mtybilly/Instruct2DS huggingface dataset:
Mitigating Hallucination in Fictional Character Role-Play
Authors: Zhouhang Xie, Byungkyu Kang, Prarit Lamba, Xiang Gao, Julian McAuley Nafis Sadeq
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Role-playing has wide-ranging applications in customer support, embodied agents, and computational social science. The influence of parametric world knowledge of large language models (LLMs) often causes role-playing characters to act out of character and to hallucinate about things outside the scope of their knowledge. In this work, we focus on the evaluation and mitigation of hallucination in fictional character role-play. We introduce a dataset with over 2,000 characters and 72,000 interviews, including 18,000 adversarial questions. We propose RoleFact, a role-playing method that mitigates hallucination by modulating the influence of parametric knowledge using a pre-calibrated confidence threshold. Experiments show that the proposed method improves the factual precision of generated responses by 18% for adversarial questions with a 44% reduction in temporal hallucination for time-sensitive interviews. The code and the dataset are available at r̆lhttps://github.com/NafisSadeq/rolefact.git.
I`m sure you`re a real scholar yourself: Exploring Ironic Content Generation by Large Language Models
Authors: Silvia Casola, Soda Marem Lo, Valerio Basile, Alessandro Mazzei Pier Felice Balestrucci
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Generating ironic content is challenging: it requires a nuanced understanding of context and implicit references and balancing seriousness and playfulness. Moreover, irony is highly subjective and can depend on various factors, such as social, cultural, or generational aspects. This paper explores whether Large Language Models (LLMs) can learn to generate ironic responses to social media posts. To do so, we fine-tune two models to generate ironic and non-ironic content and deeply analyze their outputs' linguistic characteristics, their connection to the original post, and their similarity to the human-written replies. We also conduct a large-scale human evaluation of the outputs. Additionally, we investigate whether LLMs can learn a form of irony tied to a generational perspective, with mixed results.
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
Authors: Yanda Li, Meng Fang, Ling Chen Wanqi Yang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and understanding of time-evolving facts to generate accurate answers. However, current large language models still have limited sensitivity to temporal information and their inadequate temporal reasoning capabilities. In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. Experimental results on four TSQA datasets demonstrate that our framework significantly outperforms existing LLMs in TSQA tasks, marking a step forward in bridging the performance gap between machine and human temporal understanding and reasoning.
Minimal Yet Big Impact: How AI Agent Back-channeling Enhances Conversational Engagement through Conversation Persistence and Context Richness
Authors: Saim Shin, Gahgene Gweon Jin Yea Jang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: The increasing use of AI agents in conversational services, such as counseling, highlights the importance of back-channeling (BC) as an active listening strategy to enhance conversational engagement. BC improves conversational engagement by providing timely acknowledgments and encouraging the speaker to talk. This study investigates the effect of BC provided by an AI agent on conversational engagement, offering insights for future AI conversational service design. We conducted an experiment with 55 participants, divided into Todak_BC and Todak_NoBC groups based on the presence or absence of the BC feature in Todak, a conversational agent. Each participant engaged in nine sessions with predetermined subjects and questions. We collected and analyzed approximately 6 hours and 30 minutes of conversation logs to evaluate conversational engagement using both quantitative (conversation persistence, including conversation duration and number of utterances) and qualitative metrics (context richness, including self-disclosure and topic diversity). The findings reveal significantly higher conversational engagement in the Todak_BC group compared to the Todak_NoBC group across all metrics (p\ensuremath<0.05). Additionally, the impact of BC varies across sessions, suggesting that conversation characteristics such as question type and topic sensitivity can influence BC effectiveness.
