GenAI4ELab/papercli-papers huggingface dataset:
Improving MLLM’s Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
Authors: Yupu Liang; Yaping Zhang; Zhiyang Zhang; Zhiyuan Chen; Yang Zhao; Lu Xiang; Chengqing Zong; Yu Zhou
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges. Previous efforts to enhance DIMT capability through Supervised Fine-Tuning (SFT) on the DIMT dataset often result in the forgetting of the model’s existing monolingual abilities, such as OCR. To address these challenges, we introduce a novel fine-tuning paradigm, named Synchronously Self-Reviewing (SSR) its OCR proficiency, inspired by the concept “Bilingual Cognitive Advantage”. Specifically, SSR prompts the model to generate OCR text before producing translation text, which allows the model to leverage its strong monolingual OCR ability while learning to translate text across languages. Comprehensive experiments demonstrate the proposed SSR learning helps mitigate catastrophic forgetting, improving the generalization ability of MLLMs on both OCR and DIMT tasks. The code will be released upon acceptance.
HG-InsightLog: Context Prioritization and Reduction for Question Answering with Non-Natural Language Construct Log Data
Authors: Supriya Bajpai; Athira Gopal; Chandrakant Harjpal; Niraj Kumar
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Modern IT systems generate vast amounts of log data, which pose challenges for Large Language Models (LLMs) due to their large size, irrelevant entries, and non-Natural Language (non-NL) construct (e.g., domain-specific jargon, error codes, file paths, and abbreviations). Traditional methods like Retrieval-Augmented Generation (RAG) and GraphRAG fail to preserve temporal sequences, handle non-NL for context and entities extraction, and dynamically prioritize query-relevant context. To address these limitations, we propose HG-InsightLog, a novel framework that constructs a multi-entity temporal hypergraph representing log attribute-value pair as nodes and connecting them with hyperedges, capturing critical connections in the data. HG-InsightLog introduces a multi-step query personalization mechanism enhancing the Personalized PageRank algorithm to rank hyperedges based on query relevance and contextual centrality to priortize critical connections. Top ranked hyperedges are extracted and converted back into log formats preserving temporal order and reducing context. Experimental results across multiple datasets demonstrate its superiority over existing methods, enhancing factual, causal, and analytical reasoning. Our approach enables smaller LLMs like LLaMA-8B to perform effective log-based QA. Being model-agnostic and training-free, it scales with evolving open-source LLMs without relying on proprietary systems.
Dialect Normalization using Large Language Models and Morphological Rules
Authors: Antonios Dimakis; John Pavlopoulos; Antonios Anastasopoulos
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle this issue by transforming dialectal text so that it can be used by standard-language tools downstream. In this study, we tackle this task by introducing a new normalization method that combines rule-based linguistically informed transformations and large language models (LLMs) with targeted few-shot prompting, without requiring any parallel data. We implement our method for Greek dialects and apply it on a dataset of regional proverbs, evaluating the outputs using human annotators. We then use this dataset to conduct downstream experiments, finding that previous results regarding these proverbs relied solely on superficial linguistic information, including orthographic artifacts, while new observations can still be made through the remaining semantics.
USDC: A Dataset of  ̲User  ̲Stance and  ̲Dogmatism in Long  ̲Conversations
Authors: Mounika Marreddy; Subba Reddy Oota; Venkata Charan Chinni; Manish Gupta; Lucie Flek
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Analyzing user opinion changes in long conversation threads is extremely critical for applications like enhanced personalization, market research, political campaigns, customer service, targeted advertising, and content moderation. Unfortunately, previous studies on stance and dogmatism in user conversations have focused on training models using datasets annotated at the post level, treating each post as independent and randomly sampling posts from conversation threads. Hence, first, we build a dataset for studying user opinion fluctuations in 764 long multi-user Reddit conversation threads, called USDC. USDC contains annotations for 2 tasks: i) User Stance classification, which involves labeling a user’s stance in a post within a conversation on a five-point scale; ii) User Dogmatism classification, which involves labeling a user’s overall opinion in the conversation on a four-point scale. Besides being time-consuming and costly, manual annotations for USDC are challenging because: 1) Conversation threads could be very long, increasing the chances of noisy annotations; and 2) Interpreting instances where a user changes their opinion within a conversation is difficult because often such transitions are subtle and not expressed explicitly. Hence, we leverage majority voting on zero-shot, one-shot, and few-shot annotations from Mistral Large and GPT-4 to automate the annotation process. Human annotations on 200 test conversations achieved inter-annotator agreement scores of 0.49 for stance and 0.50 for dogmatism with these LLM annotations, indicating a reasonable level of consistency between human and LLM annotations. USDC is then used to finetune and instruction-tune multiple deployable small language models like LLaMA, Falcon and Vicuna for the stance and dogmatism classification tasks. We make the code and dataset publicly available [https://github.com/mounikamarreddy/USDC].
