GenAI4ELab/papercli-papers huggingface dataset:
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts
Authors: Qing Wang; Xue Han; Jiahui Wang; Lehao Xing; Qian Hu; Lianlian Zhang; Chao Deng; Junlan Feng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Despite LLMs’ excellent code creation capabilities, multilingual code generation remains extremely challenging. To address this, we intent to improve the multi-programming-lingual (MultiPL) performance of the base LLMs while retaining the most popular ones using restricted computational resources. We consider MultiPL to be a special case of multiple natural languages and propose a MultiPL extension of LLMs utilizing a hybrid mixture of experts (MoE), called MultiPL-MoE. Specifically, MultiPL-MoE combines two paired MoEs to optimize expert selection at both the token and segment levels. The **token-level MoE** is a standard upcycling MoE structure with a shared expert and a novel gate weight normalization approach that aids in the final fusion with the segment-level MoE. The **segment-level MoE** incorporates two innovative designs to better capture the syntactic structure and contextual patterns of programming languages: First, using a sliding window to partition the input token sequence into multiple segments; Then, adopting an expert-choice routing strategy that allows experts to select the top-k segments. The results of the experiment proved the effectiveness of MultiPL-MoE.
AutoSpec: An Agentic Framework for Automatically Drafting Patent Specification
Authors: Ryan Shea; Zhou Yu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Patents play a critical role in driving technological innovation by granting inventors exclusive rights to their inventions. However the process of drafting a patent application is often expensive and time-consuming, making it a prime candidate for automation. Despite recent advancements in language models, several challenges hinder the development of robust automated patent drafting systems. First, the information within a patent application is highly confidential, which often prevents the use of closed-source LLMs for automating this task. Second, the process of drafting a patent application is difficult for even the most advanced language models due to their long context, technical writing style, and specialized domain knowledge. To address these challenges, we introduce AutoSpec, a secure, agentic framework for Automatically drafting patent Specification. Our approach decomposes the drafting process into a sequence of manageable subtasks, each solvable by smaller, open-source language models enhanced with custom tools tailored for drafting patent specification. To assess our system, we design a novel evaluation protocol in collaboration with experienced patent attorneys. Our automatic and expert evaluations show that AutoSpec outperforms existing baselines on a patent drafting task.
LimaCost: Data Valuation for Instruction Tuning of Large Language Models
Authors: Hyeonseok Moon; Jaehyung Seo; Seonmin Koo; Jinsung Kim; Young-kyoung Ham; Jiwon Moon; Heuiseok Lim
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Instruction tuning (IT) is an effective approach for aligning large language models (LLMs) with human intentions. There is ongoing discourse regarding the data quality for IT. As an effort to find the robust criteria of data quality for IT, we introduce LimaCost, a data quality measure that exhibits a strong correlation with model performance. LimaCost utilizes LIMA dataset, which effectiveness in IT has already been validated by several previous works. LimaCost then estimates the value of a given data by estimating how many LIMA data points might be needed to approximate its gradient. Our experiments reveal that LimaCost enables effective data selection that derive high alignment performance. We demonstrate that selecting data based on high LimaCost proves to be more effective than existing data selection strategies.
Two Challenges, One Solution: Robust Multimodal Learning through Dynamic Modality Recognition and Enhancement
Authors: Lanxin Bi; Yunqi Zhang; Luyi Wang; Yake Niu; Hui Zhao
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multimodal machine learning is often hindered by two critical challenges: modality missingness and modality imbalance. These challenges significantly degrade the performance of multimodal models. The majority of existing methods either require the availability of full-modality data during the training phase or necessitate explicit annotations to detect missing modalities. These dependencies severely limit the models’ applicability in the real world. To tackle these problems, we propose a Dynamic modality Recognition and Enhancement for Adaptive Multimodal fusion framework *DREAM*. Within DREAM, we innovatively employ a sample-level dynamic modality assessment mechanism to direct selective reconstruction of missing or underperforming modalities. Additionally, we introduce a soft masking fusion strategy that adaptively integrates different modalities according to their estimated contributions, enabling more accurate and robust predictions. Experimental results on three benchmark datasets consistently demonstrate that DREAM outperforms several representative baseline and state-of-the-art models, marking its robustness against modality missingness and imbalanced modality.