Large Language Models for Propaganda Span Annotation
Authors: Fatema Ahmad, Firoj Alam Maram Hasanain
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: The use of propagandistic techniques in online content has increased in recent years aiming to manipulate online audiences. Fine-grained propaganda detection and extraction of textual spans where propaganda techniques are used, are essential for more informed content consumption. Automatic systems targeting the task over lower resourced languages are limited, usually obstructed by lack of large scale training datasets. Our study investigates whether Large Language Models (LLMs), such as GPT-4, can effectively extract propagandistic spans. We further study the potential of employing the model to collect more cost-effective annotations. Finally, we examine the effectiveness of labels provided by GPT-4 in training smaller language models for the task. The experiments are performed over a large-scale in-house manually annotated dataset. The results suggest that providing more annotation context to GPT-4 within prompts improves its performance compared to human annotators. Moreover, when serving as an expert annotator (consolidator), the model provides labels that have higher agreement with expert annotators, and lead to specialized models that achieve state-of-the-art over an unseen Arabic testing set. Finally, our work is the first to show the potential of utilizing LLMs to develop annotated datasets for propagandistic spans detection task prompting it with annotations from human annotators with limited expertise. All scripts and annotations will be shared with the community.
Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles
Authors: Tianxing He, Xiaojun Wan Xiao Pu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Prompt compression condenses contexts while maintaining their informativeness for different usage scenarios. It not only shortens the inference time and reduces computational costs during the usage of large language models, but also lowers expenses when using closed-source models. In a preliminary study, we discover that when instructing language models to compress prompts, different compression styles (e.g., extractive or abstractive) impact performance of compressed prompts on downstream tasks. Building on this insight, we propose Style-Compress, a lightweight framework that adapts a smaller language model to compress prompts for a larger model on a new task without additional training. Our approach iteratively generates and selects effective compressed prompts as task-specific demonstrations through style variation and in-context learning, enabling smaller models to act as efficient compressors with task-specific examples. Style-Compress outperforms two baseline compression models in four tasks: original prompt reconstruction, text summarization, multi-hop QA, and CoT reasoning. In addition, with only 10 samples and 100 queries for adaptation, prompts compressed by Style-Compress achieve performance on par with or better than original prompts at a compression ratio of 0.25 or 0.5.
POSIX: A Prompt Sensitivity Index For Large Language Models
Authors: H S V N S Kowndinya Renduchintala, Sumit Bhatia, Tanmoy Chakraborty Anwoy Chatterjee
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Despite their remarkable capabilities, Large Language Models (LLMs) are found to be surprisingly sensitive to minor variations in prompts, often generating significantly divergent outputs in response to minor variations in the prompts, such as spelling errors, alteration of wording or the prompt template. However, while assessing the quality of an LLM, the focus often tends to be solely on its performance on downstream tasks, while very little to no attention is paid to prompt sensitivity. To fill this gap, we propose POSIX -- a novel PrOmpt Sensitivity IndeX as a reliable measure of prompt sensitivity, thereby offering a more comprehensive evaluation of LLM performance. The key idea behind POSIX is to capture the relative change in loglikelihood of a given response upon replacing the corresponding prompt with a different intent-preserving prompt. We provide thorough empirical evidence demonstrating the efficacy of POSIX in capturing prompt sensitivity and subsequently use it to measure and thereby compare prompt sensitivity of various open source LLMs. We find that merely increasing the parameter count or instruction tuning does not necessarily reduce prompt sensitivity whereas adding some few-shot exemplars, even just one, almost always leads to significant decrease in prompt sensitivity. We also find that alterations to prompt template lead to the highest sensitivity in the case of MCQ type tasks, whereas paraphrasing results in the highest sensitivity in open-ended generation tasks. The code for reproducing our results is open-sourced at https://github.com/kowndinya-renduchintala/POSIX.
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data
Authors: Xintao Wang, Rui Xu, Xinfeng Yuan, Jiaqing Liang, Yanghua Xiao, Deqing Yang Yiting Ran
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia. While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs). In this paper, we propose to enhance RPLMs via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations.
Local and Global Decoding in Text Generation
Authors: Thomas Hofmann, Ezhilmathi Krishnasamy, Tiago Pimentel Daniel Gareev
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Text generation, a component in applications such as dialogue systems, relies heavily on decoding algorithms that sample strings from a language model distribution. Traditional methods like top-$k$ and top-$π$ decoding locally normalise the model`s output, which can significantly distort the original distribution. In this paper, we investigate the effects of such distortions by introducing globally-normalised versions of these decoding methods. Further, we propose an independent Metropolis-Hastings (IMH) algorithm to approximate sampling from these globally-normalised distributions without explicitly computing them. Our empirical analyses compare the performance of local and global decoding across two algorithms (top-$k$ and top-$π$) with various hyperparameters, using the Pythia language models. Results show that in most configuration, global decoding performs worse than the local decoding versions of the same algorithms, despite preserving the distribution`s integrity. Our results thus suggest that distortion might be an important feature of local decoding algorithms.