Learning to Insert [PAUSE] Tokens for Better Reasoning
Authors: Eunki Kim; Sangryul Kim; James Thorne
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: To enhance reasoning capabilities, previous works have explored incorporating special-purpose tokens into the training process. These strategies strengthen the learning mechanism of transformer-based large language models (LLMs). Building on prior research, in which inserting dummy tokens consecutively just before reasoning steps can enhance effectiveness, we introduce a novel approach termed Dynamic Inserting Tokens Training (DIT). Our method identifies positions within sequences where model confidence is lowest according to token log-likelihood. Strategically inserting [PAUSE] tokens on these positions bolsters the model’s predictive capabilities for subsequent tokens. Experimental results across diverse datasets and models, from the 2.7B model to the 8B model, demonstrate that DIT consistently outperforms traditional fine-tuning and previous token insertion methods. With this simple yet effective method, we achieve accuracy gains of up to 4.7%p on GSM8K, 3.23%p on AQUA-RAT, and pass@1 improvements of up to 3.4%p on MBPP datasets. Our work shows a model-based, dynamic approach rather than a heuristic one, thereby broadening the scope of research in reasoning.
Understand the Implication: Learning to Think for Pragmatic Understanding
Authors: Settaluri Lakshmi Sravanthi; Kishan Maharaj; Sravani Gunnu; Abhijit Mishra; Pushpak Bhattacharyya
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Pragmatics, the ability to infer meaning beyond literal interpretation, is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. To bridge this gap, we introduce a novel pragmatic dataset ImpliedMeaningPreference that includes explicit reasoning (‘thoughts’) for both correct and incorrect interpretations. Through preference-tuning and supervised fine-tuning, we demonstrate that thought-based learning significantly enhances LLMs’ pragmatic understanding, improving accuracy by 11.12% across model families. We further discuss a transfer-learning study where we evaluate the performance of thought-based training for the other tasks of pragmatics (presupposition, deixis) that are not seen during the training time and observe an improvement of 16.10% compared to label trained models.
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data
Authors: Xinyang Lu; Jingtan Wang; Zitong Zhao; Zhongxiang Dai; Chuan-Sheng Foo; See-Kiong Ng; Bryan Kian Hsiang Low
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The impressive performances of Large Language Models (LLMs) and their immense potential for commercialization have given rise to serious concerns over the Intellectual Property (IP) of their training data. In particular, the synthetic texts generated by LLMs may infringe the IP of the data being used to train the LLMs. To this end, it is imperative to be able to perform source attribution by identifying the data provider who contributed to the generation of a synthetic text by an LLM. In this paper, we show that this problem can be tackled by watermarking, i.e., by enabling an LLM to generate synthetic texts with embedded watermarks that contain information about their source(s). We identify the key properties of such watermarking frameworks (e.g., source attribution accuracy, robustness against adversaries), and propose a source attribution framework that satisfies these key properties due to our algorithmic designs. Our framework enables an LLM to learn an accurate mapping from the generated texts to data providers, which sets the foundation for effective source attribution. Extensive empirical evaluations show that our framework achieves effective source attribution.
Dense Retrieval with Quantity Comparison Intent
Authors: Prayas Agrawal; Nandeesh Kumar K M; Muthusamy Chelliah; Surender Kumar; Soumen Chakrabarti
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Pre-trained language models (PLMs) fragment numerals and units that express quantities in arbitrary ways, depending on their subword vocabulary. Consequently, they are unable to contextualize the fragment embeddings well enough to be proficient with dense retrieval in domains like e-commerce and finance. Arithmetic inequality constraints (“laptop under 2 lb”) offer additional challenges. In response, we propose DeepQuant, a dense retrieval system built around a dense multi-vector index, but carefully engineered to elicit and exploit quantities and associated comparison intents. A novel component of our relevance score compares two quantities with compatible units, conditioned on a proposed comparison operator. The uncertain extractions of numerals, units and comparators are marginalized in a suitable manner. On two public and one proprietary e-commerce benchmark, DeepQuant is both faster and more accurate than popular PLMs. It also beats several competitive sparse and dense retrieval systems that do not take special cognizance of quantities.
