMs via prompt batching. Existing strategies to optimize inference efficiency often compromise on output quality, leading to a discounted output problem. This issue might result in reduced accuracy or outputs that are less detailed. CliqueParcel is our answer to this challenge. While ensuring accuracy and minimizing deviations from the original outputs (i.e., faithfulness), our method significantly improves efficiency during inference. To lay the groundwork, we first redefine efficiency measurements by excluding the reduction in running time due to shorter lengths. Then, we provide a comprehensive trade-off between efficiency and faithfulness to clarify the nature of the 'discounted output' problem. Within the CliqueParcel framework, we suggest multiple batching sub-methods and discuss the specific scenarios in which they can be applied. During evaluation, CliqueParcel is tested on eight widely recognized datasets, which can be classified into three types: reading comprehension, open-source question-answering, and reasoning. Our experiments explore the performance of CliqueParcel, including efficiency, faithfulness, and the trade-off between them. This work provides novel insights into inference efficiency and demonstrates promising performance.

http://arxiv.org/abs/2402.14833v1

cs.CL, cs.AI, cs.LG

EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries

Jiateng Liu, Pengfei Yu, Yuji Zhang, Sha Li, Zixuan Zhang, Heng Ji

The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating. However, current KE approaches, which typically operate on (subject, relation, object) triples, ignore the contextual information and the relation among different knowledge. Such editing methods could thus encounter an uncertain editing boundary, leaving a lot of relevant knowledge in ambiguity: Queries that could be answered pre-edit cannot be reliably answered afterward. In this work, we analyze this issue by introducing a theoretical framework for KE that highlights an overlooked set of knowledge that remains unchanged and aids in knowledge deduction during editing, which we name as the deduction anchor. We further address this issue by proposing a novel task of event-based knowledge editing that pairs facts with event descriptions. This task manifests not only a closer simulation of real-world editing scenarios but also a more logically sound setting, implicitly defining the deduction anchor to address the issue of indeterminate editing boundaries. We empirically demonstrate the superiority of event-based editing over the existing setting on resolving uncertainty in edited models, and curate a new benchmark dataset EvEdit derived from the CounterFact dataset. Moreover, while we observe that the event-based setting is significantly challenging for existing approaches, we propose a novel approach Self-Edit that showcases stronger performance, achieving 55.6% consistency improvement while maintaining the naturalness of generation.

http://arxiv.org/abs/2402.11324v1

cs.CL

Dissecting Human and LLM Preferences

Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang, Hai Zhao, Pengfei Liu

As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation. Yet, these preferences merely reflect broad tendencies, resulting in less explainable and controllable models with potential safety risks. In this work, we dissect the preferences of human and 32 different LLMs to understand their quantitative composition, using annotations from real-world user-model conversations for a fine-grained, scenario-wise analysis. We find that humans are less sensitive to errors, favor responses that support their stances, and show clear dislike when models admit their limits. On the contrary, advanced LLMs like GPT-4-Turbo emphasize correctness, clarity, and harmlessness more. Additionally, LLMs of similar sizes tend to exhibit similar preferences, regardless of their training methods, and fine-tuning for alignment does not significantly alter the preferences of pretrained-only LLMs. Finally, we show that preference-based evaluation can be intentionally manipulated. In both training-free and training-based settings, aligning a model with the preferences of judges boosts scores, while injecting the least preferred properties lowers them. This results in notable score shifts: up to 0.59 on MT-Bench (1-10 scale) and 31.94 on AlpacaEval 2.0 (0-100 scale), highlighting the significant impact of this strategic adaptation. Interactive Demo: https://huggingface.co/spaces/GAIR/Preference-Dissection-Visualization Dataset: https://huggingface.co/datasets/GAIR/preference-dissection Code: https://github.com/GAIR-NLP/Preference-Dissection