LEGOBench: Scientific Leaderboard Generation Benchmark
Authors: Shoaib Alam, Husain Malwat, Mayank Singh Shruti Singh
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: The ever-increasing volume of paper submissions makes it difficult to stay informed about the latest state-of-the-art research. To address this challenge, we introduce LEGOBench, a benchmark for evaluating systems that generate scientific leaderboards. LEGOBench is curated from 22 years of preprint submission data on arXiv and more than 11k machine learning leaderboards on the PapersWithCode portal. We present a language model-based and four graph-based leaderboard generation task configuration. We evaluate popular encoder-only scientific language models as well as decoder-only large language models across these task configurations. State-of-the-art models showcase significant performance gaps in automatic leaderboard generation on LEGOBench. The code is available on GitHub and the dataset is hosted on OSF.
H-LegalKI: A Hierarchical Legal Knowledge Integration Framework for Legal Community Question Answering
Authors: Ziyu Guan, Jie Zhao, Wei Zhao, Jiaqi Yang Yue Jiang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Legal question answering (LQA) aims to bridge the gap between the limited availability of legal professionals and the high demand for legal assistance. Traditional LQA approaches typically either select the optimal answers from an answer set or extract answers from law texts. However, they often struggle to provide relevant answers to complex, real-world questions due to the rigidity of predetermined answers. Although recent advancements in legal large language models have shown some potential in enhancing answer relevance, they fail to address the multiple user-specific circumstances, i.e., factual details in questions. To address these issues, we (1) construct the first publicly available legal community question-answering (LegalCQA) dataset; and (2) propose a Hierarchical Legal Knowledge Integration (H-LegalKI) framework. LegalCQA is collected from two widely used legal forums for developing user-centered LQA models. For H-LegalKI, we design a legal knowledge retriever that gathers comprehensive legal knowledge based on both entire questions and individual sentences. And an answer generation model is designed to understand question- and sentence-level factual details and integrate corresponding legal knowledge in a hierarchical way. Additionally, we design a de-redundancy module to remove redundant legal knowledge. Experiments on LegalCQA demonstrate the superiority of our framework over competitive baselines.
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy
Authors: Zhenlin Su, Mo Yu, Jin Xu, Jinho D. Choi, Jie Zhou, Fei Liu Liyan Xu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stronger Natural Language Inference (NLI) models, we propose an orthogonal aspect that underscores the importance of incorporating task-specific taxonomy into the inference. To this end, we consolidate key error types of inconsistent facts in summaries, and incorporate them to facilitate both the zero-shot and supervised paradigms of LLMs. Extensive experiments on ten datasets of five distinct domains suggest that, zero-shot LLM inference could benefit from the explicit solution space depicted by the error type taxonomy, and achieves state-of-the-art performance overall, surpassing specialized non-LLM baselines, as well as recent LLM baselines. We further distill models that fuse the taxonomy into parameters through our designed prompt completions and supervised training strategies, efficiently substituting state-of-the-art zero-shot inference with much larger LLMs.
Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning
Authors: Rex Ying, Leandros Tassiulas Aosong Feng
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: As the demand for processing extended textual data grows, the ability to handle long-range dependencies and maintain computational efficiency is more critical than ever. One of the key issues for long-sequence modeling using attention-based model is the mismatch between the limited-range modeling power of full attention and the long-range token dependency in the input sequence. In this work, we propose to scale up the attention receptive field by tensorizing long input sequences into compact tensor representations followed by attention on each transformed dimension. The resulting Tensorized Attention can be adopted as efficient transformer backbones to extend input context length with improved memory and time efficiency. We show that the proposed attention tensorization encodes token dependencies as a multi-hop attention process, and is equivalent to Kronecker decomposition of full attention. Extensive experiments show that tensorized attention can be used to adapt pretrained LLMs with improved efficiency. Notably, using customized Triton kernels, tensorization enables Llama-8B training under 32,768 context length and can steadily extrapolate to 128k length during inference with 11 times speedup (compared to full attention with FlashAttention-2).
