GenAI4ELab/papercli-papers huggingface dataset:
SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
Authors: Rui Qi; Zhibo Man; Yufeng Chen; Fengran Mo; Jinan Xu; Kaiyu Huang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successfully transferred to non-high-resource languages due to resource constraints, which struggles with multilingual reasoning tasks. To this end, we propose Structured-of-Thought (SoT), a training-free method that improves the performance on multilingual reasoning through a multi-step transformation: Language Thinking Transformation and Structured Knowledge Transformation. The SoT method converts language-specific semantic information into language-agnostic structured representations, enabling the models to understand the query in different languages more sophisticated. Besides, SoT effectively guides LLMs toward more concentrated reasoning to maintain consistent underlying reasoning pathways when handling cross-lingual variations in expression. Experimental results demonstrate that SoT outperforms several strong baselines on multiple multilingual reasoning benchmarks when adapting to various backbones of LLMs. It can also be integrated with other training-free strategies for further improvements. Our code is available at https://github.com/Cherry-qwq/SoT.
How Reliable is Multilingual LLM-as-a-Judge?
Authors: Xiyan Fu; Wei Liu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: LLM-as-a-Judge has emerged as a popular evaluation strategy, where advanced large language models assess generation results in alignment with human instructions. While these models serve as a promising alternative to human annotators, their reliability in multilingual evaluation remains uncertain. To bridge this gap, we conduct a comprehensive analysis of multilingual LLM-as-a-Judge. Specifically, we evaluate five models from different model families across five diverse tasks involving 25 languages. Our findings reveal that LLMs struggle to achieve consistent judgment results across languages, with an average Fleiss’ Kappa of approximately 0.3, and some models performing even worse. To investigate the cause of inconsistency, we analyze various influencing factors. We observe that consistency varies significantly across languages, with particularly poor performance in low-resource languages. Additionally, we find that neither training on multilingual data nor increasing model scale directly improves judgment consistency. These findings suggest that LLMs are not yet reliable for evaluating multilingual predictions. Our work provides valuable insights into the limitations of multilingual LLM-as-a-Judge, and sheds light on future research.
Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation
Authors: Qingsong Wang; Tao Wu; Wang Lin; Yueying Feng; Gongsheng Yuan; Chang Yao; Jingyuan Chen
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have demonstrated strong performance in open-ended generation tasks. However, they often struggle to adapt content to users with differing cognitive capacities, leading to a phenomenon we term cognitive misalignment. This issue arises in two forms: knowledge-level misalignment, where content is too complex or too simplistic relative to user understanding, and presentation style misalignment, where the structure or tone hinders effective comprehension. To address these challenges, we propose the Cognitive-Level Alignment Framework (CLAF), a general-purpose generation framework that aligns both knowledge complexity and presentation style with user cognition. CLAF integrates a capability-aware retrieval module based on a hierarchical knowledge graph and a style optimization module guided by Bloom’s taxonomy and preference learning. Additionally, a knowledge-controllable generation component ensures consistency and relevance throughout the output. To support training and evaluation, we construct Scale, a cognitively annotated dataset containing responses at multiple comprehension levels per query. Empirical results show that CLAF enhances the adaptability and informativeness of LLM outputs across a range of user profiles, offering a robust solution to cognitive-level alignment in real-world applications.
Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs
Authors: Essa Jan; Moiz Ali; Muhammad Saram Hassan; Muhammad Fareed Zaffar; Yasir Zaki
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: As the knowledge of large language models (LLMs) becomes outdated over time, there is a growing need for efficient methods to update them, especially when injecting proprietary information. Our study reveals that comprehension-intensive fine-tuning tasks (e.g., question answering and blanks) achieve substantially higher knowledge retention rates (48%) compared to mapping-oriented tasks like translation (17%) or text-to-JSON conversion (20%), despite exposure to identical factual content. We demonstrate that this pattern persists across model architectures and follows scaling laws, with larger models showing improved retention across all task types. However, all models exhibit significant performance drops when applying injected knowledge in broader contexts, suggesting limited semantic integration. These findings show the importance of task selection in updating LLM knowledge, showing that effective knowledge injection relies not just on data exposure but on the depth of cognitive engagement during fine-tuning.
