GenAI4ELab/papercli-papers huggingface dataset:
GAttention: Gated Attention for the Detection of Abusive Language
Authors: Horacio Jarquín Vásquez; Hugo Jair Escalante; Manuel Montes; Mario Ezra Aragon
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues. This paper introduces GAttention, a novel Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task. GAttention capitalizes on local and global query vectors by integrating the internal relationships within a sequence (Self-attention) and the global relationships among distinct sequences (Contextual attention). This combination allows for a more nuanced understanding and processing of sequence elements, which is particularly beneficial in context-sensitive text classification tasks such as the case of abusive language detection. By applying this mechanism to transformer-based encoder models, we showcase how it enhances the model’s ability to discern subtle nuances and contextual clues essential for identifying abusive language, a challenging and increasingly relevant NLP task.
Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback
Authors: Chu Fei Luo; Samuel Dahan; Xiaodan Zhu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: As language models have a greater impact on society, it is important to ensure they are aligned to a diverse range of perspectives and are able to reflect nuance in human values. However, the most popular training paradigms for modern language models often assume there is one optimal answer for every query, leading to generic responses and poor alignment. In this work, we aim to enhance pluralistic alignment of language models in a low-resource setting with two methods: pluralistic decoding and model steering. We empirically demonstrate that model steering offers consistent improvement over zero-shot and few-shot baselines with only 50 annotated samples. Our proposed methods decrease false positives in several high-stakes tasks such as hate speech detection and misinformation detection, and improves the distributional alignment to human values from different demographics. We hope our work highlights the importance of diversity and how language models can be adapted to consider nuanced perspectives.
ProtoXTM: Cross-Lingual Topic Modeling with Document-Level Prototype-based Contrastive Learning
Authors: Seung-Won Seo; Soon-Sun Kwon
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Cross-lingual topic modeling (CLTM) is an essential task in the field of data mining and natural language processing, aiming to extract aligned and semantically coherent topics from bilingual corpora. Recent advances in cross-lingual neural topic models have widely leveraged bilingual dictionaries to achieve word-level topic alignment. However, two critical challenges remain in cross-lingual topic modeling, the topic mismatch issue and the degeneration of intra-lingual topic interpretability. Due to linguistic diversity, some translated word pairs may not represent semantically coherent topics despite being lexical equivalents, and the objective of cross-lingual topic alignment in CLTM can consequently degrade topic interpretability within intra languages. To address these issues, we propose a novel document-level prototype-based contrastive learning paradigm for cross-lingual topic modeling. Additionally, we design a retrieval-based positive sampling strategy for contrastive learning without data augmentation. Furthermore, we introduce ProtoXTM, a cross-lingual neural topic model based on document-level prototype-based contrastive learning. Extensive experiments indicate that our approach achieves state-of-the-art performance on cross-lingual and mono-lingual benchmarks, demonstrating enhanced topic interpretability.
One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning
Authors: Mengyu Wang; Sotirios Sabanis; Miguel de Carvalho; Shay B. Cohen; Tiejun Ma
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (QA). In this work, we propose Expert Question Decomposition (EQD), an approach designed to balance the use of domain knowledge with computational efficiency. EQD is built on a two-step fine-tuning framework and guided by a reward function that measures the effectiveness of generated sub-questions in improving QA outcomes. It requires only a few thousand training examples and a single A100 GPU for fine-tuning, with inference time comparable to zero-shot prompting. Beyond its efficiency, EQD outperforms state-of-the-art domain-tuned models and advanced prompting strategies. We evaluate EQD in the financial domain, characterized by specialized knowledge and complex quantitative reasoning, across four benchmark datasets. Our method consistently improves QA performance by 0.6% to 10.5% across different LLMs. Our analysis reveals an important insight: in domain-specific QA, a single supporting question often provides greater benefit than detailed guidance steps.
