GenAI4ELab/papercli-papers huggingface dataset:
Temporal Consistency for LLM Reasoning Process Error Identification
Authors: Jiacheng Guo; Yue Wu; Jiahao Qiu; Kaixuan Huang; Xinzhe Juan; Ling Yang; Mengdi Wang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Verification is crucial for effective mathematical reasoning. We present a new temporal consistency method where verifiers iteratively refine their judgments based on the previous assessment. Unlike one-round verification or multi-model debate approaches, our method leverages consistency in a sequence of self-reflection actions to improve verification accuracy. Empirical evaluations across diverse mathematical process error identification benchmarks (Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements over baseline methods. When applied to the recent DeepSeek R1 distilled models, our method demonstrates strong performance, enabling 7B/8B distilled models to outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the distilled 14B model with our method achieves performance comparable to Deepseek-R1.
Quantifying Compositionality of Classic and State-of-the-Art Embeddings
Authors: Zhijin Guo; Chenhao Xue; Zhaozhen Xu; Hongbo Bo; Yuxuan Ye; Janet B. Pierrehumbert; Martha Lewis
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: For language models to generalize correctly to novel expressions, it is critical that they exploit access compositional meanings when this is justified. Even if we don’t know what a “pelp” is, we can use our knowledge of numbers to understand that “ten pelps” makes more pelps than “two pelps”. Static word embeddings such as Word2vec made strong, indeed excessive, claims about compositionality. The SOTA generative, transformer models and graph models, however, go too far in the other direction by providing no real limits on shifts in meaning due to context. To quantify the additive compositionality, we formalize a two-step, generalized evaluation that (i) measures the linearity between known entity attributes and their embeddings via canonical correlation analysis, and (ii) evaluates additive generalization by reconstructing embeddings for unseen attribute combinations and checking reconstruction metrics such as L2 loss, cosine similarity, and retrieval accuracy. These metrics also capture failure cases where linear composition breaks down. Sentences, knowledge graphs, and word embeddings are evaluated and tracked the compositionality across all layers and training stages. Stronger compositional signals are observed in later training stages across data modalities, and in deeper layers of the transformer-based model before a decline at the top layer. Code will be publicly available on GitHub upon acceptance.
Presumed Cultural Identity: How Names Shape LLM Responses
Authors: Siddhesh Milind Pawar; Arnav Arora; Lucie-Aimée Kaffee; Isabelle Augenstein
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Names are deeply tied to human identity - they can serve as markers of individuality, cultural heritage, and personal history. When interacting with LLMs, user names can enter chatbot conversations through direct user input (requested by chatbots), as part of task contexts such as CV reviews, or as built-in memory features that store user information for personalisation. In this work, we study name-based cultural bias by analyzing the adaptations that LLMs make when names are mentioned in the prompt. Our analyses demonstrate that LLMs exhibit significant cultural identity assumptions across multiple cultures based on users’ presumed backgrounds based on their names. We also show how using names as an indicator of identity can lead to misattribution and flattening of cultural identities. Our work has implications for designing more nuanced personalisation systems that avoid reinforcing stereotypes while maintaining meaningful customisation.
I-GUARD: Interpretability-Guided Parameter Optimization for Adversarial Defense
Authors: Mamta; Oana Cocarascu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Transformer-based models are highly vulnerable to adversarial attacks, where even small perturbations can cause significant misclassifications. This paper introduces *I-Guard*, a defense framework to increase the robustness of transformer-based models against adversarial perturbations. *I-Guard* leverages model interpretability to identify influential parameters responsible for adversarial misclassifications. By selectively fine-tuning a small fraction of model parameters, our approach effectively balances performance on both original and adversarial test sets. We conduct extensive experiments on English and code-mixed Hinglish datasets and demonstrate that *I-Guard* significantly improves model robustness. Furthermore, we demonstrate the transferability of *I-Guard* in handling other character-based perturbations.
