GenAI4ELab/papercli-papers huggingface dataset:
RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning
Authors: Yifan Niu; Miao Peng; Nuo Chen; Yatao Bian; Tingyang Xu; Jia Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The conceptual knowledge in Large Language Models (LLMs) can become outdated over time, and concept editing is often an option. Current evaluations on conceptual knowledge editing primarily focus on whether the definitions of concepts are successfully edited, neglecting the impact on the model’s related beliefs. To address this gap, we introduce a benchmark called RelEdit, which includes criteria and questions to assess both concept-level and instance-level relational reasoning abilities of edited models. Our findings reveal that existing knowledge editing methods struggle to reason about related conceptual knowledge effectively. Additionally, we introduce a simple memory-based in-context editing baseline, MICE, which prompts the language model to generate answers that align with the stored edited concepts in external memory. In addition, we find that MICE obtains the best scores on our benchmark, suggesting a promising research direction for model editing.
Unlocking Speech Instruction Data Potential with Query Rewriting
Authors: Yonghua Hei; Yibo Yan; Shuliang Liu; Huiyu Zhou; Linfeng Zhang; Xuming Hu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: End-to-end Large Speech Language Models (**LSLMs**) demonstrate strong potential in response latency and speech comprehension capabilities, showcasing general intelligence across speech understanding tasks. However, the ability to follow speech instructions has not been fully realized due to the lack of datasets and heavily biased training tasks. Leveraging the rich ASR datasets, previous approaches have used Large Language Models (**LLMs**) to continue the linguistic information of speech to construct speech instruction datasets. Yet, due to the gap between LLM-generated results and real human responses, the continuation methods further amplify these shortcomings. Given the high costs of collecting and annotating speech instruction datasets by humans, using speech synthesis to construct large-scale speech instruction datasets has become a balanced and robust alternative. Although modern Text-To-Speech (**TTS**) models have achieved near-human-level synthesis quality, it is challenging to appropriately convert out-of-distribution text instruction to speech due to the limitations of the training data distribution in TTS models. To address this issue, we propose a query rewriting framework with multi-LLM knowledge fusion, employing multiple agents to annotate and validate the synthesized speech, making it possible to construct high-quality speech instruction datasets without relying on human annotation. Experiments show that this method can transform text instructions into distributions more suitable for TTS models for speech synthesis through zero-shot rewriting, increasing data usability from 72% to 93%. It also demonstrates unique advantages in rewriting tasks that require complex knowledge and context-related abilities.
From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs
Authors: Tianle Gu; Kexin Huang; Ruilin Luo; Yuanqi Yao; Xiuying Chen; Yujiu Yang; Yan Teng; Yingchun Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: LLM Unlearning plays a crucial role in removing sensitive information from language models to mitigate potential misuse. However, previous approaches often treat nonsensical responses or template-based refusals (e.g., “Sorry, I cannot answer.”) as the unlearning target, which can give the impression of deliberate information suppression, making the process even more vulnerable to attacks and jailbreaks. Moreover, most methods rely on auxiliary models or retaining datasets, which adds complexity to the unlearning process. To address these challenges, we propose MEOW, a streamlined and stealthy unlearning method that eliminates the need for auxiliary models or retaining data while avoiding leakage through its innovative use of inverted facts. These inverted facts are generated by an offline LLM and serve as fine-tuning labels. Meanwhile, we introduce MEMO, a novel metric that measures the model’s memorization, to select optimal fine-tuning targets. The use of inverted facts not only maintains the covert nature of the model but also ensures that sensitive information is effectively forgotten without revealing the target data. Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility. MEOW also maintains strong performance across NLU and NLG tasks and demonstrates superior resilience to attacks, validated via the Min-K% membership inference method.
Context-DPO: Aligning Language Models for Context-Faithfulness
Authors: Baolong Bi; Shaohan Huang; Yiwei Wang; Tianchi Yang; Zihan Zhang; Haizhen Huang; Lingrui Mei; Junfeng Fang; Zehao Li; Furu Wei; Weiwei Deng; Feng Sun; Qi Zhang; Shenghua Liu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intentions and values, improving context-faithfulness through alignment remains underexplored. To address this, we propose Context-DPO, the first alignment method specifically designed to enhance LLMs’ context-faithfulness. We introduce ConFiQA, a benchmark that simulates Retrieval-Augmented Generation (RAG) scenarios with knowledge conflicts to evaluate context-faithfulness. By leveraging faithful and stubborn responses to questions with provided context from ConFiQA, our Context-DPO aligns LLMs through direct preference optimization. Extensive experiments demonstrate that our Context-DPO significantly improves context-faithfulness, achieving 35% to 280% improvements on popular open-source models. Further analysis demonstrates that Context-DPO preserves LLMs’ generative capabilities while providing interpretable insights into context utilization.