When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs
Authors: Mikhail Seleznyov; Mikhail Chaichuk; Gleb Ershov; Alexander Panchenko; Elena Tutubalina; Oleg Somov
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 4 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural Instructions dataset. Our evaluation covers robustness methods from both fine-tuned and in-context learning paradigms, and tests their generalization against multiple types of distribution shifts. Finally, we extend our analysis to GPT-4.1 and DeepSeek V3 to assess frontier models’ current robustness to format perturbations. Our findings offer actionable insights into the relative effectiveness of these robustness methods, enabling practitioners to make informed decisions when aiming for stable and reliable LLM performance in real-world applications. Code: tthttps://github.com/AIRI-Institute/when-punctuation-matters.
RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval
Authors: Kaishuai Xu; Wenjun Hou; Yi Cheng; Wenjie Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have shown promising performance on diverse medical benchmarks, highlighting their potential in supporting real-world clinical tasks. Retrieval-Augmented Generation (RAG) has emerged as a key approach for mitigating knowledge gaps and hallucinations by incorporating external medical information. However, RAG still struggles with complex medical questions that require intensive reasoning, as surface-level input often fails to reflect the true knowledge needs of the task. Existing methods typically focus on refining queries without explicitly modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. In this work, we propose RAR2, a joint learning framework that improves both Reasoning-Augmented Retrieval and Retrieval-Augmented Reasoning. RAR2 constructs a thought process to uncover implicit knowledge requirements and uses it to guide retrieval and answer generation. We build a training dataset of mixed preference pairs and apply Direct Preference Optimization (DPO) to train the model. Moreover, we design two test-time scaling strategies to explore the boundaries of our framework. Experiments demonstrate the effectiveness of RAR2 across several biomedical question answering datasets, outperforming RAG baselines with or without fine-tuning.
The Security Threat of Compressed Projectors in Large Vision-Language Models
Authors: Yudong Zhang; Ruobing Xie; Xingwu Sun; Jiansheng Chen; Zhanhui Kang; Di Wang; Yu Wang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The choice of a suitable visual language projector (VLP) is critical to the successful training of large visual language models (LVLMs). Mainstream VLPs can be broadly categorized into compressed and uncompressed projectors, and each offers distinct advantages in performance and computational efficiency. However, their security implications have not been thoroughly examined. Our comprehensive evaluation reveals significant differences in their security profiles: compressed projectors exhibit substantial vulnerabilities, allowing adversaries to successfully compromise LVLMs even with minimal knowledge of structure information. In stark contrast, uncompressed projectors demonstrate robust security properties and do not introduce additional vulnerabilities. These findings provide critical guidance for researchers in selecting optimal VLPs that enhance the security and reliability of visual language models. The code is available at https://github.com/btzyd/TCP.
NarratEX Dataset: Explaining the Dominant Narratives in News Texts
Authors: Nuno Guimarães; Purificação Silvano; Ricardo Campos; Alipio Jorge; Ana Filipa Pacheco; Dimitar Iliyanov Dimitrov; Nikolaos Nikolaidis; Roman Yangarber; Elisa Sartori; Nicolas Stefanovitch; Preslav Nakov; Jakub Piskorski; Giovanni Da San Martino
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.
Radical Allomorphy: Phonological Surface Forms without Phonology
Authors: Salam Khalifa; Nizar Habash; Owen Rambow
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent computational work typically frames morphophonology as generating surface forms (SFs) from abstract underlying representations (URs) by applying phonological rules or constraints. This generative stance presupposes that every morpheme has a well-defined UR from which all allomorphs can be derived, a theory-laden assumption that is expensive to annotate, especially in low-resource settings.We adopt an alternative view. Allomorphs and their phonological variants are treated as the basic, observed lexicon, not as outputs of abstract URs. The modeling task therefore shifts from deriving SFs to selecting the correct SF, given a meaning and a phonological context. This discriminative formulation removes the need to posit or label URs and lets the model exploit the surface evidence directly.
