GenAI4ELab/papercli-papers huggingface dataset:
MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP
Authors: Kurt Micallef; Claudia Borg
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various Natural Language Processing (NLP) tasks, largely due to their generalisability and ability to perform tasks without additional training. However, their effectiveness for low-resource languages remains limited. In this study, we evaluate the performance of 55 publicly available LLMs on Maltese, a low-resource language, using a newly introduced benchmark covering 11 discriminative and generative tasks. Our experiments highlight that many models perform poorly, particularly on generative tasks, and that smaller fine-tuned models often perform better across all tasks. From our multidimensional analysis, we investigate various factors impacting performance. We conclude that prior exposure to Maltese during pre-training and instruction-tuning emerges as the most important factor. We also examine the trade-offs between fine-tuning and prompting, highlighting that while fine-tuning requires a higher initial cost, it yields better performance and lower inference costs. Through this work, we aim to highlight the need for more inclusive language technologies and recommend for researchers working with low-resource languages to consider more “traditional” language modelling approaches.
TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring
Authors: Sohaila Eltanbouly; Salam Albatarni; Tamer Elsayed
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Research on holistic Automated Essay Scoring (AES) is long-dated; yet, there is a notable lack of attention for assessing essays according to individual traits. In this work, we propose TRATES, a novel trait-specific and rubric-based cross-prompt AES framework that is generic yet specific to the underlying trait. The framework leverages a Large Language Model (LLM) that utilizes the trait grading rubrics to generate trait-specific features (represented by assessment questions), then assesses those features given an essay. The trait-specific features are eventually combined with generic writing-quality and prompt-specific features to train a simple classical regression model that predicts trait scores of essays from an unseen prompt. Experiments show that TRATES achieves a new state-of-the-art performance across all traits on a widely-used dataset, with the generated LLM-based features being the most significant.
DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens
Authors: Shaoshen Chen; Yangning Li; Zishan Xu; Yongqin Zeng; Shunlong Wu; Xinshuo Hu; Zifei Shan; Xin Su; Jiwei Tang; Yinghui Li; Hai-Tao Zheng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) face computational inefficiencies and redundant processing when handling long context inputs, prompting a focus on compression techniques. While existing semantic vector-based compression methods achieve promising performance, these methods fail to account for the intrinsic information density variations between context chunks, instead allocating soft tokens uniformly across context chunks. This uniform distribution inevitably diminishes allocation to information-critical regions. To address this, we propose Dynamic Allocation of Soft Tokens (DAST), a simple yet effective method that leverages the LLM’s intrinsic understanding of contextual relevance to guide compression. DAST combines perplexity-based local information with attention-driven global information to dynamically allocate soft tokens to the informative-rich chunks, enabling effective, context-aware compression. Experimental results across multiple benchmarks demonstrate that DAST surpasses state-of-the-art methods.
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
Authors: Yimin Deng; Yuxia Wu; Yejing Wang; Guoshuai Zhao; Li Zhu; Qidong Liu; Derong Xu; Zichuan Fu; Xian Wu; Yefeng Zheng; Xiangyu Zhao; Xueming Qian
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on either graph structure learning or semantic reasoning, failing to integrate dual reasoning perspectives to handle different prediction scenarios. Moreover, they lack the capability to capture the inherent differences between historical and non-historical events, which limits their generalization across different temporal contexts. To this end, we propose a **M**ulti-**E**xpert **S**tructural-**S**emantic **H**ybrid (MESH) framework that employs three kinds of expert modules to integrate both structural and semantic information, guiding the reasoning process for different events. Extensive experiments on three datasets demonstrate the effectiveness of our approach.
MWPO: Enhancing LLMs Performance through Multi-Weight Preference Strength and Length Optimization
Authors: Shiyue Xu; Fu Zhang; Jingwei Cheng; Linfeng Zhou
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Direct Preference Optimization (DPO) have proposed offline alternatives to Reinforcement Learning from Human Feedback (RLHF). In DPO, each preference pair, which serves as the foundation for learning, is typically constructed by first generating multiple responses to the same instruction and then annotating them to indicate the preferred choice. However, when the responses are highly similar, the weak preference signal can introduce annotation noise, which may hinder model optimization. Additionally, DPO suffers from the drawback of over-optimizing for verbose generation. A potential reason is the presence of length bias in preference datasets, which can lead to length exploitation. To address these issues, we propose a DPO-based **m**ulti-**w**eight **p**reference strength and length **o**ptimization (MWPO) method. Specifically, we propose to reweight preference pairs based on implicit reward margins and response length margins, unifying them through a geometric mixture to generate synthetic weights for optimization. This method allows preference pairs with stronger preference signals or more favorable length feature to have a more pronounced impact on model parameters. Moreover, our method does not require additional annotators. We validate our method on models of four different scales across multiple benchmarks. Our method surpasses state-of-the-art (SOTA) baselines, outperforming DPO by up to 8.7% on AlpacaEval 2 while reducing generation length by 9.4% in the Mistral setting. Our code is available at https://github.com/AIR-hl/MWPO.
