GenAI4ELab/papercli-papers huggingface dataset:
MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering
Authors: Siyue Zhang; Yuxiang Xue; Yiming Zhang; Xiaobao Wu; Anh Tuan Luu; Chen Zhao
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Understanding temporal concepts and answering time-sensitive questions is crucial yet a challenging task for question-answering systems powered by large language models (LLMs). Existing approaches either update the parametric knowledge of LLMs with new facts, which is resource-intensive and often impractical, or integrate LLMs with external knowledge retrieval (i.e., retrieval-augmented generation). However, off-the-shelf retrievers often struggle to identify relevant documents that require intensive temporal reasoning. To systematically study time-sensitive question answering, we introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating complex temporal perturbations and gold evidence labels. As anticipated, all existing retrieval methods struggle with these temporal reasoning-intensive questions. We further propose Modular Retrieval (MRAG), a trainless framework that includes three modules: (1) Question Processing that decomposes question into a main content and a temporal constraint; (2) Retrieval and Summarization that retrieves, splits, and summarize evidence passages based on the main content; (3) Semantic-Temporal Hybrid Ranking that scores semantic and temporal relevance separately for each fine-grained evidence. On TempRAGEval, MRAG significantly outperforms baseline retrievers in retrieval performance, leading to further improvements in final answer accuracy.
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models
Authors: Feiyang Li; Peng Fang; Zhan Shi; Arijit Khan; Fang Wang; Weihao Wang; Zhangxin-hw; Cui Yongjian
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Chain-of-thought (CoT) reasoning boosts large language models’ (LLMs) performance on complex tasks but faces two key limitations: a lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference process, also known as the inference logic of LLMs. To address these issues, we propose CoT-RAG, a novel reasoning framework with three key designs: (i) Knowledge Graph-driven CoT Generation, featuring knowledge graphs to modulate reasoning chain generation of LLMs, thereby enhancing reasoning credibility; (ii) Learnable Knowledge Case-aware RAG, which incorporates retrieval-augmented generation (RAG) into knowledge graphs to retrieve relevant sub-cases and sub-descriptions, providing LLMs with learnable information; (iii) Pseudo-Program Prompting Execution, which promotes greater logical rigor by guiding LLMs to execute reasoning tasks as pseudo-programs. Evaluations on nine public datasets spanning three reasoning tasks reveal significant accuracy gains—ranging from 4.0% to 44.3%–over state-of-the-art methods. Furthermore, tests on four domain-specific datasets demonstrate exceptional accuracy and efficient execution, underscoring its practical applicability and scalability. Our code and data are available at https://github.com/hustlfy123/CoT-RAG.
TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data
Authors: Changjiang Jiang; Fengchang Yu; Haihua Chen; Wei Lu; Jin Zeng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited numerical capabilities. To address these issues, we propose TabDSR, a three-agent framework consisting of: (1) a query decomposer that breaks down complex questions, (2) a table sanitizer that cleans and filters noisy tables, and (3) a program-of-thoughts (PoT)-based reasoner that generates executable code to derive the final answer from the sanitized table. To ensure unbiased evaluation and mitigate data leakage, we introduce a new dataset, CalTab151, specifically designed for complex numerical reasoning over tables. Experimental results demonstrate that TabDSR consistently outperforms existing methods, achieving state-of-the-art (SOTA) performance with 8.79%, 6.08%, and 19.87% accuracy improvement on TAT-QA, TableBench, and CalTab151, respectively. Moreover, our framework integrates seamlessly with mainstream LLMs, providing a robust solution for complex tabular numerical reasoning. These findings highlight the effectiveness of our framework in enhancing LLM performance for complex tabular numerical reasoning.
Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision
Authors: Dawei Zhu; Xiyu Wei; Guangxiang Zhao; Wenhao Wu; Haosheng Zou; Junfeng Ran; XWang; Lin Sun; Xiangzheng Zhang; Sujian Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks, where models need to reason over extensive input contexts to aggregate target information. While Chain-of-Thought (CoT) prompting has shown promise for multi-step reasoning, its effectiveness for long-context scenarios remains underexplored. Through systematic investigation across diverse tasks, we demonstrate that CoT’s benefits generalize across most long-context scenarios and amplify with increasing context length. Motivated by this, we propose a process-supervised framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance. Our framework incorporates a self-sampling mechanism to bootstrap reasoning paths and a novel quality assessment protocol specifically designed for long-context scenarios. This protocol evaluates both answer correctness and process reliability, with the latter decomposed into source faithfulness and intrinsic consistency components for efficient and accurate assessment. Experimental results on various long-context benchmarks demonstrate the effectiveness of our approach, achieving significant improvements over outcome supervision baselines on both in-domain tasks (+13.6/+3.8 points for LLaMA/Qwen on MuSiQue) and cross-domain generalization (+9.3/+8.1 points on average across diverse QA tasks). Our code, data and trained models will be released upon acceptance.