Reflection on Knowledge Graph for Large Language Models Reasoning
Authors: Yigeng Zhou; Wu Li; Yifan Lu; Jing Li; Fangming Liu; Meishan Zhang; Yequan Wang; Daojing He; Honghai Liu; Min Zhang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent research shows that supplementing Large Language Models (LLMs) with knowledge graphs can enhance their performance. However, existing methods often introduce noise in the retrieval and reasoning pipeline, hindering LLMs’ ability to effectively integrate external knowledge for complex multi-hop question answering. To address this, we propose RefKG, a novel framework designed to enhance the reasoning capabilities of LLMs through reflective engagement with knowledge graphs. RefKG autonomously conduct retrieval and reflection on knowledge graphs. It consists of three modules: Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. We also introduce a multi-task tuning strategy that not only integrates external knowledge into LLMs but also trains them to leverage this knowledge for answering questions. This significantly improves their performance on knowledge-intensive tasks. Experiments on fact verification and knowledge graph question answering demonstrate RefKG’s effectiveness.
Revisiting 3D LLM Benchmarks: Are We Really Testing 3D Capabilities?
Authors: Jiahe Jin; Yanheng He; Mingyan Yang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: In this work, we identify the “2D-Cheating” problem in 3D LLM evaluation, where these tasks might be easily solved by VLMs with rendered images of point clouds, exposing ineffective evaluation of 3D LLMs’ unique 3D capabilities. We test VLM performance across multiple 3D LLM benchmarks and, using this as a reference, propose principles for better assessing genuine 3D understanding. We also advocate explicitly separating 3D abilities from 1D or 2D aspects when evaluating 3D LLMs.
DIESEL: A Lightweight Inference-Time Safety Enhancement for Language Models
Authors: Ben Ganon; Alon Zolfi; Omer Hofman; Inderjeet Singh; Hisashi Kojima; Yuval Elovici; Asaf Shabtai
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large language models (LLMs) have demonstrated impressive performance across a wide range of tasks, including open-ended dialogue, driving advancements in virtual assistants and other interactive systems. However, these models often generate outputs misaligned with human values, such as ethical norms and safety constraints, resulting in potentially harmful or inappropriate responses. While several techniques have been proposed to address this problem, they typically involve computationally intensive training procedures or introduce substantial inference-time latency. In this paper, we present DIESEL, a lightweight inference-guidance technique that can be seamlessly integrated into any autoregressive LLM to semantically filter undesirable content during generation. DIESEL guides generation by reranking token candidates according to their semantic similarity to predefined negative concepts in the latent space. It can serve either as a standalone safeguard or as an auxiliary defense layer, enhancing response safety without requiring model fine-tuning or additional data. We demonstrate DIESEL’s effectiveness on state-of-the-art conversational models, including in adversarial jailbreak scenarios. Furthermore, we show that DIESEL generalizes beyond safety applications, enabling flexible and domain-specific response filtering.
Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience
Authors: Jiawei Gu; Ziting Xian; Yuanzhen Xie; Ye Liu; Enjie Liu; Ruichao Zhong; Mochi Gao; Yunzhi Tan; Bo Hu; Zang Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large language models (LLMs) achieve strong performance on plain text tasks but underperform on structured data like tables and databases. Potential challenges arise from their underexposure during pre-training and rigid text-to-structure transfer mechanisms. Unlike humans who seamlessly apply learned patterns across data modalities, LLMs struggle to infer implicit relationships embedded in tabular formats, especially in the absence of explicit structural guidance. To bridge this cognitive gap, we introduce Contrastive Retrieval-Augmented Generation on Experience (CoRE), a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL) to simulate human-like knowledge transfer. Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance, achieving average gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks. Our Monte Carlo Tree Search (MCTS)-generated Experience Memory expands training data 8-9×, enhancing diversity and domain coverage. This training-free and continual method propels LLMs toward structured knowledge expertise.
Structured Pruning for Diverse Best-of-N Reasoning Optimization
Authors: Hieu Trung Nguyen; Bao Nguyen; Viet Anh Nguyen
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Model pruning in transformer-based language models, traditionally seen as a means of computational savings, can enhance the model’s reasoning capabilities. In this work, we uncover the surprising phenomenon that the selective pruning of certain attention heads leads to improvements in reasoning performance, particularly on challenging tasks. Motivated by this observation, we propose SPRINT, a novel contrastive learning framework that dynamically selects the optimal head and layer to prune during inference. By aligning question embeddings with head embeddings, our approach identifies those pruned-head configurations that result in more accurate reasoning. Extensive experiments on the MATH dataset demonstrate that our method significantly outperforms traditional best-of-N and random head selection strategies on the MATH500 and GSM8K datasets.