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
Authors: Yuhan Kang; Yang Shi; Mei Wen; Jun He; Jianchao Yang; Zeyu Xue; Jing Feng; Xinwang Liu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Post-training pruning, as one of the key techniques for compressing large language models (LLMs), plays a vital role in lightweight model deployment and model sparsity. However, current mainstream pruning methods dependent on the Hessian matrix face significant limitations in both pruning speed and practical effectiveness due to the computationally intensive nature of second-order derivative calculations. This paper presents SwiftPrune, a novel Hessian-free weight pruning method that achieves hardware-efficient model compression through two key innovations: 1) SwiftPrune eliminates the need for computationally intensive Hessian matrix calculations by introducing a contribution-based weight metric, which evaluates the importance of weights without relying on second-order derivatives. 2) we employ the Exponentially Weighted Moving Average (EWMA) technique to bypass weight sorting, enabling the selection of weights that contribute most to LLM accuracy and further reducing time complexity. Our approach is extended to support structured sparsity pruning, facilitating efficient execution on modern hardware accelerators. We validate the SwiftPrune on three LLMs (namely LLaMA2, LLaMA3, and Pythia), demonstrating that it significantly enhances compression performance. The experimental findings reveal that SwiftPrune completes the pruning process within seconds, achieving an average speedup of 12.29x (up to 56.02x) over existing SOTA approaches.
Training LLMs for Optimization Modeling via Iterative Data Synthesis and Structured Validation
Authors: Yang Wu; Yifan Zhang; Yurong Wu; Yuran Wang; Junkai Zhang; Jian Cheng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have revolutionized various domains but encounter substantial challenges in tackling optimization modeling tasks for Operations Research (OR), particularly when dealing with complex problem. In this work, we propose Step-Opt-Instruct, a framework that augments existing datasets and generates high-quality fine-tuning data tailored to optimization modeling. Step-Opt-Instruct employs iterative problem generation to systematically increase problem complexity and stepwise validation to rigorously verify data, preventing error propagation and ensuring the quality of the generated dataset. Leveraging this framework, we fine-tune open-source LLMs, including LLaMA-3-8B and Mistral-7B, to develop Step-Opt—a model that achieves state-of-the-art performance on benchmarks such as NL4OPT, MAMO, and IndustryOR. Extensive experiments demonstrate the superior performance of Step-Opt, especially in addressing complex OR tasks, with a notable 17.01% improvement in micro average accuracy on difficult problems. These findings highlight the effectiveness of combining structured validation with gradual problem refinement to advance the automation of decision-making processes using LLMs. The code and dataset are available at https://github.com/samwu-learn/Step.
Exploiting Prompt-induced Confidence for Black-Box Attacks on LLMs
Authors: Meina Chen; Yihong Tang; Kehai Chen
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) are vulnerable to adversarial attacks even in strict black-box settings with only hard-label feedback.Existing attacks suffer from inefficient search due to lack of informative signals such as logits or probabilities. In this work, we propose Prompt-Guided Ensemble Attack (PGEA), a novel black-box framework that leverages prompt-induced confidence, which reflects variations in a model’s self-assessed certainty across different prompt templates, as an auxiliary signal to guide attacks. We first demonstrate that confidence estimates vary significantly with prompt phrasing despite unchanged predictions. We then integrate these confidence signals in a two-stage attack: (1) estimating token-level vulnerability via confidence elicitation, and (2) applying ensemble word-level substitutions guided by these estimates. Experiments on LLaMA-3-8B-Instruct and Mistral-7B-Instruct-v0.3 on three classification tasks show that PGEA improves the attack success rate and query efficiency while maintaining semantic fidelity. Our results highlight that verbalized confidence, even without access to probabilities, is a valuable and underexplored signal for black-box adversarial attacks. The code is available at https://github.com/cmn-bits/PGEA-main.
DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment
Authors: Wei Huang; Anda Cheng; Zhao Zhang; Yinggui Wang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Current open-source training pipelines for Chinese medical language models predominantly emphasize optimizing training methodologies to enhance the performance of large language models (LLMs), yet lack comprehensive exploration into training data processing. To address this gap, we propose DPF-CM, a holistic Data Processing Framework for Chinese Medical LLMs training and deployment. DPF-CM comprises two core modules. The first module is a data processing pipeline tailored for model training. Beyond standard data processing operations, we (1) introduce a chained examples context-learning strategy to generate question-oriented instructions to mitigate the lack of instruction content, and (2) implement an ensemble-based filtering mechanism for preference data curation that averages multiple reward models to suppress noisy samples. The second module focuses on privacy preservation during model deployment. To prevent privacy risks from the inadvertent exposure of training data, we propose a Privacy Preserving Vector Database (PPVD) approach, which involves model memory search, high-risk database construction, secure database construction, and match-and-replace, four key stages to minimize privacy leakage during inference collectively. Experimental results show that DPF-CM significantly improves model accuracy, enabling our trained Chinese medical LLM to achieve state-of-the-art performance among open-source counterparts. Moreover, the framework reduces training data privacy leakage by 27%.
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward
Authors: Han Weng; Puzhen Wu; Cui Longjie; Yi Zhan; Boyi Liu; Yuanfeng Song; Dun Zeng; Yingxiang Yang; Qianru Zhang; Dong Huang; Xiaoming Yin; Yang Sun; Xing Chen
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Reinforcement learning (RL) has been widely adopted to enhance the performance of large language models (LLMs) on Text-to-SQL tasks. However, existing methods often rely on execution-based or LLM-based Bradley–Terry reward models. The former suffers from high execution latency caused by repeated database calls, whereas the latter imposes substantial GPU memory overhead, both of which significantly hinder the efficiency and scalability of RL pipelines. To this end, we propose a novel reward model framework for RL-based Text-to-SQL named Graph-Reward-SQL, which employs the GMNScore outcome reward model. We leverage SQL graph representations to provide accurate reward signals while significantly reducing time cost and GPU memory usage. Building on this foundation, we further introduce StepRTM, a stepwise reward model that provides intermediate supervision over Common Table Expression (CTE) subqueries. This encourages both functional correctness and readability of SQL. Extensive comparative and ablation experiments on standard benchmarks, including Spider and BIRD, demonstrate that our method consistently outperforms existing reward models.
StatsChartMWP: A Dataset for Evaluating Multimodal Mathematical Reasoning Abilities on Math Word Problems with Statistical Charts
Authors: Dan Zhu; Tianqiao Liu; Zitao Liu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent advancements in Large Multimodal Models (LMMs) have showcased their impressive capabilities in mathematical reasoning tasks in visual contexts. As a step toward developing AI models to conduct rigorous multi-step multimodal reasoning, we introduce StatsChartMWP, a real-world educational dataset for evaluating visual mathematical reasoning abilities on math word problems (MWPs) with statistical charts. Our dataset contains 8,514 chart-based MWPs, meticulously curated by K-12 educators within real-world teaching scenarios. We provide detailed preprocessing steps and manual annotations to help evaluate state-of-the-art models on StatsChartMWP. Comparing baselines, we find that current models struggle in undertaking meticulous multi-step mathematical reasoning among technical languages, diagrams, tables, and equations. Towards alleviate this gap, we introduce CoTAR, a chain-of-thought (CoT) augmented reasoning solution that fine-tunes the LMMs with solution-oriented CoT-alike reasoning steps. The LMM trained with CoTAR is more effective than current open-source approaches. We conclude by shedding lights on challenges and opportunities in enhancement in LMMs and steer future research and development efforts in the realm of statistical chart comprehension and analysis. The code and data are available at https://github.com/ai4ed/StatsChartMWP.
Logic-Thinker: Teaching Large Language Models to Think more Logically.
Authors: Chengyao Wen; Qiang Cheng; Shaofei Wang; Zhizhen Liu; Deng Zhao; Lei Liang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) before arriving at a final conclusion. Despite remarkable breakthroughs in complex reasoning capabilities, LongCoT still faces challenges such as redundancy and logical incoherence. To address these issues, we aim to equip large language models (LLMs) with rigorous and concise logical reasoning capabilities. In this work, we propose Logic-Thinker, a neural-symbolic reasoning framework that employs symbolic solvers to precisely solve problems and transforms their internal solving processes into concise and rigorous chains of thought, referred to as ThinkerCoT. Our experimental results demonstrate that Logic-Thinker achieves state-of-the-art performance in logical reasoning problems. Additionally, LLMs fine-tuned with ThinkerCoT outperform models distilled from QwQ32B on logic reasoning tasks, achieving an overall accuracy improvement of 3.6% while reducing token output by 73%-91%. Furthermore, ThinkerCoT enhances the comprehensive reasoning capabilities of LLMs, as evidenced by performance improvements on reasoning benchmarks such as GPQA and AIME.
