GenAI4ELab/papercli-papers huggingface dataset:
Exploring In-context Example Generation for Machine Translation
Authors: Dohyun Lee; Seungil Chad Lee; Chanwoo Yang; Yujin Baek; Jaegul Choo
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large language models (LLMs) have demonstrated strong performance across various tasks, leveraging their exceptional in-context learning ability with only a few examples.Accordingly, the selection of optimal in-context examples has been actively studied in the field of machine translation.However, these studies presuppose the presence of a demonstration pool with human-annotated pairs, making them less applicable to low-resource languages where such an assumption is challenging to meet.To overcome this limitation, this paper explores the research direction of in-context example generation for machine translation.Specifically, we propose Demonstration Augmentation for Translation (DAT), a simple yet effective approach that generates example pairs without relying on any external resources.This method builds upon two prior criteria, relevance and diversity, which have been highlighted in previous work as key factors for in-context example selection.Through experiments and analysis on low-resource languages where human-annotated pairs are scarce, we show that DAT achieves superior translation quality compared to the baselines.Furthermore, we investigate the potential of progressively accumulating generated pairs during test time to build and reuse a demonstration pool. Our implementation is publicly available at https://github.com/aiclaudev/DAT.
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
Authors: Jinheon Baek; Horst Samulowitz; Oktie Hassanzadeh; Dharmashankar Subramanian; Sola Shirai; Alfio Gliozzo; Debarun Bhattacharjya
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Text-to-SQL aims to translate natural language queries into SQL statements, which is practical as it enables anyone to easily retrieve the desired information from databases. Recently, many existing approaches tackle this problem with Large Language Models (LLMs), leveraging their strong capability in understanding user queries and generating corresponding SQL code. Yet, the parametric knowledge in LLMs might be limited to covering all the diverse and domain-specific queries that require grounding in various database schemas, which makes generated SQLs less accurate oftentimes. To tackle this, we propose constructing the knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. In particular, unlike existing approaches that either manually annotate knowledge or generate only a few pieces of knowledge for each query, our knowledge base is comprehensive, which is constructed based on a combination of all the available questions and their associated database schemas along with their relevant knowledge, and can be reused for unseen databases from different datasets and domains. We validate our approach on multiple text-to-SQL datasets, considering both the overlapping and non-overlapping database scenarios, where it outperforms relevant baselines substantially.
NBDESCRIB: A Dataset for Text Description Generation from Tables and Code in Jupyter Notebooks with Guidelines
Authors: Xuye Liu; Tengfei Ma; Yimu Wang; Fengjie Wang; Jian Zhao
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Generating cell-level descriptions for Jupyter Notebooks, which is a major resource consisting of codes, tables, and descriptions, has been attracting increasing research attention. However, existing methods for Jupyter Notebooks mostly focus on generating descriptions from code snippets or table outputs independently. On the other side, descriptions should be personalized as users have different purposes in different scenarios while previous work ignored this situation during description generation. In this work, we formulate a new task, personalized description generation with code, tables,and user-written guidelines in Jupyter Notebooks. To evaluate this new task, we collect and propose a benchmark, namely NBDESCRIB: , containing code, tables, and user-written guidelines as inputs and personalized descriptions as targets. Extensive experiments show that while existing models of text generation are able to generate fluent and readable descriptions, they still struggle to produce factually correct descriptions without user-written guidelines. CodeT5 achieved the highest scores in Orientation (1.27) and Correctness (-0.43) among foundation models in human evaluation, while the ground truth scored higher in Orientation (1.45) and Correctness (1.19). Common error patterns involve misalignment with guidelines, incorrect variable values, omission of im-031 portant code information, and reasoning errors.032 Moreover, ablation studies show that adding guidelines significantly enhances performance, both qualitatively and quantitatively.
ECoRAG: Evidentiality-guided Compression for Long Context RAG
Authors: Yeonseok Jeong; Jinsu Kim; Dohyeon Lee; Seung-won Hwang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) have shown remarkable performance in Open-Domain Question Answering (ODQA) by leveraging external documents through Retrieval-Augmented Generation (RAG). To reduce RAG overhead, from longer context, context compression is necessary. However, prior compression methods do not focus on filtering out non-evidential information, which limit the performance in LLM-based RAG. We thus propose Evidentiality-guided RAG, or ECoRAG framework. ECoRAG improves LLM performance by compressing retrieved documents based on evidentiality, ensuring whether answer generation is supported by the correct evidence. As an additional step, ECoRAG reflects whether the compressed content provides sufficient evidence, and if not, retrieves more until sufficient. Experiments show that ECoRAG improves LLM performance on ODQA tasks, outperforming existing compression methods. Furthermore, ECoRAG is highly cost-efficient, as it not only reduces latency but also minimizes token usage by retaining only the necessary information to generate the correct answer. Code is available at https://github.com/ldilab/ECoRAG.