BanglaTLit: A Benchmark Dataset for Back-Transliteration of Romanized Bangla
Authors: Fariha Tanjim Shifat, Fabiha Haider, Deeparghya Dutta Barua, MD Sakib Ul Rahman Sourove, Md Farhan Ishmam, Md Farhad Alam Bhuiyan Md Fahim
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Low-resource languages like Bangla are severely limited by the lack of datasets. Romanized Bangla texts are ubiquitous on the internet, offering a rich source of data for Bangla NLP tasks and extending the available data sources. However, due to the informal nature of romanized text, they often lack the structure and consistency needed to provide insights. We address these challenges by proposing: (1) BanglaTLit, the large-scale Bangla transliteration dataset consisting of 42.7k samples, (2) BanglaTLit-PT, a pre-training corpus on romanized Bangla with 245.7k samples, (3) encoders further-pretrained on BanglaTLit-PT achieving state-of-the-art performance in several romanized Bangla classification tasks, and (4) multiple back-transliteration baseline methods, including a novel encoder-decoder architecture using further pre-trained encoders. Our results show the potential of automated Bangla back-transliteration in utilizing the untapped sources of romanized Bangla to enrich this language. The code and datasets are publicly available: https://github.com/farhanishmam/BanglaTLit.
Finding the Optimal Byte-Pair Encoding Merge Operations for Neural Machine Translation in a Low-Resource Setting
Authors: Nelson Marcos Kristine Mae M. Adlaon
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: This paper investigates the impact of different Byte Pair Encoding (BPE) configurations, specifically, merge operations on neural machine translation (NMT) performance for the Filipino-Cebuano language pair across various text domains. Results demonstrate that smaller BPE configurations, notably 2k, 5k, and 8k consistently yield higher BLEU scores, indicating improved translation quality through finer tokenization granularity. Conversely, larger BPE configurations and the absence of BPE result in lower BLEU scores, suggesting a decline in translation quality due to coarser tokenization. Additionally, these findings help us understand how the size of the model and how finely we break down words affect the quality of translations. This knowledge will be useful for improving translation systems, especially for languages that don`t have many parallel texts available for training.
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs
Authors: Yuxia Wang, Minghan Wang, Preslav Nakov Muhammad Arslan Manzoor
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives. However, modeling empathy using NLP approaches remains challenging due to its deep interconnection with human interaction dynamics. Previous approaches, which involve fine-tuning language models (LMs) on human-annotated empathic datasets, have had limited success. In our pursuit of improving empathy understanding in LMs, we propose several strategies, including contrastive learning with masked LMs and supervised fine-tuning with large language models. While these methods show improvements over previous methods, the overall results remain unsatisfactory. To better understand this trend, we performed an analysis which reveals a low agreement among annotators. This lack of consensus hinders training and highlights the subjective nature of the task. We also explore the cultural impact on annotations. To study this, we meticulously collected story pairs in Urdu language and find that subjectivity in interpreting empathy among annotators appears to be independent of cultural background. Our systematic exploration of LMs' understanding of empathy reveals substantial opportunities for further investigation in both task formulation and modeling.
EU DisinfoTest: a Benchmark for Evaluating Language Models' Ability to Detect Disinformation Narratives
Authors: Arkadiusz Modzelewski, Kinga Skorupska, Jahna Otterbacher, Adam Wierzbicki Witold Sosnowski
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: As narratives shape public opinion and influence societal actions, distinguishing between truthful and misleading narratives has become a significant challenge. To address this, we introduce the EU DisinfoTest, a novel benchmark designed to evaluate the efficacy of Language Models in identifying disinformation narratives. Developed through a Human-in-the-Loop methodology and grounded in research from EU DisinfoLab, the EU DisinfoTest comprises more than 1,300 narratives. Our benchmark includes persuasive elements under Logos, Pathos, and Ethos rhetorical dimensions. We assessed state-of-the-art LLMs, including the newly released GPT-4o, on their capability to perform zero-shot classification of disinformation narratives versus credible narratives. Our findings reveal that LLMs tend to regard narratives with authoritative appeals as trustworthy, while those with emotional appeals are frequently incorrectly classified as disinformative. These findings highlight the challenges LLMs face in nuanced content interpretation and suggest the need for tailored adjustments in LLM training to better handle diverse narrative structures.