CLEAR: Character Unlearning in Textual and Visual Modalities
Authors: Alexey Dontsov; Dmitrii Korzh; Alexey Zhavoronkin; Boris Mikheev; Denis Bobkov; Aibek Alanov; Oleg Rogov; Ivan Oseledets; Elena Tutubalina
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Machine Unlearning (MU) is critical for removing private or hazardous information from deep learning models. While MU has advanced significantly in unimodal (text or vision) settings, multimodal unlearning (MMU) remains underexplored due to the lack of open benchmarks for evaluating cross-modal data removal. To address this gap, we introduce CLEAR, the first open-source benchmark designed specifically for MMU. CLEAR contains 200 fictitious individuals and 3,700 images linked with corresponding question-answer pairs, enabling a thorough evaluation across modalities. We conduct a comprehensive analysis of 11 MU methods (e.g., SCRUB, gradient ascent, DPO) across four evaluation sets, demonstrating that jointly unlearning both modalities outperforms single-modality approaches. The dataset is available at [link](https://huggingface.co/datasets/therem/CLEAR)
Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification
Authors: John Dougrez-Lewis; Mahmud Elahi Akhter; Federico Ruggeri; Sebastian Löbbers; Yulan He; Maria Liakata
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Although LLMs have shown great performance on Mathematics and Coding related reasoning tasks, the reasoning capabilities of LLMs regarding other forms of reasoning are still an open problem. Here, we examine the issue of reasoning from the perspective of claim verification. We propose a framework designed to break down any claim paired with evidence into atomic reasoning types that are necessary for verification. We use this framework to create RECV, the first claim verification benchmark, incorporating real-world claims, to assess the deductive and abductive reasoning capabilities of LLMs. The benchmark comprises of three datasets, covering reasoning problems of in creasing complexity. We evaluate three state of-the-art proprietary LLMs under multiple prompt settings. Our results show that while LLMs can address deductive reasoning prob lems, they consistently fail in cases of abductive reasoning. Moreover, we observe that enhancing LLMs with rationale generation is not always beneficial. Nonetheless, we find that generated rationales are semantically similar to those provided by humans, especially in deduc tive reasoning cases.
Language Models Lack Temporal Generalization and Bigger is Not Better
Authors: Stella Verkijk; Piek Vossen; Pia Sommerauer
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: This paper presents elaborate testing of various LLMs on their generalization capacities. We finetune six encoder models that have been pretrained with very different data (varying in size, language, and period) on a challenging event detection task in Early Modern Dutch archival texts. Each model is finetuned with 5 seeds on 15 different data splits, resulting in 450 finetuned models. We also pre-train a domain-specific Language Model on the target domain and fine-tune and evaluate it in the same way to provide an upper bound. Our experimental setup allows us to look at underresearched aspects of generalizability, namely i) shifts at multiple places in a modeling pipeline, ii) temporal and crosslingual shifts and iii) generalization over different initializations. The results show that none of the models reaches domain-specific model performance, demonstrating their incapacity to generalize. mBERT reaches highest F1 performance, and is relatively stable over different seeds and datasplits, contrary to XLM-R. We find that contemporary Dutch models do not generalize well to Early Modern Dutch as they underperform compared to crosslingual as well as historical models. We conclude that encoder LLMs lack temporal generalization capacities and that bigger models are not better, since even a model pre-trained with five hundred GPUs on 2.5 terabytes of training data (XLM-R) underperforms considerably compared to our domain-specific model, pre-trained on one GPU and 6 GB of data. All our code, data, and the domain-specific model are openly available.
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
Authors: Ying Zhou; Xinyao Wang; Yulei Niu; Yaojie Shen; Lexin Tang; Fan Chen; Ben He; Le Sun; Longyin Wen
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advancements in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis. However, synthetic data generation via prompting LLMs remains challenging due to LLMs’ limited understanding of target data distributions and the complexity of prompt engineering, especially for structured formatted data. To address these issues, we introduce DiffLM, a controllable data synthesis framework based on variational autoencoder (VAE), which further (1) leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution and (2) decouples the learning of target distribution knowledge from the LLM’s generative objectives via a plug-and-play latent feature injection module. As we observed significant discrepancies between the VAE’s latent representations and the real data distribution, the latent diffusion module is introduced into our framework to learn a fully expressive latent distribution. Evaluations on seven real-world datasets with structured formatted data (i.e., Tabular, Code, and Tool data) demonstrate that DiffLM generates high-quality data, with performance on downstream tasks surpassing that of real data by 2%–7% in certain cases. Data and code are available at https://github.com/bytedance/DiffLM.
Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models?
