GenAI4ELab/papercli-papers huggingface dataset:
Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor
Authors: Sangwon Yu; Changmin Lee; Hojin Lee; Sungroh Yoon
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Controlled text generation, aiming to ensure that language models produce text containing only the desired domain or corpus attributes, is immensely crucial in the practical application of language models. Existing methods, however, are inapplicable to black-box models or suffer a significant trade-off between control and fluency in text generation. This paper introduces the Score-based Progressive Editor (ScoPE), a novel approach designed to overcome these issues. ScoPE modifies the context at the token level during the generation process of a backbone language model. This modification guides the subsequent text to naturally include the target attributes. To facilitate this process, ScoPE employs a training objective that maximizes a target score, comprehensively considering both control and fluency. Experimental results on diverse controlled generation tasks demonstrate that ScoPE can effectively regulate the attributes of the generated text while effectively utilizing the capability of the backbone large language models.
LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP
Authors: Danlu Chen; Freda Shi; Aditi Agarwal; Jacobo Myerston; Taylor Berg-Kirkpatrick
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Standard natural language processing (NLP) pipelines operate on symbolic representations of language, which typically consist of sequences of discrete tokens. However, creating an analogous representation for ancient logographic writing systems is an extremely labor-intensive process that requires expert knowledge. At present, a large portion of logographic data persists in a purely visual form due to the absence of transcription—this issue poses a bottleneck for researchers seeking to apply NLP toolkits to study ancient logographic languages: most of the relevant data are images of writing. This paper investigates whether direct processing of visual representations of language offers a potential solution. We introduce LogogramNLP, the first benchmark enabling NLP analysis of ancient logographic languages, featuring both transcribed and visual datasetsfor four writing systems along with annotations for tasks like classification, translation, and parsing. Our experiments compare systems thatemploy recent visual and text encoding strategies as backbones. The results demonstrate that visual representations outperform textual representations for some investigated tasks, suggesting that visual processing pipelines may unlock a large amount of cultural heritage data of logographic languages for NLP-based analyses. Data and code are available at https: //logogramNLP.github.io/.
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Authors: Ming Li; Yong Zhang; Shwai He; Zhitao Li; Hongyu Zhao; Jianzong Wang; Ning Cheng; Tianyi Zhou
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also leads to extra cost and computation due to the involvement of LLMs in this process. To reduce the filtering cost, we study Superfiltering: Can we use a smaller and weaker model to select data for finetuning a larger and stronger model? Despite the performance gap between weak and strong language models, we find their highly consistent capability to perceive instruction difficulty and data selection results. This enables us to use a much smaller and more efficient model to filter the instruction data used to train a larger language model. Not only does it largely speed up the data filtering, but the filtered-data-finetuned LLM achieves even better performance on standard benchmarks. Extensive experiments validate the efficacy and efficiency of our approach.
Confabulation: The Surprising Value of Large Language Model Hallucinations
Authors: Peiqi Sui; Eamon Duede; Sophie Wu; Richard So
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: This paper presents a systematic defense of large language model (LLM) hallucinations or ‘confabulations’ as a potential resource instead of a categorically negative pitfall. The standard view is that confabulations are inherently problematic and AI research should eliminate this flaw. In this paper, we argue and empirically demonstrate that measurable semantic characteristics of LLM confabulations mirror a human propensity to utilize increased narrativity as a cognitive resource for sense-making and communication. In other words, it has potential value. Specifically, we analyze popular hallucination benchmarks and reveal that hallucinated outputs display increased levels of narrativity and semantic coherence relative to veridical outputs. This finding reveals a tension in our usually dismissive understandings of confabulation. It suggests, counter-intuitively, that the tendency for LLMs to confabulate may be intimately associated with a positive capacity for coherent narrative-text generation.