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization
Authors: Chao Zhang; Xin Shi; Xueqiao Zhang; Yifan Zhu; Yi Yang; Yawei Luo
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent advances in Emotional Support Conversation (ESC) have improved emotional support generation by fine-tuning Large Language Models (LLMs) via Supervised Fine-Tuning (SFT). However, common psychological errors still persist. While Direct Preference Optimization (DPO) shows promise in reducing such errors through pairwise preference learning, its effectiveness in ESC tasks is limited by two key challenges: (1) Entangled data structure: Existing ESC data inherently entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs; and (2) Optimization ambiguity: Applying vanilla DPO to such entangled pairwise data leads to ambiguous training objectives. To address these issues, we introduce Inferential Preference Mining (IPM) to construct high-quality preference data, forming the IPM-PrefDial dataset. Building upon this data, we propose a Decoupled ESC framework inspired by Gross’s Extended Process Model of Emotion Regulation, which decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. Each was trained via SFT and subsequently enhanced by DPO to align with the psychological preference. Extensive experiments demonstrate that our Decoupled ESC framework outperforms baselines, reducing preference bias and improving response quality.
Local Normalization Distortion and the Thermodynamic Formalism of Decoding Strategies for Large Language Models
Authors: Tom Kempton; Stuart Burrell
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Advances in hardware and language model architecture have spurred a revolution in natural language generation. However, autoregressive models compute probability distributions over next-token choices, and sampling from these distributions, known as decoding, has received significantly less attention than other design choices. Existing decoding strategies are largely based on heuristics, resulting in methods that are difficult to apply or improve in a principled manner. We develop the theory of decoding strategies for language models by expressing popular decoding algorithms as equilibrium states in the language of ergodic theory and stating the objective functions they optimize. Using this, we analyze the effect of the local normalization step required to make probabilities sum to one in top-k, nucleus, and temperature sampling. We argue that local normalization distortion is a fundamental defect of decoding strategies and quantify the size of this distortion and its effect on mathematical proxies for the quality and diversity of generated text. This yields conclusions for the design of decoding algorithms and the detection of machine-generated text.
BRIT: Bidirectional Retrieval over Unified Image-Text Graph
Authors: Ainulla Khan; Moyuru Yamada; Srinidhi Akella
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising technique to enhance the quality and relevance of responses generated by large language models. While recent advancements have mainly focused on improving RAG for text-based queries, RAG on multi-modal documents containing both texts and images has not been fully explored. Especially when fine-tuning does not work. This paper proposes BRIT, a novel multi-modal RAG framework that effectively unifies various text-image connections in the document into a multi-modal graph and retrieves the texts and images as a query-specific sub-graph. By traversing both image-to-text and text-to-image paths in the graph, BRIT retrieve not only directly query-relevant images and texts but also further relevant contents to answering complex cross-modal multi-hop questions. To evaluate the effectiveness of BRIT, we introduce MM-RAG test set specifically designed for multi-modal question answering tasks that require to understand the text-image relations. Our comprehensive experiments demonstrate the superiority of BRIT, highlighting its ability to handle cross-modal questions on the multi-modal documents.
ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking
Authors: Boyoung Kim; Dosung Lee; Sumin An; Jinseong Jeong; Paul Hongsuck Seo
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent advances in question answering have led to substantial progress in tasks such as multi-hop reasoning. However, global sensemaking—answering questions by synthesizing information from an entire corpus—remains a significant challenge. A prior graph-basedapproach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs. To address these limitations, we propose ReTAG, a RetrievalEnhanced, Topic-Augmented Graph framework that constructs topic-specific subgraphs and retrieves the relevant summaries for response generation. Experiments show that ReTAG improves response quality while significantly reducing inference time compared to the baseline. Our code is available at https://github.com/bykimby/retag.