PodAgent: A Comprehensive Framework for Podcast Generation
Authors: Yujia Xiao; Lei He; Haohan Guo; Feng-Long Xie; Tan Lee
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. The key challenges lie in in-depth content generation, appropriate and expressive voice production. This paper proposed PodAgent, a comprehensive framework for creating audio programs. PodAgent 1) generates informative topic-discussion content by designing a Host-Guest-Writer multi-agent collaboration system, 2) builds a voice pool for suitable voice-role matching and 3) utilizes LLM-enhanced speech synthesis method to generate expressive conversational speech. Given the absence of standardized evaluation criteria for podcast-like audio generation, we developed comprehensive assessment guidelines to effectively evaluate the model’s performance. Experimental results demonstrate PodAgent’s effectiveness, significantly surpassing direct GPT-4 generation in topic-discussion dialogue content, achieving an 87.4% voice-matching accuracy, and producing more expressive speech through LLM-guided synthesis. Demo page: https://podcast-agent.github.io/demo/. Source code: https://github.com/yujxx/PodAgent.
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
Authors: Wenhao Liu; Zhenyi Lu; Xinyu Hu; Jerry Zhang; Dailin Li; Jiacheng Cen; Huilin Cao; Haiteng Wang; Yuhan Li; Xie Kun; Dandan Li; Pei Zhang; Chengbo Zhang; Yuxiang Ren; Xiaohong Huang; Yan Ma
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: High-quality math datasets are crucial for advancing the reasoning abilities of large language models (LLMs). However, existing datasets often suffer from three key issues: outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation.To address these, we introduce STORM-BORN, an ultra-challenging dataset of mathematical derivations sourced from cutting-edge academic papers, which includes dense human-like approximations and heuristic cues.To ensure the reliability and quality, we propose a novel human-in-the-loop, multi-agent data generation framework, integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations. We curated a set of 2,000 synthetic samples and deliberately selected the 100 most difficult problems.Even most advanced models like GPT-o1 solved fewer than 5% of them. Fine-tuning on STORM-BORN boosts accuracy by 7.84% (LLaMA3-8B) and 9.12% (Qwen2.5-7B).As AI approaches mathematician-level reasoning, STORM-BORN provides both a high-difficulty benchmark and a human-like reasoning training resource. Our code and dataset are publicly available at https://github.com/lwhere/STORM-BORN.
iMOVE : Instance-Motion-Aware Video Understanding
Authors: Jiaze Li; Yaya Shi; Zongyang Ma; Haoran Xu; Yandong.bai Yandong.bai; Huihui Xiao; Ruiwen Kang; Fan Yang; Tingting Gao; Di Zhang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Enhancing the fine-grained instance spatiotemporal motion perception capabilities of Video Large Language Models is crucial for improving their temporal and general video understanding. However, current models struggle to perceive detailed and complex instance motions. To address these challenges, we have made improvements from both data and model perspectives. In terms of data, we have meticulously curated iMOVE-IT, the first large-scale instance-motion-aware video instruction-tuning dataset. This dataset is enriched with comprehensive instance motion annotations and spatiotemporal mutual-supervision tasks, providing extensive training for the model’s instance-motion-awareness. Building on this foundation, we introduce iMOVE, an instance-motion-aware video foundation model that utilizes Event-aware Spatiotemporal Efficient Modeling to retain informative instance spatiotemporal motion details while maintaining computational efficiency. It also incorporates Relative Spatiotemporal Position Tokens to ensure awareness of instance spatiotemporal positions. Evaluations indicate that iMOVE excels not only in video temporal understanding and general video understanding but also demonstrates significant advantages in long-term video understanding. We will release the data, code, and model weights after acceptance.
SceneGram: Conceptualizing and Describing Tangrams in Scene Context
Authors: Simeon Junker; Sina Zarrieß
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Research on reference and naming suggests that humans can come up with very different ways of conceptualizing and referring to the same object, e.g. the same abstract tangram shape can be a “crab”, “sink” or “space ship”. Another common assumption in cognitive science is that scene context fundamentally shapes our visual perception of objects and conceptual expectations. This paper contributes SceneGram, a dataset of human references to tangram shapes placed in different scene contexts, allowing for systematic analyses of the effect of scene context on conceptualization. Based on this data, we analyze references to tangram shapes generated by multimodal LLMs, showing that these models do not account for the richness and variability of conceptualizations found in human references.