Reasoning Does Not Necessarily Improve Role-Playing Ability
Authors: Xiachong Feng; Longxu Dou; Lingpeng Kong
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The application of role-playing large language models (LLMs) is rapidly expanding in both academic and commercial domains, driving an increasing demand for high-precision role-playing models. Simultaneously, the rapid advancement of reasoning techniques has continuously pushed the performance boundaries of LLMs. This intersection of practical role-playing demands and evolving reasoning capabilities raises an important research question: Can reasoning techniques enhance the role-playing capabilities of LLMs?” To address this, we conduct a comprehensive study using 6 role-playing benchmarks, 24 LLMs, and 3 distinct role-playing strategies, comparing the effectiveness of direct zero-shot role-playing, role-playing with Chain-of-Thought (CoT), and role-playing using reasoning-optimized LLMs. Our findings reveal that CoT may reduce role-playing performance, reasoning-optimized LLMs are unsuitable for role-playing, reasoning ability disrupts the role-playing scaling law, and large models still lack proficiency in advanced role-playing. Furthermore, based on extensive experimental results, we propose two promising future research directions: Role-aware Chain-of-Thought for improving role-playing LLMs and Reinforcement Learning for role-playing LLMs, aiming to enhance the adaptability, consistency, and effectiveness of role-playing LLMs for both research and real-world applications.
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
Authors: Xiaokang Zhang; Sijia Luo; Bohan Zhang; Zeyao Ma; Jing Zhang; Yang Li; Guanlin Li; Zijun Yao; Kangli Xu; Jinchang Zhou; Daniel Zhang-Li; Jifan Yu; Shu Zhao; Juanzi Li; Jie Tang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to understand reasoning patterns more effectively as well as a cross-way validation strategy, ensuring the quality of the automatically generated data. To evaluate the performance of TableLLM, we have crafted benchmarks tailored to address both document and spreadsheet formats as well as constructed a well-organized evaluation pipeline capable of handling both scenarios. Thorough evaluations underscore the advantages of TableLLM when compared to various existing general-purpose and tabular data-focused LLMs. We have publicly released the model checkpoint, source code, benchmarks, and a web application for user interaction on this anonymized repository.
A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
Authors: Wenxuan Wang; Zizhan Ma; Zheng Wang; Chenghan Wu; Jiaming Ji; Wenting Chen; Xiang Li; Yixuan Yuan
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) are transforming healthcare through LLM-based agents that can understand and assist with medical tasks. This survey examines the architectures, applications, and challenges of LLM-based agents in medicine. We analyze key components including system profiles, clinical planning, medical reasoning frameworks, and external capacity enhancement. The survey covers major applications in clinical decision support, medical documentation, training simulations, and healthcare service optimization, along with evaluation frameworks and metrics. While these agents show promise in enhancing healthcare delivery, challenges remain in hallucination management, multimodal integration, implementation, and ethics. We conclude by highlighting future directions in medical reasoning, physical system integration, and training simulations, providing researchers and practitioners with a structured overview of the field’s current state and prospects.
Context-Robust Knowledge Editing for Language Models
Authors: Haewon Park; Gyubin Choi; Minjun Kim; Yohan Jo
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Knowledge editing (KE) methods offer an efficient way to modify knowledge in large language models. Current KE evaluations typically assess editing success by considering only the edited knowledge without any preceding contexts. In real-world applications, however, preceding contexts often trigger the retrieval of the original knowledge and undermine the intended edit. To address this issue, we have developed CHED—a benchmark designed to evaluate the context robustness of KE methods. Evaluations on CHED show that they often fail when preceding contexts are present. To mitigate this shortcoming, we introduce CoRE, a KE method designed to strengthen context robustness by minimizing context-sensitive variance in hidden states of the model for edited knowledge. This method not only improves the editing success rate in situations where a preceding context is present but also preserves the overall capabilities of the model. We also provide an in-depth analysis of the differing impacts of preceding contexts when introduced as user utterances versus assistant responses, and we dissect attention-score patterns to assess how specific tokens influence editing success. We release our dataset and code at [https://github.com/holi-lab/CoRE](https://github.com/holi-lab/CoRE).