From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression
Authors: Sunkyung Lee, Minjin Choi, Jun Park, Jongwuk Lee Eunseong Choi
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) have achieved significant performance gains using advanced prompting techniques over various tasks. However, the increasing length of prompts leads to high computational costs and often obscures crucial information. Prompt compression has been proposed to alleviate these issues, but it faces challenges in (i) capturing the global context and (ii) training the compressor effectively. To tackle these challenges, we introduce a novel prompt compression method, namely Reading To Compressing (R2C), utilizing the Fusion-in-Decoder (FiD) architecture to identify the important information in the prompt. Specifically, the cross-attention scores of the FiD are used to discern essential chunks and sentences from the prompt. R2C effectively captures the global context without compromising semantic consistency while detouring the necessity of pseudo-labels for training the compressor. Empirical results show that R2C retains key contexts, enhancing the LLM performance by 6% in out-of-domain evaluations while reducing the prompt length by 80%.
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis
Authors: Yuming Lin, Lihua He, You Li, Liang Chang, Ya Zhou Xinyu Feng
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Multimodal Sentiment Analysis (MSA) utilizes multimodal data to infer the users' sentiment. Previous methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modality may become dominant. In this paper, we propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) for multimodal sentiment analysis. KuDA uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality. In addition, with the obtained multimodal representation, the model can further highlight the contribution of dominant modality through the correlation evaluation loss. Extensive experiments on four MSA benchmark datasets indicate that KuDA achieves state-of-the-art performance and is able to adapt to different scenarios of dominant modality.
LexMatcher: Dictionary-centric Data Curation for LLM-based Machine Translation
Authors: Jiali Zeng, Yafu Li, Fandong Meng, Yue Zhang Yongjing Yin
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from traditional neural machine translation. However, the area of data collection for instruction fine-tuning in machine translation remains relatively underexplored. In this paper, we present LexMatcher, a simple yet effective method for data curation,the design of which is driven by the coverage of senses found in bilingual dictionaries. The construction process comprises data retrieval from an existing corpus and data augmentation that supplements the infrequent senses of polysemous words. Utilizing LLaMA2 as our base model, our method outperforms the established baselines on the WMT2022 test sets and also exhibits remarkable performance in tasks related to word sense disambiguation and specialized terminology translation. Our method is also applicable to other pre-trained models, and complements the method of continual pre-training using monolingual data, demonstrating the effectiveness of LexMatcher in enhancing LLM-based machine translation.
SARCAT: Generative Span-Act Guided Response Generation using Copy-enhanced Target Augmentation
Authors: Hyeongjun Choi, Beomseok Hong, Youngsub Han, Byoung-Ki Jeon, Seung-Hoon Na Jeong-Doo Lee
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: In this paper, we present a novel extension to improve the document grounded response generation, by proposing the Generative Span Act Guided Response Generation using Copy enhanced Target Augmentation (SARCAT) that consists of two major components as follows: 1) Copy-enhanced target-side input augmentation is an extended data augmentation to deal with the exposure bias problem by additionally incorporating the copy mechanism on top of the target-side augmentation (Xie et al., 2021). 2) Span-act guided response generation, which first predicts grounding spans and dialogue acts before generating a response. Experiment results on validation set in MultiDoc2Dial show that the proposed SARSAT leads to improvement over strong baselines on both seen and unseen settings and achieves the start-of the-art performance, even with the base reader using the pretrained T5-base model.
Does Context Help Mitigate Gender Bias in Neural Machine Translation?
Authors: Thierry Etchegoyhen Harritxu Gete
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Neural Machine Translation models tend to perpetuate gender bias present in their training data distribution. Context-aware models have been previously suggested as a means to mitigate this type of bias. In this work, we examine this claim by analysing in detail the translation of stereotypical professions in English to German, and translation with non-informative context in Basque to Spanish. Our results show that, although context-aware models can significantly enhance translation accuracy for feminine terms, they can still maintain or even amplify gender bias. These results highlight the need for more fine-grained approaches to bias mitigation in Neural Machine Translation.