ACEBench: A Comprehensive Evaluation of LLM Tool Usage
Authors: Chen Chen; Xinlong Hao; Weiwen Liu; Xu Huang; Xingshan Zeng; Shuai Yu; Dexun Li; Yuefeng Huang; Xiangcheng Liu; Wang Xinzhi; Wu Liu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex problems. However, existing benchmarks for evaluating LLMs’ tool usage face several limitations: (1) limited evaluation scenarios, often lacking assessments in real multi-turn dialogue contexts; (2) narrow evaluation dimensions, with insufficient detailed assessments of how LLMs use tools; and (3) reliance on LLMs or real API executions for evaluation, which introduces significant overhead. To address these challenges, we introduce ACEBench, a comprehensive benchmark for assessing tool usage in LLMs. ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. “Normal” evaluates tool usage in basic scenarios; “Special” evaluates tool usage in situations with ambiguous or incomplete instructions; “Agent” evaluates tool usage through multi-agent interactions to simulate real-world, multi-turn dialogues. We conducted extensive experiments using ACEBench, analyzing various LLMs in-depth and providing a more granular examination of error causes across different data types.
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis
Authors: Xue Tan; Hao Luan; Mingyu Luo; Xiaoyan Sun; Ping Chen; Jun Dai
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database, enabling them to produce responses that are more accurate and contextually appropriate. It is worth noting that the knowledge database, being sourced from publicly available channels such as Wikipedia, inevitably introduces a new attack surface. RAG poisoning attack involves injecting malicious texts into the knowledge database, ultimately leading to the generation of the attacker’s target response (also called poisoned response). However, there are currently limited methods available for detecting such poisoning attacks. We aim to bridge the gap in this work by introducing RevPRAG, a flexible and automated detection pipeline that leverages the activations of LLMs for poisoned response detection. Our investigation uncovers distinct patterns in LLMs’ activations when generating poisoned responses versus correct responses. Our results on multiple benchmarks and RAG architectures show our approach can achieve a 98% true positive rate, while maintaining a false positive rate close to 1%.
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
Authors: Wei Huang; Huang Wei; Yinggui Wang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. With many open-source LLMs available, selecting the best model for fine-tuning downstream tasks is challenging, primarily focusing on how to quickly identify the optimal LLM. We introduce a Data and Model Compression Framework (DaMoC) that addresses this challenge by: 1) Data Level: A systematic categorization of data filtering methodologies for LLMs is first established, classifying them into three distinct paradigms: (1) distribution-aware methods, (2) quality-aware methods, and (3) hybrid approaches considering both dimensions. Further, we enhance the density of key tokens in the text achieving token compression. Subsequently, we use an LLM to iterative rewrite the text to optimize its expression. 2) Model Level: We use layer similarity scores to assess each layer’s importance and remove those with lower importance. Then, we introduce a sparse merging paradigm to preserve as much of the original model’s capability as possible. Extensive experiments on four datasets, medical Q&A, financial Q&A, general Q&A, and reading comprehension, show that we can select the optimal LLM while saving approximately 20-fold in training time.
CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning
Authors: Jianfeng Pan; Senyou Deng; Shaomang Huang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Research on LLM technologies is rapidly emerging, with most of them employ a ‘fast thinking’ approach to inference. Most LLMs generate the final result based solely on a single query and LLM’s reasoning capabilities. However, with the advent of OpenAI-o1, ‘slow thinking’ techniques have garnered increasing attention because its process is closer to the human thought process. Inspired by the human ability to constantly associate and replenish knowledge during thinking, we developed the novel Chain-of-Associated-Thoughts (CoAT) framework, which introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed ‘associative memory’. By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time. This allows the framework to not only revisit and refine earlier inferences but also adaptively incorporate evolving information, ensuring that the final output is both accurate and comprehensive. We validate CoAT’s effectiveness across a variety of generative and reasoning tasks. Quantitative experiments show that CoAT achieves over 10% performance improvement on open-source multi-hop reasoning datasets (HotpotQA, MuSiQue) and more than 15% gain on our proprietary CRB dataset.