Model Calibration for Emotion Detection
Authors: Mihaela Petre-Vlad; Cornelia Caragea; Florentina Hristea
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In this paper, we propose a unified approach to model calibration for emotion detection that exploits the complementary strengths of knowledge distillation and the MixUp data augmentation technique to enhance the trustworthiness of emotion detection models. Specifically, we use a MixUp method informed by training dynamics that generates augmented data by interpolating easy-to-learn with ambiguous samples based on their similarity and dissimilarity provided by saliency maps. We use this MixUp method to calibrate the teacher model in the first generation of the knowledge distillation process. To further calibrate the teacher models in each generation, we employ dynamic temperature scaling to update the temperature used for scaling the teacher predictions. We find that calibrating the teachers with our method also improves the calibration of the student models. We test our proposed method both in-distribution (ID) and out-of-distribution (OOD). To obtain better OOD performance, we further fine-tune our models with a simple MixUp method that interpolates a small number of OOD samples with ambiguous ID samples.
From Benchmark to Better Embeddings: Leveraging Synonym Substitution to Enhance Multimodal Models in Ukrainian
Authors: Volodymyr Mudryi; Yurii Laba
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We study the robustness of text–image retrieval for Ukrainian under synonym-substitution attacks (SSA). On Multi30K with OpenCLIP, we evaluate two SSA methods: dictionary-based and LLM-based, and find Ukrainian degrades far more than English (e.g., GPT-4o SSA drops HIT@1 from 32.1 → 10.9 vs. 41.6 → 30.4). We introduce a Hybrid method that filters dictionary candidates with an LLM to preserve sense and grammar, yielding higher-quality perturbations (Ukrainian HIT@1 16.8 vs. 7.6/10.9). To mitigate this problem, we propose synonym-augmented fine-tuning, injecting one-word substitutions into training; it boosts robustness (Hybrid 28.1, GPT-4o 25.1) without harming original performance. This is the first systematic SSA evaluation for Ukrainian multimodal retrieval and a practical recipe for improving models in low-resource, morphologically rich languages. We release code, prompts, and trained checkpoints at https://github.com/YuriiLaba/UA-B2BE.
Context Copying Modulation: The Role of Entropy Neurons in Managing Parametric and Contextual Knowledge Conflicts
Authors: Zineddine Tighidet; Andrea Mogini; Hedi Ben younes; Jiali Mei; Patrick Gallinari; Benjamin Piwowarski
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The behavior of Large Language Models (LLMs) when facing contextual information that conflicts with their internal parametric knowledge is inconsistent, with no generally accepted explanation for the expected outcome distribution. Recent work has identified in autoregressive transformer models a class of neurons – called entropy neurons – that produce a significant effect on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens. In this paper, we investigate the preliminary claim that these neurons are involved in inhibiting context copying behavior in transformers by looking at their role in resolving conflicts between contextual and parametric information. We show that entropy neurons are responsible for suppressing context copying across a range of LLMs, and that ablating them leads to a significant change in the generation process. These results enhance our understanding of the internal dynamics of LLMs when handling conflicting information.
A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling
Authors: Shiyu Ji; Farnoosh Hashemi; Joice Chen; Juanwen Pan; Weicheng Ma; Hefan Zhang; Sophia Pan; Ming Cheng; Shubham Mohole; Saeed Hassanpour; Soroush Vosoughi; Michael Macy
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Rhetorical strategies are central to persuasive communication, from political discourse and marketing to legal argumentation. However, analysis of rhetorical strategies has been limited by reliance on human annotation, which is costly, inconsistent, difficult to scale. Their associated datasets are often limited to specific topics and strategies, posing challenges for robust model development. We propose a novel framework that leverages large language models (LLMs) to automatically generate and label synthetic debate data based on a four-part rhetorical typology (causal, empirical, emotional, moral). We fine-tune transformer-based classifiers on this LLM-labeled dataset and validate its performance against human-labeled data on this dataset and on multiple external corpora. Our model achieves high performance and strong generalization across topical domains. We illustrate two applications with the fine-tuned model: (1) the improvement in persuasiveness prediction from incorporating rhetorical strategy labels, and (2) analyzing temporal and partisan shifts in rhetorical strategies in U.S. Presidential debates (1960–2020), revealing increased use of affective over cognitive argument in U.S. Presidential debates.