A Critical Look at Meta-evaluating Summarisation Evaluation Metrics
Authors: Sarvnaz Karimi, Biaoyan Fang Xiang Dai
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarisation systems efficiently. Estimating the effectiveness of an automatic evaluation metric, termed meta-evaluation, is a critically important research question. In this position paper, we review recent meta-evaluation practices for summarisation evaluation metrics and find that (1) evaluation metrics are primarily meta-evaluated on datasets consisting of examples from news summarisation datasets, and (2) there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. We argue that the time is ripe to build more diverse benchmarks that enable the development of more robust evaluation metrics and analyze the generalization ability of existing evaluation metrics. In addition, we call for research focusing on user-centric quality dimensions that consider the generated summary`s communicative goal and the role of summarisation in the workflow.
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study
Authors: Paul Youssef, Christin Seifert, Jörg Schlötterer Van Bach Nguyen
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: As NLP models become more complex, understanding their decisions becomes more crucial. Counterfactuals (CFs), where minimal changes to inputs flip a model`s prediction, offer a way to explain these models. While Large Language Models (LLMs) have shown remarkable performance in NLP tasks, their efficacy in generating high-quality CFs remains uncertain. This work fills this gap by investigating how well LLMs generate CFs for three tasks. We conduct a comprehensive comparison of several common LLMs, and evaluate their CFs, assessing both intrinsic metrics, and the impact of these CFs on data augmentation. Moreover, we analyze differences between human and LLM-generated CFs, providing insights for future research directions. Our results show that LLMs generate fluent CFs, but struggle to keep the induced changes minimal. Generating CFs for Sentiment Analysis (SA) is less challenging than NLI and Hate Speech (HS) where LLMs show weaknesses in generating CFs that flip the original label. This also reflects on the data augmentation performance, where we observe a large gap between augmenting with human and LLM CFs. Furthermore, we evaluate LLMs' ability to assess CFs in a mislabelled data setting, and show that they have a strong bias towards agreeing with the provided labels. GPT4 is more robust against this bias, but it shows strong preference to its own generations. Our analysis suggests that safety training is causing GPT4 to prefer its generations, since these generations do not contain harmful content. Our findings reveal several limitations and point to potential future work directions.
Unlocking Black-Box Prompt Tuning Efficiency via Zeroth-Order Optimization
Authors: Congliang Chen, Tian Ding, Ziniu Li, Ruoyu Sun Heshen Zhan
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Prompt optimization emerges as an important technique for adapting Large Language Models (LLMs) to specific tasks. Unfortunately, LLM proprietors often limit access to models' internal weights, confining users to inference API services. This restriction poses a significant challenge for prompt optimization, as conventional optimization-based algorithms rely heavily on gradient information, which is unavailable via inference APIs. Addressing this challenge, this paper presents the Zeroth-Order Tuning (ZOT) approach, which enables efficient prompt tuning solely via inference APIs. ZOT adopts the zeroth-order optimization framework, utilizing finite differences to approximate gradient information. We further incorporate ZOT with gradient clipping and momentum techniques to enhance the tuning effectiveness. Experimental results show that ZOT outperforms existing black-box prompt tuning methods in terms of both task-specific performance and convergence speed. Furthermore, we provide a theoretical explanation for the unexpectedly strong performance of zeroth-order methods on LLM prompt tuning. By introducing the concept of effective dimension, we establish a strong connection between the inherently low effective dimension of prompt spaces and the superior convergence speed of zeroth-order methods. Our code is available at https://github.com/ZhanHeshen/ZOT.