From Complexity to Clarity: AI/NLP’s Role in Regulatory Compliance
Authors: Jivitesh Jain; Nivedhitha Dhanasekaran; Mona T. Diab
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Regulatory data compliance is a cornerstone of trust and accountability in critical sectors like finance, healthcare, and technology, yet its complexity poses significant challenges for organizations worldwide. Recent advances in natural language processing, particularly large language models, have demonstrated remarkable capabilities in text analysis and reasoning, offering promising solutions for automating compliance processes. This survey examines the current state of automated data compliance, analyzing key challenges and approaches across problem areas. We identify critical limitations in current datasets and techniques, including issues of adaptability, completeness, and trust. Looking ahead, we propose research directions to address these challenges, emphasizing standardized evaluation frameworks and balanced human-AI collaboration.
EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations
Authors: Hyunjong Kim; Sangyeop Kim; Jongheon Jeong; Yeongjae Cho; Sungzoon Cho
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advances in large language models and vision-language models have led to growing interest in explainable evaluation metrics for image captioning. However, these metrics generate explanations without standardized criteria, and the overall quality of the generated explanations remains unverified. In this paper, we propose EXPERT, a reference-free evaluation metric that provides structured explanations based on three fundamental criteria: fluency, relevance, and descriptiveness. By constructing large-scale datasets of high-quality structured explanations, we develop a two-stage evaluation template to effectively supervise a vision-language model for both scoring and explanation generation. EXPERT achieves state-of-the-art results on benchmark datasets while providing significantly higher-quality explanations than existing metrics, as validated through comprehensive human evaluation. Our code and datasets are available at https://github.com/hjkim811/EXPERT.
Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning
Authors: Eitan Wagner; Nitay Alon; Joseph M Barnby; Omri Abend
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Theory of Mind (ToM) capabilities in LLMs have recently become a central object of investigation, sparking debates and discussions. In this position paper, we explore many lines of work in different communities in AI and cognitive science. Inspired by cognitive work, we view ToM tasks as a two-step process: (I) first, determining whether and how to invoke ToM, which includes setting the appropriate Depth of Mentalizing (DoM); and (II) second, applying correct inference given the appropriate DoM. We identify that many works about ToM in LLMs, such as benchmarks and add-on modules, tend to unjustly overlook the first step and focus exclusively on the second one, which can be framed as a logic-reasoning task. We support our distinction with empirical evidence about the difficulty of the different steps in existing benchmarks. We conclude with suggestions for improved evaluation of ToM capabilities, inspired by dynamic environments used in cognitive tasks in biological agents.
LLMs are Biased Evaluators But Not Biased for Fact-Centric Retrieval Augmented Generation
Authors: Yen-Shan Chen; Jing Jin; Peng-Ting Kuo; Chao-Wei Huang; Yun-Nung Chen
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent studies have demonstrated that large language models (LLMs) exhibit significant biases in evaluation tasks, particularly in preferentially rating and favoring self-generated content. However, the extent to which this bias manifests in fact-oriented tasks, especially within retrieval-augmented generation (RAG) frameworks—where keyword extraction and factual accuracy take precedence over stylistic elements—remains unclear. Our study addresses this knowledge gap by simulating two critical phases of the RAG framework. In the first phase, LLMs evaluated human-authored and model-generated passages, emulating the pointwise reranking phase. The second phase involves conducting pairwise reading comprehension tests to simulate the generation phase. Contrary to previous findings indicating a self-preference in rating tasks, our results reveal no significant self-preference effect in RAG frameworks. Instead, we observe that factual accuracy significantly influences LLMs’ output, even in the absence of prior knowledge. These findings are consistent among three common QA datasets (NQ, MARCO, TriviaQA Datasets) and 5 widely adopted language models (GPT-3.5, GPT-4o-mini, Gemini, LLaMA3, and Mistral). Our research contributes to the ongoing discourse on LLM biases and their implications for RAG-based system, offering insights that may inform the development of more robust and unbiased LLM systems.