Multimodal Document-level Triple Extraction via Dynamic Graph Enhancement and Relation-Aware Reflection
Authors: Xiang Li; Runhai Jiao; Zhou Changyu; Shoupeng Qiao; Ruojiao Qiao; Ruifan Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multimodal documents, which are among the most prevalent data formats, combine a large amount of textual and visual content. Extracting structured triples knowledge from these documents is a highly valuable task, aimed at helping users efficiently acquire key entities and their relationships. However, existing methods face limitations in simultaneously processing long textual content and multiple associated images for triple extraction. Therefore, we propose a Multimodal Document-level Triple Extraction (MDocTE) framework. Specifically, we introduce a dynamic document graph construction method that extends the model’s scope to the entire document and the external world, while adaptively optimizing the graph structure. Next, we inject the global information and external knowledge learned by the graph neural network into the large language model, generating structured triples after deep interaction. Finally, we design a multimodal relation-aware mechanism and loss function to guide the model in reflecting on the shared information between text and visuals. We release a new triple extraction dataset for multimodal documents and conduct extensive experiments. The results demonstrate that the proposed framework outperforms the state-of-the-art baselines, thus filling the gap in multimodal document extraction. Our data is available at https://github.com/XiangLiphd/Triple-extraction-dataset-for-multimodal-documents.
Distill Visual Chart Reasoning Ability from LLMs to MLLMs
Authors: Wei He; Zhiheng Xi; Wanxu Zhao; Xiaoran Fan; Yiwen Ding; Zifei Shan; Tao Gui; Qi Zhang; Xuanjing Huang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs), including recognizing key information from visual inputs and conducting reasoning over it. While fine-tuning MLLMs for reasoning is critical, collecting and annotating charts and questions is expensive, hard to scale, and often results in low-quality annotations. To address this, we propose Code-as-Intermediary Translation (CIT), a cost-effective, efficient and scalable data synthesis method for distilling visual reasoning abilities from LLMs to MLLMs. The code serves as an intermediary that translates visual chart representations into textual representations, enabling language models to understand cross-modal information and generate reasoning chains accordingly. In this way, we can employ text-based synthesizing techniques to expand chart-plotting code and generate high-quality Q&A pairs for training models. This produces ReachQA, a dataset containing 3k reasoning-intensive charts and 20k Q&A pairs to enhance both recognition and reasoning abilities of MLLMs. Experiments show that models fine-tuned with ReachQA not only perform well on chart-related tasks but also show performance gains on general reasoning benchmarks.
FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture
Authors: Minghao Hu; Junzhe Wang; Weisen Zhao; Qiang Zeng; Lannan Luo
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures (ISAs). It is thus important to extend malware detection capacity to multiple ISAs. However, training a deep learning-based malware detection model usually requires a large number of labeled malware samples. The process of collecting and labeling sufficient malware samples to build datasets for each ISA is labor-intensive and time-consuming. To reduce the burden of data collection, we propose to leverage the ideas of Neural Machine Translation (NMT) and Normalizing Flows (NFs) for malware detection. Specifically, when dealing with malware in a certain ISA, we translate it to an ISA with sufficient malware samples (like X86-64). This allows us to apply a model trained on one ISA to analyze malware from another ISA. Our approach reduces the data collection effort by enabling malware detection across multiple ISAs using a model trained on a single ISA.