Unveiling Narrative Reasoning Limits of Large Language Models with Trope in Movie Synopses
Authors: Ya-Ching Hsu, Xudong Lin, Xiang-Qian Shi, Yulei Niu, Han-Yuan Hsu, Hung-yi Lee, Winston H. Hsu Hung-Ting Su
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) equipped with chain-of-thoughts (CoT) prompting have shown significant multi-step reasoning capabilities in factual content like mathematics, commonsense, and logic. However, their performance in narrative reasoning, which demands greater abstraction capabilities, remains unexplored. This study utilizes tropes in movie synopses to assess the abstract reasoning abilities of state-of-the-art LLMs and uncovers their low performance. We introduce a trope-wise querying approach to address these challenges and boost the F1 score by 11.8 points. Moreover, while prior studies suggest that CoT enhances multi-step reasoning, this study shows CoT can cause hallucinations in narrative content, reducing GPT-4`s performance. We also introduce an Adversarial Injection method to embed trope-related text tokens into movie synopses without explicit tropes, revealing CoT`s heightened sensitivity to such injections. Our comprehensive analysis provides insights for future research directions.
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
Authors: Yupeng Zhang, Bingning Wang, Xin Zhao, Ji-Rong Wen, Weipeng Chen Jie Chen
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs). Studies have shown that synthetic data can effectively improve the performance of LLMs on downstream benchmarks. However, despite its potential benefits, our analysis suggests that there may be inherent flaws in synthetic data. The uniform format of synthetic data can lead to pattern overfitting and cause significant shifts in the output distribution, thereby reducing the model`s instruction-following capabilities. Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. The empirical results demonstrate the effectiveness of our approach, which can reverse the instruction-following issues caused by pattern overfitting without compromising performance on benchmarks at relatively low cost. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
CED: Comparing Embedding Differences for Detecting Out-of-Distribution and Hallucinated Text
Authors: Keon-Hee Park, Hoyoon Byun, Jeyoon Yeom, Jihee Kim, Gyeong-Moon Park, Kyungwoo Song Hakyung Lee
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety and robustness of models deployed in real-world scenarios. While most studies on OOD detection focus on fine-tuned models trained on in-distribution (ID) data, detecting OOD in pre-trained models is also important due to computational limitations and the widespread use of open-source pre-trained models. However, in the same domain shift setting, the OOD detection performance of pre-trained models is insufficient because both ID and OOD samples originate from the same domain, leading to a high overlap in their embeddings. To address this issue, we introduce a new method called CED, a training-free OOD detection technique designed to enhance the distinction between ID and OOD datasets. We theoretically validate that specific auxiliary and oracle samples that satisfy certain conditions improve this distinction. Motivated by our theoretical analysis, CED enhances the differentiation by utilizing these specially designed auxiliary and oracle samples. As a result, CED significantly improves the ability of pre-trained models to distinguish between ID and OOD samples in text classification and hallucination detection tasks. Furthermore, we verify that CED is a plug-and-play method compatible with various backbone networks, such as RoBERTa, Llama, and OpenAI Embedding.
CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models
Authors: Sihan Cai, Jiaxu Zhao, Mykola Pechenizkiy, Meng Fang Qin Zhang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language.
Analyzing Context Contributions in LLM-based Machine Translation
Authors: Nuno M Guerreiro, Andre Martins Emmanouil Zaranis
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) have achieved state-of-the-art performance in machine translation (MT) and demonstrated the ability to leverage in-context learning through few-shot examples. However, the mechanisms by which LLMs use different parts of the input context remain largely unexplored. In this work, we provide a comprehensive analysis of context utilization in MT, studying how LLMs use various context parts, such as few-shot examples and the source text, when generating translations. We highlight several key findings: (1) the source part of few-shot examples appears to contribute more than its corresponding targets, irrespective of translation direction; (2) finetuning LLMs with parallel data alters the contribution patterns of different context parts; and (3) there is a positional bias where earlier few-shot examples have higher contributions to the translated sequence. Finally, we demonstrate that inspecting anomalous context contributions can potentially uncover pathological translations, such as hallucinations. Our findings shed light on the internal workings of LLM-based MT which go beyond those known for standard encoder-decoder MT models.
ARTS: Assessing Readability & Text Simplicity
Authors: Christin Katharina Kreutz, Fabian Haak, Philipp Schaer Björn Engelmann
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Automatic text simplification aims to reduce a text`s complexity. Its evaluation should quantify how easy it is to understand a text. Datasets with simplicity labels on text level are a prerequisite for developing such evaluation approaches. However, current publicly available datasets do not align with this, as they mainly treat text simplification as a relational concept (“How much simpler has this text gotten compared to the original version?”) or assign discrete readability levels.This work alleviates the problem of Assessing Readability & Text Simplicity. We present ARTS, a method for language-independent construction of datasets for simplicity assessment. We propose using pairwise comparisons of texts in conjunction with an Elo algorithm to produce a simplicity ranking and simplicity scores. Additionally, we provide a high-quality human-labeled and three GPT-labeled simplicity datasets. Our results show a high correlation between human and LLM-based labels, allowing for an effective and cost-efficient way to construct large synthetic datasets.