http://arxiv.org/abs/2402.11296v1

cs.CL, cs.AI

Puzzle Solving using Reasoning of Large Language Models: A Survey

Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou

Exploring the capabilities of Large Language Models (LLMs) in puzzle solving unveils critical insights into their potential and challenges in AI, marking a significant step towards understanding their applicability in complex reasoning tasks. This survey leverages a unique taxonomy -- dividing puzzles into rule-based and rule-less categories -- to critically assess LLMs through various methodologies, including prompting techniques, neuro-symbolic approaches, and fine-tuning. Through a critical review of relevant datasets and benchmarks, we assess LLMs' performance, identifying significant challenges in complex puzzle scenarios. Our findings highlight the disparity between LLM capabilities and human-like reasoning, particularly in those requiring advanced logical inference. The survey underscores the necessity for novel strategies and richer datasets to advance LLMs' puzzle-solving proficiency and contribute to AI's logical reasoning and creative problem-solving advancements.

http://arxiv.org/abs/2402.11291v2

cs.CL, cs.AI

Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models

Pei Wang, Yejie Wang, Muxi Diao, Keqing He, Guanting Dong, Weiran Xu

In the deployment of large language models (LLMs), accurate confidence estimation is critical for assessing the credibility of model predictions. However, existing methods often fail to overcome the issue of overconfidence on incorrect answers. In this work, we focus on improving the confidence estimation of large language models. Considering the fragility of self-awareness in language models, we introduce a Multi-Perspective Consistency (MPC) method. We leverage complementary insights from different perspectives within models (MPC-Internal) and across different models (MPC-Across) to mitigate the issue of overconfidence arising from a singular viewpoint. The experimental results on eight publicly available datasets show that our MPC achieves state-of-the-art performance. Further analyses indicate that MPC can mitigate the problem of overconfidence and is effectively scalable to other models.

http://arxiv.org/abs/2402.11279v1

cs.CL, cs.AI

MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning

Shu Yang, Muhammad Asif Ali, Cheng-Long Wang, Lijie Hu, Di Wang

Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge. In this paper, we propose MoRAL, i.e., Mixture-of-Experts augmented Low-Rank Adaptation for Lifelong Learning. MoRAL combines the multi-tasking abilities of MoE with the fine-tuning abilities of LoRA for effective life-long learning of LLMs. In contrast to the conventional approaches that use factual triplets as inputs MoRAL relies on simple question-answer pairs, which is a more practical and effective strategy for robust and efficient learning. Owing to new data settings, we introduce a new evaluation benchmark namely: Life Long Learning of LLM (5L-bench) encompassing a newly curated dataset of question-answer pairs, and a set of evaluation metrics for rigorous evaluation of MoRAL in open-book and closed-book settings. Experimental evaluation shows (i) LLMs learn fast in open-book settings with up to 30.15% improvement in "RA" for Phi-2-2.7B compared to closed-book (for models fine-tuned with MoRAL); (ii) MoRAL shows higher performance improvement for models with a greater number of parameters; (iii) MoRAL is robust to catastrophic forgetting offering better knowledge retention compared to baselines.

http://arxiv.org/abs/2402.11260v1

cs.CL, cs.AI

C-ICL: Contrastive In-context Learning for Information Extraction

Ying Mo, Jian Yang, Jiahao Liu, Shun Zhang, Jingang Wang, Zhoujun Li

Recently, there has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE). Although researchers are exploring the use of few-shot information extraction through in-context learning with LLMs, they tend to focus only on using correct or positive examples for demonstration, neglecting the potential value of incorporating incorrect or negative examples into the learning process. In this paper, we present c-ICL, a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. This approach enhances the ability of LLMs to extract entities and relations by utilizing prompts that incorporate not only the positive samples but also the reasoning behind them. This method allows for the identification and correction of potential interface errors. Specifically, our proposed method taps into the inherent contextual information and valuable information in hard negative samples and the nearest positive neighbors to the test and then applies the in-context learning demonstrations based on LLMs. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods, delivering substantial enhancements in performance across a broad spectrum of related tasks. These improvements are noteworthy, showcasing the versatility of our approach in miscellaneous scenarios.

http://arxiv.org/abs/2402.11254v1

cs.CL