Standard Quality Criteria Derived from Current NLP Evaluations for Guiding Evaluation Design and Grounding Comparability and AI Compliance Assessments
Authors: Anya Belz; Simon Mille; Craig Thomson
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Research shows that two evaluation experiments reporting results for the same quality criterion name (e.g. Fluency) do not necessarily evaluate the same aspect of quality. Not knowing when two evaluations are comparable in this sense means we currently lack the ability to draw conclusions based on multiple independently conducted evaluations. It is hard to see how this issue can be fully addressed other than by the creation of a standard set of quality criterion names and definitions that the evaluations in use in NLP can be grounded in. Taking a descriptivist approach, the QCET Quality Criteria for Evaluation Taxonomy derives a standard set of 114 quality criterion names and definitions from three surveys of a combined total of 933 evaluation experiments in NLP, and structures them into a reference taxonomy. We present QCET and its uses in (i) establishing comparability of existing evaluations, (ii) guiding the design of new evaluations, and (iii) assessing regulation compliance.
skLEP: A Slovak General Language Understanding Benchmark
Authors: Marek Suppa; Andrej Ridzik; Daniel Hládek; Tomáš Javůrek; Viktória Ondrejová; Kristína Sásiková; Martin Tamajka; Marian Simko
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities. To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources. Within this paper, we also present the first systematic and extensive evaluation of a wide array of Slovak-specific, multilingual, and English pre-trained language models using the skLEP tasks. Finally, we also release the complete benchmark data, an open-source toolkit facilitating both fine-tuning and evaluation of models, and a public leaderboard at https://github.com/slovak-nlp/sklep in the hopes of fostering reproducibility and drive future research in Slovak NLU.
Can Vision Language Models Understand Mimed Actions?
Authors: Hyundong Justin Cho; Spencer Lin; Tejas Srinivasan; Michael Saxon; Deuksin Kwon; Natali T. Chavez; Jonathan May
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Non-verbal communication (NVC) is an integral part of human language, but it has been overlooked in natural language processing research. Studying NVC in general is challenging because of its high variance in interpretation among individuals and cultures, but mime—the theatrical technique of suggesting intent using only gesture, expression, and movement—is a subset of NVC with much lower human interpretation variance. As a gateway for evaluating vision-language models on their understanding of NVC, we propose Mime Identification-based Multimodal Evaluation (MIME), a gesture recognition task built upon a novel corpus of mimed activity comprising 86 unique gestures with a variety of perturbations applied to the avatar, background, and viewpoint for evaluating recognition robustness. We find that both open-weight and API-based vision-language models perform significantly worse than humans at identifying mimed gestures in MIME, motivating the need for increased research for instilling more robust understanding of human actions for VLMs.
Training Language Model to Critique for Better Refinement
Authors: Tianshu Yu; Chao Xiang; Mingchuan Yang; Pei Ke; Bosi Wen; Cunxiang Wang; Jiale Cheng; Li Zhang; Xinyu Mu; Chuxiong Sun; Minlie Huang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large language models (LLMs) have demonstrated remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. However, limited research has explored which types of critiques are most effective for improving model responses or how to generate such critiques. To address this gap, we introduce Refinement-oriented Critique Optimization (RCO), a novel framework designed to train critic models using refinement signals. RCO uses a feedback loop where critiques, generated by the critic model, guide the actor model in refining its responses. The critique utility (CU) quantifies the effectiveness of these refinements, serving as the reward signal for training the critic model. By focusing on critiques that lead to better refinements, RCO eliminates the need for direct critique preference assessment, ensuring that critiques driving meaningful improvements are rewarded. We evaluate RCO across five tasks—dialog generation, summarization, question answering, mathematical reasoning, and code generation—and show that it significantly outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes. Our contributions include the introduction of RCO, a novel supervision scheme based on refined response preferences, and comprehensive experimental results that highlight the method’s effectiveness in enhancing LLM critique-refinement loops. Code and data will be publicly available upon acceptance of this paper.