Capturing Latent Modal Association For Multimodal Entity Alignment
Authors: Yongquan Ji; Jingwei Cheng; Fu Zhang; Chenglong Lu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multimodal entity alignment aims to identify equivalent entities in heterogeneous knowledge graphs by leveraging complementary information from multiple modalities. However, existing methods often overlook the quality of input modality embeddings during modality interaction – such as missing modality generation, modal information transfer, modality fusion – which may inadvertently amplify noise propagation while suppressing discriminative feature representations. To address these issues, we propose a novel model – CLAMEA for capturing latent modal association for multimodal entity alignment. Specifically, we use a self- attention mechanism to enhance salient information while attenuating noise within individual modality embeddings. We design a dynamic modal attention flow fusion module to capture and balance latent intra- and inter-modal associations and generate fused modality embeddings. Based on both fused and available modalities, we adopt variational autoencoder (VAE) to generate high quality embeddings for the missing modality. We use a cross-modal association extraction module to extract latent modal associations from the completed modality embeddings, further enhancing embedding quality. Experimental results on two real-world datasets demonstrate the effectiveness of our approach, which achieves an absolute 3.1% higher Hits@ 1 score than the sota method.
Explaining novel senses using definition generation with open language models
Authors: Mariia Fedorova; Andrey Kutuzov; Francesco Periti; Yves Scherrer
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL’24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.
Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching
Authors: Seoyeon Kim; Huiseo Kim; Chanjun Park; Jinyoung Yeo; Dongha Lee
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. The limited resource for low-resource languages remains a crucial challenge. Code-switching (CS), a phenomenon where multilingual speakers alternate between languages in a discourse, can convey subtle cultural and linguistic nuances that can be otherwise lost in translation and elicits language-specific knowledge in human communications. In light of this, we investigate whether code-switching can activate, or identify and leverage knowledge for reasoning when LLMs solve low-resource language tasks. To facilitate the research, we first present EnKoQA, a synthetic English-Korean CS question-answering dataset. We provide comprehensive analysis on a variety of multilingual LLMs by subdividing activation process into knowledge identification and knowledge leveraging. Our results demonstrate that compared to English text, CS can faithfully activate knowledge inside LLMs especially on language-specific domains, suggesting the potential of code-switching on low-resource language tasks.
Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation
Authors: Armel Randy Zebaze; Benoît Sagot; Rachel Bawden
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. Machine Translation (MT) has been shown to benefit from in-context examples, in particular when they are semantically similar to the sentence to translate. In this paper, we propose a new LLM-based translation paradigm, compositional translation, to replace naive few-shot MT with similarity-based demonstrations. An LLM is used to decompose a sentence into simpler phrases, and then to translate each phrase with the help of retrieved demonstrations. Finally, the LLM is prompted to translate the initial sentence with the help of the self-generated phrase-translation pairs. Our intuition is that this approach should improve translation because these shorter phrases should be intrinsically easier to translate and easier to match with relevant examples. This is especially beneficial in low-resource scenarios, and more generally whenever the selection pool is small or out of domain. We show that compositional translation boosts LLM translation performance on a wide range of popular MT benchmarks, including FLORES200, NTREX 128 and TICO-19. Code and outputs will be made freely available.
TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation
Authors: Armel Randy Zebaze; Benoît Sagot; Rachel Bawden
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning, rivalling supervised models when translating into high-resource languages (HRLs). However, they lag behind when dealing with low-resource language (LRLs). Example selection via similarity search and supervised fine-tuning help. However the improvements they give are limited by the size, quality and diversity of existing parallel datasets. A common technique in low-resource MT is synthetic parallel data creation, the most frequent of which is backtranslation, whereby existing target-side texts are automatically translated into the source language. However, it also relies on the existence of good quality and relevant target-side texts, which are not readily available for many LRLs. In this paper, we present a new approach, TopXGen, which involves using an LLM to automatically generate topic-specific target-side data in the LRL, which can then be backtranslated to produce useful and diverse parallel texts for ICL and fine-tuning. Our intuition is that while LLMs struggle to translate into LRLs, their ability to translate well into HRLs and their multilinguality enable them to generate good quality, natural-sounding target-side texts, which can be translated well into a high-resource source language. We show that TopXGen boosts LLM translation performance during fine-tuning and in-context learning. Our code and outputs will be made freely available.