IAPT: Instance-Aware Prompt Tuning for Large Language Models
Authors: Wei Zhu; Aaron Tian; Congrui Yin; Yuan Ni; Xiaoling Wang; Guotong Xie
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it has a clear drawback: many soft tokens must be inserted into the input sequences to guarantee downstream performance. As a result, soft prompt tuning is less considered than Low-rank adaptation (LoRA) in the large language modeling (LLM) era. In this work, we propose a novel prompt tuning method, Instruction-Aware Prompt Tuning (IAPT), that requires only four soft tokens. First, we install a parameter-efficient soft prompt generator at each Transformer layer to generate idiosyncratic soft prompts for each input instruction. The generated soft prompts can be seen as a semantic summary of the input instructions and can effectively guide the output generation. Second, the soft prompt generators are modules with a bottleneck architecture consisting of a self-attention pooling operation, two linear projections, and an activation function. Pilot experiments show that prompt generators at different Transformer layers require different activation functions. Thus, we propose to learn the idiosyncratic activation functions for prompt generators automatically with the help of rational functions. We have conducted experiments on various tasks, and the experimental results demonstrate that (a) our IAPT method can outperform the recent baselines with comparable tunable parameters. (b) Our IAPT method is more efficient than LoRA under the single-backbone multi-tenant setting.
DeVAn: Dense Video Annotation for Video-Language Models
Authors: Tingkai Liu; Yunzhe Tao; Haogeng Liu; Qihang Fang; Ding Zhou; Huaibo Huang; Ran He; Hongxia Yang
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: We present a novel human annotated dataset for evaluating the ability for visual-language models to generate both short and long descriptions for real-world video clips, termed DeVAn (Dense Video Annotation). The dataset contains 8.5K YouTube video clips of 20-60 seconds in duration and covers a wide range of topics and interests. Each video clip is independently annotated by 5 human annotators, producing both captions (1 sentence) and summaries (3-10 sentences). Given any video selected from the dataset and its corresponding ASR information, we evaluate visual-language models on either caption or summary generation that is grounded in both the visual and auditory content of the video. Additionally, models are also evaluated on caption- and summary-based retrieval tasks, where the summary-based retrieval task requires the identification of a target video given excerpts of a given summary. Given the novel nature of the paragraph-length video summarization task, we compared different existing evaluation metrics and their alignment with human preferences and found that model-based evaluation metrics provide more semantically-oriented and human-aligned evaluation. Finally, we benchmarked a wide range of current video-language models on DeVAn, and we aim for DeVAn to serve as a useful evaluation set in the age of large language models and complex multi-modal tasks. Code is available at https://github.com/TK-21st/DeVAn.
How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
Authors: Yi Zeng; Hongpeng Lin; Jingwen Zhang; Diyi Yang; Ruoxi Jia; Weiyan Shi
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Most traditional AI safety research views models as machines and centers on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. Observing this, we shift the perspective, by treating LLMs as human-like communicators to examine the interplay between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak risk across all risk categories: PAP consistently achieves an attack success rate of over 92% on Llama-2-7b-Chat, GPT-3.5, and GPT-4 in 10 trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP, find a significant gap in existing defenses, and advocate for more fundamental solutions for AI safety.
The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models
Authors: Adithya Bhaskar; Dan Friedman; Danqi Chen
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Prior work has found that pretrained language models (LMs) fine-tuned with different random seeds can achieve similar in-domain performance but generalize differently on tests of syntactic generalization. In this work, we show that, even within a single model, we can find multiple subnetworks that perform similarly in-domain, but generalize vastly differently. To better understand these phenomena, we investigate if they can be understood in terms of “competing subnetworks”: the model initially represents a variety of distinct algorithms, corresponding to different subnetworks, and generalization occurs when it ultimately converges to one. This explanation has been used to account for generalization in simple algorithmic tasks (“grokking”). Instead of finding competing subnetworks, we find that all subnetworks—whether they generalize or not—share a set of attention heads, which we refer to as the _heuristic core_. Further analysis suggests that these attention heads emerge early in training and compute shallow, non-generalizing features. The model learns to generalize by incorporating additional attention heads, which depend on the outputs of the “heuristic” heads to compute higher-level features. Overall, our results offer a more detailed picture of the mechanisms for syntactic generalization in pre-trained LMs.
Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models
Authors: Lei Li; Yuqi Wang; Runxin Xu; Peiyi Wang; Xiachong Feng; Lingpeng Kong; Qi Liu
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Large vision-language models (LVLMs) excel across diverse tasks involving concrete images from natural scenes. However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of training datasets in scientific domains.To fill this gap, we introduce Multimodal ArXiv, consisting of ArXivCap and ArXivQA, for enhancing LVLMs scientific comprehension.ArXivCap is a figure-caption dataset comprising 6.4M images and 3.9M captions, sourced from 572K ArXiv papers spanning various scientific domains.Drawing from ArXivCap, we introduce ArXivQA, a question-answering dataset generated by prompting GPT-4V based on scientific figures. ArXivQA greatly enhances open-sourced LVLMs’ mathematical reasoning capabilities, achieving a 10.4% absolute accuracy gain on a multimodal mathematical reasoning benchmark.Furthermore, employing ArXivCap, we devise four vision-to-text tasks for benchmarking LVLMs.Evaluation results with state-of-the-art LVLMs underscore their struggle with the nuanced semantics of academic figures, while domain-specific training yields substantial performance gains.Our error analysis uncovers misinterpretations of visual context, recognition errors, and the production of overly simplified captions by current LVLMs, shedding light on future improvements.
L-Eval: Instituting Standardized Evaluation for Long Context Language Models
Authors: Chenxin An; Shansan Gong; Ming Zhong; Xingjian Zhao; Mukai Li; Jun Zhang; Lingpeng Kong; Xipeng Qiu
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Recently, there has been growing interest in long-context scaling of large language models (LLMs). To facilitate research in this field, we propose L-Eval to institute a more standardized evaluation for Long-Context Language Models (LCLMs) addressing two key aspects: dataset construction and evaluation metrics. On the one hand, we build a new evaluation suite containing 20 sub-tasks, 508 long documents, and more than 2,000 human-labeled query-response pairs including diverse task types, domains, and input length (3k~200k tokens). On the other hand, we investigate the effectiveness of evaluation metrics for LCLMs and we show that Length-instruction-enhanced (LIE) evaluation and LLM judges can better correlate with human judgments. We conducted a comprehensive study of 4 popular commercial LLMs and 12 open-source counterparts using the L-Eval benchmark. Our empirical findings offer useful insights into the study of LCLMs and lay the groundwork for the development of a more principled evaluation of these models.
DIALECTBENCH: An NLP Benchmark for Dialects, Varieties, and Closely-Related Languages
Authors: Fahim Faisal; Orevaoghene Ahia; Aarohi Srivastava; Kabir Ahuja; David Chiang; Yulia Tsvetkov; Antonios Anastasopoulos
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is language variation in the form of non-standard dialects or language varieties (hereafter, varieties). Most NLP benchmarks are limited to standard language varieties. To fill this gap, we propose DIALECTBENCH, the first-ever large-scale benchmark for NLP on varieties, which aggregates an extensive set of task-varied varieties datasets (10 text-level tasks covering 281 varieties). This allows for a comprehensive evaluation of NLP system performance on different varieties. We provide substantial proof of performance disparities between standard and non-standard language varieties, and we also identify language clusters with larger performance divergence across tasks.We believe DIALECTBENCH provides a comprehensive view of the current state of NLP for varieties and one step towards advancing it further.
Causal-Guided Active Learning for Debiasing Large Language Models
Authors: Zhouhao Sun; Li Du; Xiao Ding; Yixuan Ma; Yang Zhao; Kaitao Qiu; Ting Liu; Bing Qin
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Although achieving promising performance, recent analyses show that current generative large language models (LLMs) may still capture dataset biases and utilize them for generation, leading to poor generalizability and harmfulness of LLMs. However, due to the diversity of dataset biases and the over-optimization problem, previous prior-knowledge-based debiasing methods and fine-tuning-based debiasing methods may not be suitable for current LLMs.To address this issue, we explore combining active learning with the causal mechanisms and propose a casual-guided active learning (CAL) framework, which utilizes LLMs itself to automatically and autonomously identify informative biased samples and induce the bias patterns. Then a cost-effective and efficient in-context learning based method is employed to prevent LLMs from utilizing dataset biases during generation.Experimental results show that CAL can effectively recognize typical biased instances and induce various bias patterns for debiasing LLMs.
PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents
Authors: Qisen Yang; Zekun Wang; Honghui Chen; Shenzhi Wang; Yifan Pu; Xin Gao; Wenhao Huang; Shiji Song; Gao Huang
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews, often face challenges with engagement and accessibility. While game-based and LLM-based tools have been explored to improve user interest and automate assessment, they struggle to balance engagement with generalizability. In this work, we propose PsychoGAT (Psychological Game AgenTs) to achieve a generic gamification of psychological assessment. The main insight is that powerful LLMs can function both as adept psychologists and innovative game designers. By incorporating LLM agents into designated roles and carefully managing their interactions, PsychoGAT can transform any standardized scales into personalized and engaging interactive fiction games. To validate the proposed method, we conduct psychometric evaluations to assess its effectiveness and employ human evaluators to examine the generated content across various psychological constructs, including depression, cognitive distortions, and personality traits. Results demonstrate that PsychoGAT serves as an effective assessment tool, achieving statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity. Moreover, human evaluations confirm PsychoGAT’s enhancements in content coherence, interactivity, interest, immersion, and satisfaction.
Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient
Authors: Mingxin Li; Richong Zhang; Zhijie Nie
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Sentence Representation Learning (SRL) is a crucial task in Natural Language Processing (NLP), where contrastive Self-Supervised Learning (SSL) is currently a mainstream approach. However, the reasons behind its remarkable effectiveness remain unclear. Specifically, many studies have investigated the similarities between contrastive and non-contrastive SSL from a theoretical perspective. Such similarities can be verified in classification tasks, where the two approaches achieve comparable performance. But in ranking tasks (i.e., Semantic Textual Similarity (STS) in SRL), contrastive SSL significantly outperforms non-contrastive SSL. Therefore, two questions arise: First, *what commonalities enable various contrastive losses to achieve superior performance in STS?* Second, *how can we make non-contrastive SSL also effective in STS?* To address these questions, we start from the perspective of gradients and discover that four effective contrastive losses can be integrated into a unified paradigm, which depends on three components: the **Gradient Dissipation**, the **Weight**, and the **Ratio**. Then, we conduct an in-depth analysis of the roles these components play in optimization and experimentally demonstrate their significance for model performance. Finally, by adjusting these components, we enable non-contrastive SSL to achieve outstanding performance in STS.
Emergent Word Order Universals from Cognitively-Motivated Language Models
Authors: Tatsuki Kuribayashi; Ryo Ueda; Ryo Yoshida; Yohei Oseki; Ted Briscoe; Timothy Baldwin
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The world’s languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions. Explaining the source of such biases is a key goal of linguistics.We study word-order universals through a computational simulation with language models (LMs).Our experiments show that typologically-typical word orders tend to have lower perplexity estimated by LMs with cognitively plausible biases: syntactic biases, specific parsing strategies, and memory limitations. This suggests that the interplay of cognitive biases and predictability (perplexity) can explain many aspects of word-order universals.It also showcases the advantage of cognitively-motivated LMs, typically employed in cognitive modeling, in the simulation of language universals.
Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View
Authors: Jintian Zhang; Xin Xu; Ningyu Zhang; Ruibo Liu; Bryan Hooi; Shumin Deng
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: As Natural Language Processing (NLP) systems are increasingly employed in intricate social environments, a pressing query emerges: *Can these NLP systems mirror human-esque collaborative intelligence, in a multi-agent society consisting of multiple large language models (LLMs)?* This paper probes the collaboration mechanisms among contemporary NLP systems by melding practical experiments with theoretical insights. We fabricate four unique ‘societies’ comprised of LLM agents, where each agent is characterized by a specific ‘trait’ (easy-going or overconfident) and engages in collaboration with a distinct ‘thinking pattern’ (debate or reflection). Through evaluating these multi-agent societies on three benchmark datasets, we discern that certain collaborative strategies not only outshine previous top-tier approaches but also optimize efficiency (using fewer API tokens). Moreover, our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring foundational social psychology theories. In conclusion, we integrate insights from social psychology to contextualize the collaboration of LLM agents, inspiring further investigations into the collaboration mechanism for LLMs. We commit to sharing our code and datasets, hoping to catalyze further research in this promising avenue.