Dynamic Task Vector Grouping for Efficient Multi-Task Prompt Tuning
Authors: Peiyi Zhang; Richong Zhang; Zhijie Nie; Ziqiao Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Multi-task prompt tuning utilizes multiple high-resource source tasks to improve performance on low-source target tasks. Existing approaches transfer the soft prompt trained by combining all source tasks or a single “high-similar” source task one-time-only. However, we find that the optimal transfer performance often comes from a combination of source tasks, which is neither one nor all. Further, we find that the similarity between source and target tasks also changes dynamically during fine-tuning after transfering, making similarity calculation in the initiation stage inadequate. To address these issues, we propose a method called Dynamic Task Vector Grouping (DTVG), whose core ideas contain (1) measuring the task similarity with task vectors instead of soft prompt, (2) grouping the optimal source task combination based on two metrics: target similarity and knowledge consistency; (3) dynamically updating the combination in each iteration step. Extensive experiments on the 26 NLP datasets under different settings demonstrate that DTVG effectively groups similar source tasks while reducing negative transfer, achieving the start-of-art performance.
DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues
Authors: Kyochul Jang; Donghyeon Lee; Kyusik Kim; Dongseok Heo; Taewhoo Lee; Woojeong Kim; Bongwon Suh
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Existing function-calling benchmarks focus on single-turn interactions. However, they overlook the complexity of real-world scenarios. To quantify how existing benchmarks address practical applications, we introduce DICE-SCORE, a metric that evaluates the dispersion of tool-related information such as function name and parameter values throughout the dialogue. Analyzing existing benchmarks through DICE-SCORE reveals notably low scores, highlighting the need for more realistic scenarios. To address this gap, we present DICE-BENCH, a framework that constructs practical function-calling datasets by synthesizing conversations through a tool graph that maintains dependencies across rounds and a multi-agent system with distinct personas to enhance dialogue naturalness. The final dataset comprises 1,607 high-DICE-SCORE instances. Our experiments on 19 LLMs with DICE-BENCH show that significant advances are still required before such models can be deployed effectively in real-world settings. Our code and data are all publicly available.
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Authors: Jinyu Guo; Xunlei Chen; Qiyang Xia; Zhaokun Wang; Jie Ou; Libo Qin; Shunyu Yao; Wenhong Tian
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. Our queries directly learn binary hash codes from knowledgebase code, eliminating intermediate feature extraction steps, and significantly reducing storage and computational overhead. Building upon this hash-based efficient retrieval framework, we establish the foundation for fine-grained chunking. Consequently, we design a Prompt-Guided Chunk-to-Context (PGCC) module that leverages retrieved hash-indexed propositions and their original document segments through prompt engineering to enhance the LLM’s contextual awareness. Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. Additionally, The proposed system outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores.
A Constrained Text Revision Agent via Iterative Planning and Searching
Authors: Hannan Cao; Hwee Tou Ng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Existing text revision systems are capable of generating fluent and coherent text, but struggle with constrained text revision (CTR), which requires adherence to specific constraints. Furthermore, adapting these systems to diverse constraints is challenging. To bridge this gap, we introduce TRIPS, a Text Revision agent via Iterative Planning and Searching, focusing on CTR. TRIPS utilizes a planner, a reviser (i.e., a large language model), and adaptable tools to generate revisions tailored to different scenarios. Specifically, we propose an iterative self-training alignment method to construct the planner, which generates tool usage and text revision plans. Furthermore, we propose Tool-Guided Monte Carlo Tree Search (TG-MCTS), a novel CTR algorithm that extends MCTS with tool-guided expansion and evaluation, enabling the search for optimal revision strategies across various scenarios. To evaluate TRIPS, we introduce ConsTRev, a dataset with multi-level constrained instructions for paragraph-level revision. Experimental results show that TRIPS outperforms baselines in both constraint adherence and revision quality. Furthermore, TRIPS exhibits robust performance across diverse use cases, including plain text and LaTeX revision.
MMRefine: Unveiling the Obstacles to Robust Refinement in Multimodal Large Language Models
Authors: Gio Paik; Geewook Kim; Jinbae Im
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: This paper introduces MMRefine, a MultiModal Refinement benchmark designed to evaluate the error refinement capabilities of Multimodal Large Language Models (MLLMs). As the emphasis shifts toward enhancing reasoning during inference, MMRefine provides a framework that evaluates MLLMs’ abilities to detect and correct errors across six distinct scenarios beyond just comparing final accuracy before and after refinement. Furthermore, the benchmark analyzes the refinement performance by categorizing errors into six error types.Experiments with various open and closed MLLMs reveal bottlenecks and factors impeding refinement performance, highlighting areas for improvement in effective reasoning enhancement. Our code and dataset are publicly available at https://github.com/naver-ai/MMRefine.