Fast, Not Fancy: Rethinking G2P with Rich Data and Statistical Models
Authors: Mahta Fetrat Qharabagh; Zahra Dehghanian; Hamid R. Rabiee
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Homograph disambiguation remains a significant challenge in grapheme-to-phoneme (G2P) conversion, especially for low-resource languages. This challenge is twofold: (1) creating balanced and comprehensive homograph datasets is labor-intensive and costly, and (2) specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications such as screen readers and other accessibility tools. In this paper, we address both issues. First, we propose a semi-automated pipeline for constructing homograph-focused datasets, introduce the HomoRich dataset generated through this pipeline, and demonstrate its effectiveness by applying it to enhance a state-of-the-art deep learning-based G2P system for Persian. Second, we advocate for a paradigm shift—utilizing rich offline datasets to inform the development of fast, statistical methods suitable for latency-sensitive accessibility applications like screen readers. To this end, we improve one of the most well-known rule-based G2P systems, eSpeak, into a fast homograph-aware version, HomoFast eSpeak. Our results show an approximate 30 percentage-point improvement in homograph disambiguation accuracy for the deep learning-based and eSpeak systems.
Personalized open world plan generation for safety-critical human centered autonomous systems: A case study on Artificial Pancreas
Authors: Ayan Banerjee; Sandeep Gupta
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Design-time safety guarantees for human-centered autonomous systems (HCAS) often break down in open-world deployment due to uncertain human interaction. In practice, HCAS must follow a user-personalized safety plan, with the human providing external inputs to handle out-of-distribution events. Open-world safety planning for HCAS demands modeling dynamical systems, exploring novel actions, and rapid replanning when plans are invalidated or dynamics shift. No single state-of-the-art planner meets all these needs. We introduce an LLM-based architecture that automatically generates personalized safety plans. By itself, the LLM fares poorly at producing safe usage plans, but coupling it with a safety verifier—which evaluates plan safety over the planning horizon and feeds back quality scores—enables the discovery of safe plans. Moreover, fine-tuning the LLM on personalized models inferred from open-world data further enhances plan quality. We validate our approach by generating safe usage plans for artificial pancreas systems in automated insulin delivery for Type 1 Diabetes patients. Code: https://github.com/ImpactLabASU/LLMOpen
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Authors: Emilio Villa-Cueva; Sholpan Bolatzhanova; Diana Turmakhan; Kareem Elzeky; Henok Biadglign Ademtew; Alham Fikri Aji; Vladimir Araujo; Israel Abebe Azime; Jinheon Baek; Frederico Belcavello; Fermin Cristobal; Jan Christian Blaise Cruz; Mary Dabre; Raj Dabre; Toqeer Ehsan; Naome A Etori; Fauzan Farooqui; Jiahui Geng; Guido Ivetta; Thanmay Jayakumar; Soyeong Jeong; Zheng Wei Lim; Aishik Mandal; Sofía Martinelli; Mihail Minkov Mihaylov; Daniil Orel; Aniket Pramanick; Sukannya Purkayastha; Israfel Salazar; Haiyue Song; Tiago Timponi Torrent; Debela Desalegn Yadeta; Injy Hamed; Atnafu Lambebo Tonja; Thamar Solorio
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations.
Training Text-to-Molecule Models with Context-Aware Tokenization
Authors: Seojin Kim; Hyeontae Song; Jaehyun Nam; Jinwoo Shin
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recently, text-to-molecule models have shown great potential across various chemical applications, e.g., drug-discovery. These models adapt language models to molecular data by representing molecules as sequences of atoms. However, they rely on atom-level tokenizations, which primarily focus on modeling local connectivity, thereby limiting the ability of models to capture the global structural context within molecules. To tackle this issue, we propose a novel text-to-molecule model, coined Context-Aware Molecular T5 (CAMT5). Inspired by the significance of the substructure-level contexts in understanding molecule structures, e.g., ring systems, we introduce substructure-level tokenization for text-to-molecule models. Building on our tokenization scheme, we develop an importance-based training strategy that prioritizes key substructures, enabling CAMT5 to better capture the molecular semantics. Extensive experiments verify the superiority of CAMT5 in various text-to-molecule generation tasks. Intriguingly, we find that CAMT5 outperforms the state-of-the-art methods using only 2% of training tokens. In addition, we propose a simple yet effective ensemble strategy that aggregates the outputs of text-to-molecule models to further boost the generation performance.