AXCEL: Automated eXplainable Consistency Evaluation using LLMs
Authors: Sahil Verma, Suransh Chopra, Abhishek Persad, Sarik Ghazarian, Narayanan Sadagopan P Aditya Sreekar
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large Language Models (LLMs) are widely used in both industry and academia for various tasks, yet evaluating the consistency of generated text responses continues to be a challenge. Traditional metrics like ROUGE and BLEU show a weak correlation with human judgment. More sophisticated metrics using Natural Language Inference (NLI) have shown improved correlations but are complex to implement, require domain-specific training due to poor cross-domain generalization, and lack explainability. More recently, prompt-based metrics using LLMs as evaluators have emerged; while they are easier to implement, they still lack explainability and depend on task-specific prompts, which limits their generalizability. This work introduces Automated eXplainable Consistency Evaluation using LLMs (AXCEL), a prompt-based consistency metric which offers explanations for the consistency scores by providing detailed reasoning and pinpointing inconsistent text spans. AXCEL is also a generalizable metric which can be adopted to multiple tasks without changing the prompt. AXCEL outperforms both non-prompt and prompt-based state-of-the-art (SOTA) metrics in detecting inconsistencies across summarization by 8.7%, free text generation by 6.2%, and data-to-text conversion tasks by 29.4%. We also evaluate the influence of underlying LLMs on prompt based metric performance and recalibrate the SOTA prompt-based metrics with the latest LLMs for fair comparison. Further, we show that AXCEL demonstrates strong performance using open source LLMs.
Prospector: Improving LLM Agents with Self-Asking and Trajectory Ranking
Authors: Youngsoo Jang, Lajanugen Logeswaran, Geon-Hyeong Kim, Yu Jin Kim, Honglak Lee, Moontae Lee Byoungjip Kim
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) have shown the ability to solve complex decision-making tasks beyond natural language processing tasks. LLM agents based on few-shot in-context learning (ICL) achieve surprisingly high performance without training. Despite their simplicity and generalizability, ICL-based agents are limited in their ability to incorporate feedback from an environment. In this paper, we introduce Prospector, an LLM agent that consists of two complementary LLMs, an Actor and a Critic. To elicit better instruction-aligned actions from the LLM agent, we propose AskAct prompting that performs an additional self-asking step such as goal and progress checking before generating an action. Furthermore, to implicitly incorporate the environment feedback, we propose Trajectory Ranking that orders generated trajectories by predicting the expected total reward. Prospector encourages the LLM Actor to generate diverse (creative) trajectories, and harnesses the LLM Critic to select the most rewarding trajectory. On representative decision-making benchmark environments such as ALFWorld and WebShop, we empirically demonstrate that Prospector can considerably increase the success rate of given tasks, while outperforming recent advancements such as ReAct and Reflexion.
Characterizing Text Datasets with Psycholinguistic Features
Authors: Charu Karakkaparambil James, Marius Kloft, Sophie Fellenz Marcio Monteiro
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Fine-tuning pretrained language models on task-specific data is a common practice in Natural Language Processing (NLP) applications. However, the number of pretrained models available to choose from can be very large, and it remains unclear how to select the optimal model without spending considerable amounts of computational resources, especially for the text domain. To address this problem, we introduce PsyMatrix, a novel framework designed to efficiently characterize text datasets. PsyMatrix evaluates multiple dimensions of text and discourse, producing interpretable, low-dimensional embeddings. Our framework has been tested using a meta-dataset repository that includes the performance of 24 pretrained large language models fine-tuned across 146 classification datasets. Using the proposed embeddings, we successfully developed a meta-learning system capable of recommending the most effective pretrained models (optimal and near-optimal) for fine-tuning on new datasets.