Multi-Agent Collaboration via Cross-Team Orchestration
Authors: Zhuoyun Du; Chen Qian; Wei Liu; Zihao Xie; YiFei Wang; Rennai Qiu; Yufan Dang; Weize Chen; Cheng Yang; Ye Tian; Xuantang Xiong; Lei Han
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. A representative scenario is in software development, where agents can collaborate in a team like humans, following predefined phases to complete sub-tasks sequentially. However, for an agent team, each phase yields only one possible outcome. This results in the completion of only one development chain, thereby losing the opportunity to explore multiple potential decision paths within the solution space. Consequently leading to suboptimal results or extensive trial and error. To address this, we introduce Cross-Team Orchestration (Croto), a scalable multi-team framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. Experiments reveal a notable increase in software quality compared to state-of-the-art baselines. We further tested our framework on story generation tasks, which demonstrated a promising generalization ability of our framework in other domains. The code and data is available at https://github.com/OpenBMB/ChatDev/tree/macnet
Semantic Evaluation of Multilingual Data-to-Text Generation via NLI Fine-Tuning: Precision, Recall and F1 scores
Authors: William Soto Martinez; Yannick Parmentier; Claire Gardent
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Performance in the KG-to-Text task has improved over the years, particularly in English. However, models are still prone to mistakes like Additions and Omissions. Furthermore, few languages are taken into account since both train and test data are not readily available. In this paper, we hope to facilitate the development and improvement of multilingual KG-to-Text models by providing a multilingual evaluation framework that is reference-less (no need for test data) and permits estimating how much a KG-to-Text Model under- (omission) or over- (addition) generates. We focus on two high (English, Russian) and five low (Breton, Irish, Maltese, Welsh, Xhosa) resource languages and show that our metric has fair to moderate correlation with reference-based metrics, positioning it as a consistent alternative when no references are available. We also show that our metric outperforms prior reference-less metrics in correlation with existing human judgments. Additional human evaluation shows moderate to strong correlation with human annotators in assessing precision and recall at a higher granularity level than shown in previous studies. Since our metric provides scores for precision and recall, it helps better assess the level of over- or under-generation of multilingual KG-to-Text models.
Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval
Authors: Kidist Amde Mekonnen; Yosef Worku Alemneh; Maarten de Rijke
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Neural retrieval methods using transformer-based pre-trained language models have advanced multilingual and cross-lingual retrieval. However, their effectiveness for low-resource, morphologically rich languages such as Amharic remains underexplored due to data scarcity and suboptimal tokenization. We address this gap by introducing Amharic-specific dense retrieval models based on pre-trained Amharic BERT and RoBERTa backbones. Our proposed RoBERTa-Base-Amharic-Embed model (110M parameters) achieves a 17.6% relative improvement in MRR@10 and a 9.86% gain in Recall@10 over the strongest multilingual baseline, Arctic Embed 2.0 (568M parameters). More compact variants, such as RoBERTa-Medium-Amharic-Embed (42M), remain competitive while being over 13× smaller. Additionally, we train a ColBERT-based late interaction retrieval model that achieves the highest MRR@10 score (0.843) among all evaluated models. We benchmark our proposed models against both sparse and dense retrieval baselines to systematically assess retrieval effectiveness in Amharic. Our analysis highlights key challenges in low-resource settings and underscores the importance of language-specific adaptation. To foster future research in low-resource IR, we publicly release our dataset, codebase, and trained models at https://github.com/kidist-amde/amharic-ir-benchmarks.
Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge
Authors: Yue Fang; Zhi Jin; Jie An; Hongshen Chen; Xiaohong Chen; Naijun Zhan
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Temporal Logic (TL), especially Signal Temporal Logic (STL), enables precise formal specification, making it widely used in cyber-physical systems such as autonomous driving and robotics. Automatically transforming NL into STL is an attractive approach to overcome the limitations of manual transformation, which is time-consuming and error-prone. However, due to the lack of datasets, automatic transformation currently faces significant challenges and has not been fully explored. In this paper, we propose a NL-STL dataset named STL-Diversity-Enhanced (STL-DivEn), comprising 16,000 samples enriched with diverse patterns. To develop the dataset, we first manually create a small-scale seed set of NL-STL pairs. Next, representative examples are identified through clustering and used to guide large language models (LLMs) in generating additional NL-STL pairs. Finally, diversity and accuracy are ensured through rigorous rule-based filters and human validation. Furthermore, we introduce the Knowledge-Guided STL Transformation (KGST) framework, a novel approach for transforming natural language into STL, involving a generate-then-refine process based on external knowledge. Statistical analysis shows that the STL-DivEn dataset exhibits more diversity than the existing NL-STL dataset. Moreover, both metric-based and human evaluations indicate that our KGST approach outperforms baseline models in transformation accuracy on STL-DivEn and DeepSTL datasets.
