GenAI4ELab/papercli-papers huggingface dataset:
JuStRank: Benchmarking LLM Judges for System Ranking
Authors: Ariel Gera; Odellia Boni; Yotam Perlitz; Roy Bar-Haim; Lilach Eden; Asaf Yehudai
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and versatility of such evaluations make the use of LLM-based judges a compelling solution for this challenge. Crucially, this approach requires first to validate the quality of the LLM judge itself. Previous work has focused on instance-based assessment of LLM judges, where a judge is evaluated over a set of responses, or response pairs, while being agnostic to their source systems. We argue that this setting overlooks critical factors affecting system-level ranking, such as a judge’s positive or negative bias towards certain systems. To address this gap, we conduct the first large-scale study of LLM judges as system rankers. System scores are generated by aggregating judgment scores over multiple system outputs, and the judge’s quality is assessed by comparing the resulting system ranking to a human-based ranking. Beyond overall judge assessment, our analysis provides a fine-grained characterization of judge behavior, including their decisiveness and bias.
Generating Diverse Training Samples for Relation Extraction with Large Language Models
Authors: Zexuan Li; Hongliang Dai; Piji Li
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Using Large Language Models (LLMs) to generate training data can potentially be a preferable way to improve zero or few-shot NLP tasks. However, many problems remain to be investigated for this direction. For the task of Relation Extraction (RE), we find that samples generated by directly prompting LLMs may easily have high structural similarities with each other. They tend to use a limited variety of phrasing while expressing the relation between a pair of entities. Therefore, in this paper, we study how to effectively improve the diversity of the training samples generated with LLMs for RE, while also maintaining their correctness. We first try to make the LLMs produce dissimilar samples by directly giving instructions in In-Context Learning (ICL) prompts. Then, we propose an approach to fine-tune LLMs for diversity training sample generation through Direct Preference Optimization (DPO). Our experiments on commonly used RE datasets show that both attempts can improve the quality of the generated training data. We also find that comparing with directly performing RE with an LLM, training a non-LLM RE model with its generated samples may lead to better performance.
MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts
Authors: Dominik Macko; Jakub Kopál; Robert Moro; Ivan Srba
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Recent LLMs are able to generate high-quality multilingual texts, indistinguishable for humans from authentic human-written ones. Research in machine-generated text detection is however mostly focused on the English language and longer texts, such as news articles, scientific papers or student essays. Social-media texts are usually much shorter and often feature informal language, grammatical errors, or distinct linguistic items (e.g., emoticons, hashtags). There is a gap in studying the ability of existing methods in detection of such texts, reflected also in the lack of existing multilingual benchmark datasets. To fill this gap we propose the first multilingual (22 languages) and multi-platform (5 social media platforms) dataset for benchmarking machine-generated text detection in the social-media domain, called MultiSocial. It contains 472,097 texts, of which about 58k are human-written and approximately the same amount is generated by each of 7 multilingual LLMs. We use this benchmark to compare existing detection methods in zero-shot as well as fine-tuned form. Our results indicate that the fine-tuned detectors have no problem to be trained on social-media texts and that the platform selection for training matters.
Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection
Authors: Cilin Yan; Jingyun Wang; Lin Zhang; Ruihui Zhao; Xiaopu Wu; Kai Xiong; Qingsong Liu; Guoliang Kang; Yangyang Kang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Automatic prompt engineering aims to enhance the generation quality of large language models (LLMs). Recent works utilize feedbacks generated from erroneous cases to guide the prompt optimization. During inference, they may further retrieve several semantically-related exemplars and concatenate them to the optimized prompts to improve the performance. However, those works only utilize the feedback at the current step, ignoring historical and unseleccted feedbacks which are potentially beneficial. Moreover, the selection of exemplars only considers the general semantic relationship and may not be optimal in terms of task performance and matching with the optimized prompt. In this work, we propose an Exemplar-Guided Reflection with Memory mechanism (ERM) to realize more efficient and accurate prompt optimization. Specifically, we design an exemplar-guided reflection mechanism where the feedback generation is additionally guided by the generated exemplars. We further build two kinds of memory to fully utilize the historical feedback information and support more effective exemplar retrieval. Empirical evaluations show our method surpasses previous state-of-the-arts with less optimization steps, i.e., improving F1 score by 10.1 on LIAR dataset, and reducing half of the optimization steps on ProTeGi.
Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation
Authors: Aneta Zugecova; Dominik Macko; Ivan Srba; Robert Moro; Jakub Kopál; Katarína Marcinčinová; Matúš Mesarčík
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined narratives. Their capabilities to generate personalized (in various aspects) content have also been evaluated and mostly found usable. However, a combination of personalization and disinformation abilities of LLMs has not been comprehensively studied yet. Such a dangerous combination should trigger integrated safety filters of the LLMs, if there are some. This study fills this gap by evaluating vulnerabilities of recent open and closed LLMs, and their willingness to generate personalized disinformation news articles in English. We further explore whether the LLMs can reliably meta-evaluate the personalization quality and whether the personalization affects the generated-texts detectability. Our results demonstrate the need for stronger safety-filters and disclaimers, as those are not properly functioning in most of the evaluated LLMs. Additionally, our study revealed that the personalization actually reduces the safety-filter activations; thus effectively functioning as a jailbreak. Such behavior must be urgently addressed by LLM developers and service providers.