SecDecoding: Steerable Decoding for Safer LLM Generation
Authors: Jiayou Wang; Rundong Liu; Yue Hu; Huijia Wu; Zhaofeng He
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) have achieved remarkable performance across diverse tasks, yet ensuring output safety remains a fundamental challenge. Existing defense methods often suffer from limited generalization, high computational overhead, or significant utility degradation. In this work, we present SecDecoding, a lightweight decoding-time defense framework that significantly improves output safety without compromising model helpfulness. SecDecoding leverages a pair of small contrastive models, namely a base model and a safety fine-tuned expert, to estimate token-level safety signals by measuring divergence in their output distributions. These signals dynamically steer the target model’s generation toward safer trajectories, effectively suppressing unsafe content. Experimental results show that SecDecoding achieves near-zero attack success rates against a wide spectrum of advanced jailbreak attacks across multiple LLMs, while maintaining the model’s helpfulness with minimal degradation. Additionally, SecDecoding is a modular and resource-efficient approach that requires only an auxiliary 1-billion-parameter model and is compatible with speculative decoding, offering up to 1.5× inference speedup.
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
Authors: Tuo Wang; Adithya Kulkarni; Tyler Cody; Peter A. Beling; Yujun Yan; Dawei Zhou
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semantic dependencies, relying on token-level probability measures that fail to capture structural relationships within the generated text. We propose GENUINE: Graph ENhanced mUlti-level uncertaINty Estimation for Large Language Models, a structure-aware framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification. By incorporating supervised learning, GENUINE effectively models semantic and structural relationships, improving confidence assessments. Extensive experiments across NLP tasks show that GENUINE achieves up to 29% higher AUROC than semantic entropy-based approaches and reduces calibration errors by over 15%, demonstrating the effectiveness of graph-based uncertainty modeling. The code is available at https://github.com/ODYSSEYWT/GUQ.
ReviewEval: An Evaluation Framework for AI-Generated Reviews
Authors: Madhav Krishan Garg; Tejash Prasad; Tanmay Singhal; Chhavi Kirtani; Murari Mandal; Dhruv Kumar
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: (1) ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and (2) ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78% and 47.62% over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97% and 12.73%, enhances adherence to guidelines by 10.11% and 47.26% respectively. This paper establishes essential metrics for AI-based peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.
Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic Select Algorithms
Authors: Abhinay Shankar Belde; Rohit Ramkumar; Jonathan Rusert
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Adversarial text attack research plays a crucial role in evaluating the robustness of NLP models. However, the increasing complexity of transformer-based architectures has dramatically raised the computational cost of attack testing, especially for researchers with limited resources (e.g., GPUs). Existing popular black-box attack methods often require a large number of queries, which can make them inefficient and impractical for researchers. To address these challenges, we propose two new attack selection strategies called Hybrid and Dynamic Select, which better combine the strengths of previous selection algorithms. Hybrid Select merges generalized BinarySelect techniques with GreedySelect by introducing a size threshold to decide which selection algorithm to use. Dynamic Select provides an alternative approach of combining the generalized Binary and GreedySelect by learning which lengths of texts each selection method should be applied to. This greatly reduces the number of queries needed while maintaining attack effectiveness (a limitation of BinarySelect). Across 4 datasets and 6 target models, our best method(sentence-level Hybrid Select) is able to reduce the number of required queries per attack up 25.82% on average against both encoder models and LLMs, without losing the effectiveness of the attack.