DAGS: A Dependency-Based Dual-Attention and Global Semantic Improvement Framework for Metaphor Recognition
Authors: Puli Chen; Cheng Yang; Xingmao Zhang; Qingbao Huang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Current metaphor recognition mainly rely on Metaphor Detection Theory (MDT), such as the Metaphor Identification Procedure, which recognizes metaphors by comparing the basic meaning of target word with context meaning. Existing studies have gradually adopted literal annotations to model basic meanings, rejecting the aggregated meanings of target words. However, these methods ignore the problem of interference caused by literal annotations, and do not make full use of semantic expression relations of MDT, making the models difficult to detect and generalize. To address these challenges, we propose a dependency-based Dual-Attention and Global Semantic Improvement (DAGS) framework. DAGS first extracts literal annotations of target words as basic meaning from several mainstream corpora. Then, we apply dependency tree and dual-attention while filtering on input sentences and basic meanings. Finally, we improve the MDT to further consider the global semantic relationship on contexts. The DAGS can not only extract features from multiple information sources but alsoeffectively removes redundancy, while focusing on mission-critical information. We achieve state-of-the-art on several mainstream metaphor datasets (e.g., VUA ALL, VUAverb, TroFi and PSUCMC), which suggests that filtering and global semantic improvement of contexts is crucial for enhancing metaphor recognition performance.
ESF: Efficient Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models
Authors: Xiaofan Bai; Pingyi Hu; Xiaojing Ma; Linchen Yu; Dongmei Zhang; Qi Zhang; Bin Benjamin Zhu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The rapid adoption of large language models (LLMs) in diverse applications has intensified concerns over their security and integrity, especially in cloud environments where internal model parameters are inaccessible to users. Traditional tamper detection methods, designed for deterministic classification models, fail to address the output randomness and massive parameter spaces characteristic of LLMs. In this paper, we introduce Efficient Sensitive Fingerprinting (ESF), the first fingerprinting method tailored for black-box tamper detection of LLMs. ESF generates fingerprint samples by optimizing output sensitivity at selected detection token positions and leverages Randomness-Set Consistency Checking (RSCC) to accommodate inherent output randomness. Furthermore, a novel Max Coverage Strategy (MCS) is proposed to select an optimal set of fingerprint samples that maximizes joint sensitivity to tampering. Grounded in a rigorous theoretical framework, ESF is both computationally efficient and scalable to large models. Extensive experiments across state-of-the-art LLMs demonstrate that ESF reliably detects tampering, such as fine-tuning, model compression, and backdoor injection, with a detection rate exceeding 99.2% using 5 fingerprint samples, thereby offering a robust solution for securing cloud-based AI systems.
The Lessons of Developing Process Reward Models in Mathematical Reasoning
Authors: Zhenru Zhang; Chujie Zheng; Yangzhen Wu; Beichen Zhang; Runji Lin; Bowen Yu; Dayiheng Liu; Jingren Zhou; Junyang Lin
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Process Reward Models (PRMs) aim to identify and mitigate intermediate errors in the reasoning processes in mathematical reasoning of Large Language Models (LLMs).However, the development of effective PRMs faces significant challenges, particularly in data annotation and evaluation methodologies.In this paper, through extensive experiments, we demonstrate that commonly used Monte Carlo (MC) estimation-based data synthesis for PRMs typically yields inferior performance and generalization compared to LLM-as-a-judge and human annotation methods.Furthermore, we identify potential biases in conventional Best-of-N (BoN) evaluation strategies for PRMs.To address these challenges, we develop a consensus filtering mechanism that effectively integrates MC estimation with LLM-as-a-judge and advocates a more comprehensive evaluation framework that combines response-level and step-level metrics. Based on the mechanisms, we significantly improve both model performance and data efficiency in the BoN evaluation and the step-wise error identification task.Finally, we release a new state-of-the-art PRM that outperforms existing open-source alternatives and provides practical guidelines for future research.