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents
Authors: Cheng Qian; Peixuan Han; Qinyu Luo; Bingxiang He; Xiusi Chen; Yuji Zhang; Hongyi Du; Jiarui Yao; Xiaocheng Yang; Denghui Zhang; Yunzhu Li; Heng Ji
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative adaptation in unfamiliar environments. To address this, we introduce EscapeBench—a benchmark suite of room escape game environments designed to challenge agents with creative reasoning, unconventional tool use, and iterative problem-solving to uncover implicit goals. Our results show that current LM models, despite employing working memory and Chain-of-Thought reasoning, achieve only 15% average progress without hints, highlighting their limitations in creativity. To bridge this gap, we propose EscapeAgent, a framework designed to enhance creative reasoning through Foresight (innovative tool use) and Reflection (identifying unsolved tasks). Experiments show that EscapeAgent can execute action chains over 1,000 steps while maintaining logical coherence. It navigates and completes games with up to 40% fewer steps and hints, performs robustly across difficulty levels, and achieves higher action success rates with more efficient and innovative puzzle-solving strategies.
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving
Authors: Teng Wang; Wing Yin Yu; Zhenqi He; Zehua Liu; HaileiGong HaileiGong; Han Wu; Xiongwei Han; Wei Shi; Ruifeng She; Fangzhou Zhu; Tao Zhong
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in operations research domain lack detailed annotations of the modeling process, such as variable definitions, focusing solely on objective values, which hinders reinforcement learning applications. To address this, we release the StructuredOR dataset, annotated with comprehensive labels that capture the complete mathematical modeling process. We further propose BPP-Search, an algorithm that integrates reinforcement learning into a tree-of-thought structure using Beam search, a Process reward model, and a pairwise Preference algorithm. This approach enables efficient exploration of tree structures, avoiding exhaustive search while improving accuracy. Extensive experiments on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets show that BPP-Search significantly outperforms state-of-the-art methods. In tree-based reasoning, BPP-Search excels in accuracy and efficiency, enabling faster retrieval of correct solutions. The StructuredOR dataset is available on Huggingface https://huggingface.co/datasets/LLM4OR/StructuredOR and GitHub https://github.com/LLM4OR/StructuredOR.
LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation
Authors: Jakub Šmíd; Pavel Priban; Pavel Kral
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Cross-lingual aspect-based sentiment analysis (ABSA) involves detailed sentiment analysis in a target language by transferring knowledge from a source language with available annotated data. Most existing methods depend heavily on often unreliable translation tools to bridge the language gap. In this paper, we propose a new approach that leverages a large language model (LLM) to generate high-quality pseudo-labelled data in the target language without the need for translation tools. First, the framework trains an ABSA model to obtain predictions for unlabelled target language data. Next, LLM is prompted to generate natural sentences that better represent these noisy predictions than the original text. The ABSA model is then further fine-tuned on the resulting pseudo-labelled dataset. We demonstrate the effectiveness of this method across six languages and five backbone models, surpassing previous state-of-the-art translation-based approaches. The proposed framework also supports generative models, and we show that fine-tuned LLMs outperform smaller multilingual models.
Fusing Highly Specialized Language Models for Comprehensive Expertise
Authors: Ning Ding; Yulin Chen; Ganqu Cui; Xingtai Lv; Weilin Zhao; Kaiyan Zhang; Ruobing Xie; Bowen Zhou; Zhiyuan Liu; Maosong Sun
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that strive to achieve high performance across all three domains simultaneously. Achieving a very high level of proficiency for an LLM within a specific domain often requires extensive training with relevant corpora, which is typically accompanied by a sacrifice in performance in other domains. In this paper, we aim to “play the dealt cards well” and propose to fuse models that are already highly-specialized directly. The proposed fusing framework, , consists of different distinct specialists that are already sufficiently trained on different domains (we mainly focus on language, coding, and mathematics in this paper). A token-level gating mechanism is introduced to blend the specialists’ outputs. A two-stage training strategy accompanied by balanced sampling is designed to ensure stability. To effectively train the fused model, we further construct a high-quality supervised instruction tuning dataset, , which includes text, code, and mathematical content. This dataset comprises approximately 300,000 instructions and covers a wide range of topics in each domain. Experiments show that our model could simultaneously achieve mastery of the three crucial domains.