INDOORWORLD : Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
Authors: Dekun Wu; Frederik Brudy; Bang Liu; Yi Wang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Virtual environments are essential to AI agent research. Existing environments for LLM agent research typically focus on either physical task solving or social simulation, with the former oversimplifying agent individuality and social dynamics, and the latter lacking physical grounding of social behaviors. We introduce IndoorWorld, a heterogeneous multi-agent environment that tightly integrates physical and social dynamics. By introducing novel challenges for LLM-driven agents in orchestrating social dynamics to influence physical environments and anchoring social interactions within world states, IndoorWorld opens up possibilities of LLM-based building occupant simulation for architectural design. We demonstrate the potential with a series of experiments within an office setting to examine the impact of multi-agent collaboration, resource competition, and spatial layout on agent behavior.
ARXSA: A General Negative Feedback Control Theory in Vision-Language Models
Authors: Zeyu Zhang; Tianqi Chen; Yuki Todo
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The Transformer model has been increasingly applied across various domains, driven by the self-attention mechanism, which offers robust data processing capabilities and has substantially contributed to the advancement of the model. In the self-attention mechanism, three core matrices from the same data batch are computed together to determine correlations between input elements. Drawing inspiration from the efficiency and stability conferred by negative feedback structures in predictive control systems, the concept of vertical training was introduced to integrate data from multiple batches. Accordingly, this paper proposes an autoregressive with exogenous inputs (ARX) approach for the self-attention mechanism, transforming the Encoder block into a negative feedback predictive control system. A network architecture based on this method is also proposed, enabling the autoregressive with exogenous inputs for self-attention to transmit data from batches at previous time points. The effectiveness of the proposed approach is validated through comparative experimental evaluations.
Breaking the Attention Trap in Code LLMs: A Rejection Sampling Approach to Enhance Code Execution Prediction
Authors: Xingcheng Ruan; Haoxiang Geng; Yunhui Xia; Bingran Zhao
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Code-specific Large Language Models (Code LLMs) have greatly improved performance across code-related tasks, offering substantial benefits in practical applications. However, existing research reveals significant performance bottlenecks in Code Execution tasks, which requires models to predict the execution results of given code snippets. This study identifies that, the Attention Trap phenomenon in training data constitutes a key constraint on model performance. To address this phenomenon, we propose the Attention Cracking with Rejection Sampling (AC-RS) method. The method first applies structural optimization to training data to eliminate attention traps. Then, it conducts secondary training on the outputs generated by the fine-tuned model to mitigate potential negative impacts from manual data intervention. Experimental results show that AC-RS significantly enhances the accuracy of Code Execution while preserving models’ original capabilities. Notably, the optimized 7B model achieves Code Execution accuracy comparable to 32B model and GPT-4o.
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation
Authors: Shijie Zhang; Renhao Li; Songsheng Wang; Philipp Koehn; Min Yang; Derek F. Wong
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The advancement of Large Language Models (LLMs) enables flexible and interpretable automatic evaluations. In the field of machine translation evaluation, utilizing LLMs with translation error annotations based on Multidimensional Quality Metrics (MQM) yields more human-aligned judgments. However, current LLM-based evaluation methods still face challenges in accurately identifying error spans and assessing their severity. In this paper, we propose HiMATE, a Hierarchical Multi-Agent Framework for Machine Translation Evaluation. We argue that existing approaches inadequately exploit the fine-grained structural and semantic information within the MQM hierarchy. To address this, we develop a hierarchical multi-agent system grounded in the MQM error typology, enabling granular evaluation of subtype errors. Two key strategies are incorporated to further mitigate systemic hallucinations within the framework: the utilization of the model’s self-reflective capability and the facilitation of agent discussion involving asymmetric information. Empirically, HiMATE outperforms competitive baselines across different datasets in conducting human-aligned evaluations. Further analyses underscore its significant advantage in error span detection and severity assessment, achieving an average F1-score improvement of 89% over the best-performing baseline. We make our code and data publicly available at https://github.com/nlp2ct-shijie/HiMATE.
ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments
Authors: Gili Lior; Eliya Habba; Shahar Levy; Avi Caciularu; Gabriel Stanovsky
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: LLMs are highly sensitive to prompt phrasing, yet standard benchmarks typically report performance using a single prompt, raising concerns about the reliability of such evaluations. In this work, we argue for a stochastic method of moments evaluation over the space of meaning-preserving prompt perturbations. We introduce a formal definition of *reliable evaluation* that accounts for prompt sensitivity, and suggest ReliableEval - a method for estimating the number of prompt resamplings needed to obtain meaningful results. Using our framework, we stochastically evaluate five frontier LLMs and find that even top-performing models like GPT-4o and Claude-3.7-Sonnet exhibit substantial prompt sensitivity. Our approach is model-, task-, and metric-agnostic, offering a recipe for meaningful and robust LLM evaluation.
From Characters to Tokens: Dynamic Grouping with Hierarchical BPE
Authors: Rares Dolga; Lucas Maystre; Tudor Berariu; David Barber
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Subword tokenization methods like Byte Pair Encoding (BPE) are widely used in large language models due to their balance of vocabulary compactness and representational power. However, they suffer from inefficiencies in representing rare words and require large embedding matrices. Character-level models address these issues but introduce performance bottlenecks, particularly in Transformer-based architectures. Recent hierarchical models attempt to merge the benefits of both paradigms by grouping characters into patches, but existing patching strategies either rely on whitespace—limiting applicability to certain languages—or require auxiliary models that introduce new dependencies. In this paper, we propose a dynamic character grouping method that leverages the structure of existing BPE tokenization without requiring additional models. By appending explicit end-of-patch markers to BPE tokens and introducing a second-level BPE compression stage to control patch granularity, our method offers efficient, flexible, and language-agnostic representations. Empirical results demonstrate that our approach matches or exceeds the performance of dynamic entropy- and whitespace-based patching strategies, while maintaining a compact vocabulary.
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant
Authors: Lei Shen; Xiaoyu Shen
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In recent years, multi-agent frameworks powered by large language models (LLMs) have advanced rapidly. Despite this progress, there is still a notable absence of benchmark datasets specifically tailored to evaluate their performance. To bridge this gap, we introduce Auto-SLURP, a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks in the context of smart personal assistants. Auto-SLURP extends the original SLURP dataset—initially developed for natural language understanding tasks—by relabeling the data and integrating simulated servers and external services. This enhancement enables a comprehensive end-to-end evaluation pipeline, covering language understanding, task execution, and response generation. Our experiments demonstrate that Auto-SLURP presents a significant challenge for current state-of-the-art frameworks, highlighting that truly reliable and intelligent multi-agent personal assistants remain a work in progress.
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings
Authors: Or Shachar; Uri Katz; Yoav Goldberg; Oren Glickman
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We present NER Retriever, a zero-shot retrieval framework for ad-hoc Named Entity Recognition (NER), where a user-defined type description is used to retrieve documents mentioning entities of that type. Instead of relying on fixed schemas or fine-tuned models, our method builds on pretrained language models (LLMs) to embed both entity mentions and type descriptions into a shared semantic space. We show that internal representations—specifically, the value vectors from mid-layer transformer blocks—encode fine-grained type information more effectively than commonly used top-layer embeddings. To refine these representations, we train a lightweight contrastive projection network that aligns type-compatible entities while separating unrelated types. The resulting entity embeddings are compact, type-aware, and well-suited for nearest-neighbor search. Evaluated on three benchmarks, NER Retriever significantly outperforms both lexical (BM25) and dense (sentence-level) retrieval baselines, particularly in low-context settings. Our findings provide empirical support for representation selection within LLMs and demonstrate a practical solution for scalable, schema-free entity retrieval.
MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters
Authors: Rrubaa Panchendrarajan; Rubén Míguez Pérez; Arkaitz Zubiaga
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In the context of fact-checking, claims are often repeated across various platforms and in different languages, which can benefit from a process that reduces this redundancy. While retrieving previously fact-checked claims has been investigated as a solution, the growing number of unverified claims and expanding size of fact-checked databases calls for alternative, more efficient solutions. A promising solution is to group claims that discuss the same underlying facts into clusters to improve claim retrieval and validation. However, research on claim clustering is hindered by the lack of suitable datasets. To bridge this gap, we introduce MultiClaimNet, a collection of three multilingual claim cluster datasets containing claims in 86 languages across diverse topics. Claim clusters are formed automatically from claim-matching pairs with limited manual intervention. We leverage two existing claim-matching datasets to form the smaller datasets within MultiClaimNet. To build the larger dataset, we propose and validate an approach involving retrieval of approximate nearest neighbors to form candidate claim pairs and an automated annotation of claim similarity using large language models. This larger dataset contains 85.3K fact-checked claims written in 78 languages. We further conduct extensive experiments using various clustering techniques and sentence embedding models to establish baseline performance. Our datasets and findings provide a strong foundation for scalable claim clustering, contributing to efficient fact-checking pipelines.
DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path
Authors: Yongqiang Liu; Qiyao Peng; Binrong Liu; Hongtao Liu; XueWei Li; Wenjun Wang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) excel in natural language tasks, with Chain-of-Thought (CoT) prompting enhancing reasoning through step-by-step decomposition. However, CoT struggles in knowledge-intensive tasks with multiple entities and implicit multi-hop relations, failing to connect entities systematically in zero-shot settings. Existing knowledge graph methods, limited by static structures, lack adaptability in complex scenarios. We propose DS-MHP, a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. DS-MHP operates in three stages: 1) constructing query-specific subgraphs by extracting entities and relations; 2) generating and refining multi-hop paths using a hybrid strategy of Breadth-First Search, greedy expansion, and LLM supplementation; and 3) guiding LLMs with subgraphs and paths, aggregating answers via majority voting. Evaluated on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning, DS-MHP outperforms baselines and state-of-the-art methods in nearly all benchmarks. It achieves overall average accuracy increases of 3.9% on Mistral-7B and 3.6% on GPT-3.5 Turbo compared to SOTA, with significant gains in logical and symbolic reasoning. Additionally, DS-MHP reduces runtime and LLM calls compared to SOTA, enhancing computational efficiency. These improvements demonstrate DS-MHP’s superior reasoning accuracy, explainability, and efficiency in complex multi-entity tasks.
LongTail-Swap: benchmarking language models’ abilities on rare words
Authors: Robin Algayres; Charles-Éric Saint-James; Mahi Luthra; Jiayi Shen; Youssef Benchekroun; Dongyan Lin; Rashel Moritz; Juan Pino; Emmanuel Dupoux
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Children learn to speak with a low amount of data and can be taught new words on a few-shot basis, making them particularly data-efficient learners. The BabyLM challenge aims at exploring language model (LM) training in the low-data regime but uses metrics that concentrate on the head of the word distribution. Here, we introduce LongTail-Swap (LT-Swap), a benchmark that focuses on the tail of the distribution, i.e., measures the ability of LMs to learn new words with very little exposure, like infants do. LT-Swap is a pretraining corpus-specific test set of acceptable versus unacceptable sentence pairs that isolate semantic and syntactic usage of rare words. Models are evaluated in a zero-shot fashion by computing the average log probabilities over the two members of each pair.We built two such test sets associated with the 10M words and 100M words BabyLM training sets, respectively, and evaluated 16 models from the BabyLM leaderboard. Our results not only highlight the poor performance of language models on rare words but also reveal that performance differences across LM architectures are much more pronounced in the long tail than in the head. This offers new insights into which architectures are better at handling rare word generalization. We’ve also made the code publicly available on GitHub, enabling the generation of LT-Swap benchmarks based on any English text corpus.
TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis
Authors: Xiang Li; Xianfu Cheng; Dezhuang Miao; Xiaoming Zhang; Zhoujun Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multimodal Sentiment Analysis (MSA) with missing modalities has attracted increasing attention recently. While current Transformer-based methods leverage dense text information to maintain model robustness, their quadratic complexity hinders efficient long-range modeling and multimodal fusion. To this end, we propose a novel and efficient Text-enhanced Fusion Mamba (TF-Mamba) framework for robust MSA with missing modalities. Specifically, a Text-aware Modality Enhancement (TME) module aligns and enriches non-text modalities, while reconstructing the missing text semantics. Moreover, we develop Text-based Context Mamba (TC-Mamba) to capture intra-modal contextual dependencies under text collaboration. Finally, Text-guided Query Mamba (TQ-Mamba) queries text-guided multimodal information and learns joint representations for sentiment prediction. Extensive experiments on three MSA datasets demonstrate the effectiveness and efficiency of the proposed method under missing modality scenarios. Code is available at https://github.com/codemous/TF-Mamba.