AdaTP: Attention-Debiased Token Pruning for Video Large Language Models
Authors: Fengyuan Sun; Leqi Shen; Hui Chen; Sicheng Zhao; Jungong Han; Guiguang Ding
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Video Large Language Models (Video LLMs) have achieved remarkable results in video understanding tasks. However, they often suffer from heavy computational overhead due to the large number of visual tokens generated from multiple video frames. Existing visual token compression methods often rely on attention scores from language models as guidance. However, these scores exhibit inherent biases: global bias reflects a tendency to focus on the two ends of the visual token sequence, while local bias leads to an over-concentration on the same spatial positions across different frames. To address the issue of attention bias, we propose Attention-Debiased Token Pruning for Video Large Language Models(AdaTP), a novel token pruning pipeline for Video LLMs. AdaTP integrates two dedicated debiasing modules into the pipeline, targeting global attention bias and local attention bias, respectively. Without the need for additional training, our method significantly reduces the computational overhead of Video LLMs while retaining the performance of vanilla models. Extensive evaluation shows that AdaTP achieves state-of-the-art performance in various commonly used video understanding benchmarks. In particular, on LLaVA-OneVision-7B, AdaTP maintains performance without degradation while using only up to 27.3% FLOPs compared to the vanilla model. Our code will be released soon.
AdaptFlow: Adaptive Workflow Optimization via Meta-Learning
Authors: Runchuan Zhu; Bowen Jiang; Lingrui Mei; Fangkai Yang; Lu Wang; Haoxiang Gao; Fengshuo Bai; Pu Zhao; Qingwei Lin; Saravan Rajmohan; Dongmei Zhang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent advances in large language models (LLMs) have sparked growing interest in agentic workflows—structured sequences of LLM invocations designed to solve complex tasks. However, existing approaches often rely on static templates or manually designed workflows, which limit adaptability to diverse tasks and hinder scalability. We propose AdaptFlow, a natural language-based meta-learning framework inspired by model-agnostic meta-learning (MAML). AdaptFlow uses a bi-level optimization process: the inner loop performs task-specific adaptation via LLM-generated feedback, while the outer loop consolidates these refinements into a shared, generalizable initialization. Evaluated across question answering, code generation, and mathematical reasoning benchmarks, AdaptFlow consistently outperforms both manually crafted and automatically searched baselines, achieving state-of-the-art results with strong generalization across tasks and models.
LMUNIT: Fine-grained Evaluation with Natural Language Unit Tests
Authors: Jon Saad-Falcon; Rajan Pathe Vivek; William Berrios; Nandita Shankar Naik; Matija Franklin; Bertie Vidgen; Amanpreet Singh; Douwe Kiela; Shikib Mehri
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge – human evaluation is costly and noisy, while automated metrics provide only coarse, difficult-to-interpret signals. We introduce natural language unit tests, a paradigm that decomposes response quality into explicit, testable criteria, along with a unified scoring model, LMUnit, which combines multi-objective training across preferences, direct ratings, and natural language rationales. Through controlled human studies, we show this paradigm significantly improves inter-annotator agreement and enables more effective LLM development workflows. LMUnit achieves state-of-the-art performance on evaluation benchmarks including FLASK, BigGenBench, and RewardBench 2, while maintaining competitive results on the original RewardBench. These results validate both our proposed paradigm and scoring model, suggesting a promising path forward for language model evaluation and development. Our code has been released at github.com/ContextualAI/LMUnit with an MIT license.
ThinkAnswer Loss: Balancing Semantic Similarity and Exact Matching for LLM Reasoning Enhancement
Authors: Shan Yang; Kun Wu; Zeju Li; Linlin Zhang; Xiangyu Pei; Leike An; Yu Liu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Knowledge distillation for large language models often uses Chain-of-Thought (CoT) and answer pairs, but existing methods struggle with appropriate supervision signals. Uniform constraints (e.g., cross-entropy) on CoT can enforce literal, verbose reasoning and suppress expressive diversity, while solely semantic constraints on answers can reduce accuracy in classification tasks. This paper proposes ThinkAnswer Loss, an information-theoretic differential supervision framework that decouples CoT and answer supervision. ThinkAnswer Loss applies semantic similarity constraints to the CoT portion while maintaining strict literal matching for the answer. We theoretically demonstrate its connection to mutual information maximization and derive a tight upper bound on generalization error. Experimental validation on text quality assessment and mathematical reasoning tasks shows that our method maintains answer accuracy while effectively reducing CoT length and preserving semantic content, thereby accelerating inference.
Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models
Authors: Jinwen Chen; Hainan Zhang; Fei Sun; Qinnan Zhang; Sijia Wen; Ziwei Wang; Zhiming Zheng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Stealthy data poisoning during fine-tuning can backdoor large language models (LLMs), threatening downstream safety. Existing detectors either use classifier-style probability signals—ill-suited to generation—or rely on rewriting, which can degrade quality and even introduce new triggers. We address the practical need to efficiently remove poisoned examples before or during fine-tuning. We observe a robust signal in the response space: after applying TF-IDF to model responses, poisoned examples form compact clusters (driven by consistent malicious outputs), while clean examples remain dispersed. We leverage this with RFTC—Reference-Filtration + TF-IDF Clustering. RFTC first compares each example’s response with that of a reference model and flags those with large deviations as suspicious; it then performs TF-IDF clustering on the suspicious set and identifies true poisoned examples using intra-class distance. On two machine translation datasets and one QA dataset, RFTC outperforms prior detectors in both detection accuracy and the downstream performance of the fine-tuned models. Ablations with different reference models further validate the effectiveness and robustness of Reference-Filtration.
Rust-doctor: Enhanced Feature for Rust Ownership and Lifetime Repair with Balanced Training Data Generation
Authors: Wenzhang Yang; Xiaoning Ren; Cuifeng Gao; Yinxing Xue
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: As a relatively new programming language, Rust has gained significant popularity in recent years due to its safety features during compilation. However, Rust developers often face challenges stemming from its strict compilation checks due to the steep learning curve of safety rules. To make matters worse, the lack of training data and the unique semantics of Rust lead to poor performance in learning-based automated program repair techniques. To address these challenges, we propose a novel error injection approach to generate a balanced training dataset and leverage the Mid-level Intermediate Representation (MIR) as enhanced features for Rust’s unique compilation error repair. Using these innovations, we fine-tuned a new code model, LLaRRA: Large Language and Rust Repair Assistant. Experimental results demonstrate that LLaRRA significantly outperforms state-of-the-art models in terms of Pass@K and Acc@K.
SLIM: Subtrajectory-Level Elimination for More Effective Reasoning
Authors: Xifeng Yao; Chengyuan Ma; Dongyu Lang; Yinhao Ni; Zhiwei Xu; Huarui Xie; Zihao Chen; Guang Shen; Dandan Tu; Yi Bai; Changzheng Zhang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In recent months, substantial progress has been made in complex reasoning of Large Language Models (LLMs), particularly through the application of test-time scaling. Notable examples include, though are not limited to, OpenAI’s o1/o3/o4 series and DeepSeek-R1. When responding to a query, these models generate an extended reasoning trajectory, during which the model explores, reflects, backtracks, and self-verifies before arriving at a conclusion. However, fine-tuning models with such reasoning trajectories may not always be optimal. Our findings indicate that not all components within these reasoning trajectories contribute positively to the reasoning process; in fact, some components may affect the overall performance negatively. In this study, we divide a reasoning trajectory into individual subtrajectories and develop a “5+2” framework to: (1) systematically identify suboptimal subtrajectories within the reasoning trajectory based on five human-established criteria; (2) assess the independence of the suboptimal subtrajectories identified in (1) from the subsequent content, ensuring that their elimination does not compromise overall flow and coherence of the reasoning process. Additionally, a sampling algorithm, built upon the “5+2” framework, is employed to select data whose reasoning process is free from suboptimal subtrajectories to the highest degree. Experimental results demonstrate that our method can reduce the number of suboptimal subtrajectories by 25.9% during the inference. Furthermore, our method achieves an average accuracy of 58.92% on highly challenging AIME24, AIME25, AMC24 and MATH500 benchmarks with only two thirds of training data, surpassing the average accuracy of 58.06% achieved with the entire data, and outperforming open-source datasets, including s1K-1.1, Light-R1-SFT-stage-1, OpenR1-Math-94k, and OpenThoughts-114k, when fine-tuning Qwen2.5-Math-7B. Finally, we have validated the efficacy of our method under resource-constrained scenarios, where it exhibits performance improvements across different maximum inference token limits: 2k, 4k, 8k, and 16k tokens.
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning
Authors: Zihan Chen; Song Wang; Xingbo Fu; Chengshuai Shi; Zhenyu Lei; Cong Shen; Jundong Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. However, collecting high-quality examples for new or challenging tasks can be costly and labor-intensive. In this work, we propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling. Our approach first leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances. We then introduce a graph-based label propagation method that spreads label information to the remaining target examples without additional LLM queries. The resulting fully pseudo-labeled dataset is used to construct in-task demonstrations for ICL. This pipeline combines the flexibility of cross-task supervision with the scalability of LLM-free propagation. Experiments across five tasks demonstrate that our method achieves strong performance while lowering labeling costs.