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases
Authors: Meng-Chieh Lee; Qi Zhu; Costas Mavromatis; Zhen Han; Soji Adeshina; Vassilis N. Ioannidis; Huzefa Rangwala; Christos Faloutsos
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions?Retrieval-Augmented Generation (RAG) retrieves documents to assist large language models (LLMs) in question answering; while Graph RAG (GRAG) uses structured knowledge bases as its knowledge source.However, many questions require both textual and relational information from SKB — referred to as “hybrid” questions — which complicates the retrieval process and underscores the need for a hybrid retrieval method that leverages both information.In this paper, through our empirical analysis, we identify key insights that show why existing methods may struggle with hybrid question answering (HQA) over SKB. Based on these insights, we propose HybGRAG for HQA, consisting of a retriever bank and a critic module, with the following advantages:1. Agentic, it automatically refines the output by incorporating feedback from the critic module, 2. Adaptive, it solves hybrid questions requiring both textual and relational information with the retriever bank,3. Interpretable, it justifies decision making with intuitive refinement path, and4. Effective, it surpasses all baselines on HQA benchmarks.In experiments on the STaRK benchmark, HybGRAG achieves significant performance gains, with an average relative improvement in Hit@1 of 51%.
Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms
Authors: Rajvardhan Oak; Muhammad Haroon; Claire Wonjeong Jo; Magdalena Wojcieszak; Anshuman Chhabra
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Social media platforms utilize Machine Learning (ML) and Artificial Intelligence (AI) powered recommendation algorithms to maximize user engagement, which can result in inadvertent exposure to harmful content. Current moderation efforts, reliant on classifiers trained with extensive human-annotated data, struggle with scalability and adapting to new forms of harm. To address these challenges, we propose a novel re-ranking approach using Large Language Models (LLMs) in zero-shot and few-shot settings. Our method dynamically assesses and re-ranks content sequences, effectively mitigating harmful content exposure without requiring extensive labeled data. Alongside traditional ranking metrics, we also introduce two new metrics to evaluate the effectiveness of re-ranking in reducing exposure to harmful content. Through experiments on three datasets, three models and across three configurations, we demonstrate that our LLM-based approach significantly outperforms existing proprietary moderation approaches, offering a scalable and adaptable solution for harm mitigation.
Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review
Authors: Yidong Gan; Maciej Rybinski; Ben Hachey; Jonathan K. Kummerfeld
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, shows that widely used evaluation methods are not aligned with real clinical contexts. For example, evaluations that focus on the top 50 most common codes are an oversimplification, as there are thousands of codes used in practice. This position paper aims to align AI coding research more closely with practical challenges of clinical coding. Based on our analysis, we offer eight specific recommendations, suggesting ways to improve current evaluation methods. Additionally, we propose new AI-based methods beyond automated coding, suggesting alternative approaches to assist clinical coders in their workflows.
MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
Authors: Ziyan Liu; Chunxiao Fan; Haoran Lou; Yuexin Wu; Kaiwei Deng
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. MIND implements three key strategies: 1) We retrieve similar memes from an unannotated reference set to provide contextual information. 2) We propose a bi-directional insight derivation mechanism to extract a comprehensive understanding of similar memes. 3) We then employ a multi-agent debate mechanism to ensure robust decision-making through reasoned arbitration. Extensive experiments on three meme datasets demonstrate that our proposed framework not only outperforms existing zero-shot approaches but also shows strong generalization across different model architectures and parameter scales, providing a scalable solution for harmful meme detection.
EvoWiki: Evaluating LLMs on Evolving Knowledge
Authors: Wei Tang; Yixin Cao; Yang Deng; Jiahao Ying; Bo Wang; Yizhe Yang; Yuyue Zhao; Qi Zhang; Xuanjing Huang; Yu-Gang Jiang; Yong Liao
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as contamination. In this paper, we introduce EvoWiki, an evolving dataset designed to reflect knowledge evolution by categorizing information into stable, evolved, and uncharted states. EvoWiki is fully auto-updatable, enabling precise evaluation of continuously changing knowledge and newly released LLMs. Through experiments with Retrieval-Augmented Generation (RAG) and Continual Learning (CL), we evaluate how effectively LLMs adapt to evolving knowledge. Our results indicate that current models often struggle with evolved knowledge, frequently providing outdated or incorrect responses. Moreover, the dataset highlights a synergistic effect between RAG and CL, demonstrating their potential to better adapt to evolving knowledge. EvoWiki provides a robust benchmark for advancing future research on the knowledge evolution capabilities of large language models.
Rethinking Repetition Problems of LLMs in Code Generation
Authors: Yihong Dong; Yuchen Liu; Xue Jiang; Bin Gu; Zhi Jin; Ge Li
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: With the advent of neural language models, the performance of code generation has been significantly boosted. However, the problem of repetitions during the generation process continues to linger. Previous work has primarily focused on content repetition, which is merely a fraction of the broader repetition problem in code generation. A more prevalent and challenging problem is structural repetition. In structural repetition, the repeated code appears in various patterns but possesses a fixed structure, which can be inherently reflected in grammar. In this paper, we formally define structural repetition and propose an efficient decoding approach called RPG, which stands for Repetition Penalization based on Grammar, to alleviate the repetition problems in code generation for LLMs. Specifically, RPG first leverages grammar rules to identify repetition problems during code generation, and then strategically decays the likelihood of critical tokens that contribute to repetitions, thereby mitigating them in code generation. To facilitate this study, we construct a new dataset CodeRepetEval to comprehensively evaluate approaches for mitigating the repetition problems in code generation. Extensive experimental results demonstrate that RPG substantially outperforms the best-performing baselines on CodeRepetEval dataset as well as HumanEval and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code.