Authors: Yifei Wang; Yu Sheng; Linjing Li; Daniel Dajun Zeng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advances in handling long sequences have unlocked new possibilities for long-context in-context learning (ICL). While existing research predominantly focuses on performance gains driven by additional in-context examples, the impact on the trustworthiness of generated responses remains underexplored. This paper addresses this gap by investigating how increased examples influence predictive uncertainty—an essential aspect in trustworthiness. We begin by systematically quantifying uncertainty across different “shot” configurations in ICL, emphasizing the role of example quantity. Through uncertainty decomposition, we introduce a novel perspective on performance enhancement, focusing on epistemic uncertainty (EU). Our results reveal that additional examples reduce total uncertainty in both simple and complex tasks by injecting task-specific knowledge, thereby diminishing EU and enhancing performance. For complex tasks, these advantages emerge only after addressing the increased noise and uncertainty associated with longer inputs. Finally, we investigate the progression of internal confidence across layers, uncovering the underlying mechanisms that drive the reduction in uncertainty.
ToolSpectrum: Towards Personalized Tool Utilization for Large Language Models
Authors: Zihao Cheng; Hongru Wang; Zeming Liu; Yuhang Guo; Yuanfang Guo; Yunhong Wang; Haifeng Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: While integrating external tools into large language models (LLMs) enhances their ability to access real-time information and domain-specific services, existing approaches focus narrowly on functional tool selection following user instructions while overlooking the critical role of context-aware personalization in tool selection. This oversight leads to suboptimal user satisfaction and inefficient tool utilization, particularly when overlapping toolsets require nuanced selection based on contextual factors. To bridge this gap, we introduce ToolSpectrum, a benchmark designed to evaluate LLMs’ capabilities in personalized tool utilization. Specifically, we formalize two key dimensions of personalization, user profile and environmental factors, and analyze their individual and synergistic impacts on tool selection. Through extensive experiments on ToolSpectrum, we demonstrate that personalized tool selection significantly improves user experience across diverse scenarios. However, even state-of-the-art LLMs exhibit the limited ability to reason jointly about user profiles and environmental factors, often prioritizing one dimension at the expense of the other. Our findings underscore the necessity of context-aware personalization in tool-augmented LLMs and reveal critical limitations for current models. Our data and code will be released soon.
Reverse Preference Optimization for Complex Instruction Following
Authors: Xiang Huang; Ting-En Lin; Feiteng Fang; Yuchuan Wu; Hangyu Li; Yuzhong Qu; Fei Huang; Yongbin Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Instruction following (IF) is a critical capability for large language models (LLMs). However, handling complex instructions with multiple constraints remains challenging. Previous methods typically select preference pairs based on the number of constraints they satisfy, introducing noise where chosen examples may fail to follow some constraints and rejected examples may excel in certain respects over the chosen ones. To address the challenge of aligning with multiple preferences, we propose a simple yet effective method called Reverse Preference Optimization (RPO). It mitigates noise in preference pairs by dynamically reversing the constraints within the instruction to ensure the chosen response is perfect, alleviating the burden of extensive sampling and filtering to collect perfect responses. Besides, reversal also enlarges the gap between chosen and rejected responses, thereby clarifying the optimization direction and making it more robust to noise. We evaluate RPO on two multi-turn IF benchmarks, Sysbench and Multi-IF, demonstrating average improvements over the DPO baseline of 4.6 and 2.5 points (on Llama-3.1 8B), respectively. Moreover, RPO scales effectively across model sizes (8B to 70B parameters), with the 70B RPO model surpassing GPT-4o.
MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens
Authors: Jeong Hun Yeo; Hyeongseop Rha; Se Jin Park; Yong Man Ro
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Audio-Visual Speech Recognition (AVSR) achieves robust speech recognition in noisy environments by combining auditory and visual information. However, recent Large Language Model (LLM) based AVSR systems incur high computational costs due to the high temporal resolution of audio-visual speech processed by LLMs. In this work, we introduce an efficient multimodal speech LLM framework that minimizes token length while preserving essential linguistic content. Our approach employs an early AV-fusion module for streamlined feature integration, an audio-visual speech Q-Former that dynamically allocates tokens based on input duration, and a refined query allocation strategy with a speech rate predictor to adjust token allocation according to speaking speed of each audio sample. Extensive experiments on the LRS3 dataset show that our method achieves state-of-the-art performance with a WER of 0.72% while using only 3.5 tokens per second. Moreover, our approach not only reduces token usage by 86% compared to the previous multimodal speech LLM framework, but also improves computational efficiency by reducing FLOPs by 35.7%. The code and models are available https://github.com/JeongHun0716/MMS-LLaMA.
Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic Segmentation
Authors: Seungmin Lee; Yongsang Yoo; Minhwa Jung; Min Song
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Dialogue Topic Segmentation (DTS) aims to divide dialogues into coherent segments. DTS plays a crucial role in various NLP downstream tasks, but suffers from chronic problems: data shortage, labeling ambiguity, and incremental complexity of recently proposed solutions. On the other hand, Despite advances in Large Language Models (LLMs) and reasoning strategies, these have rarely been applied to DTS. This paper introduces Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic Segmentation, which utilizes LLM-based multi-step deductive reasoning to enhance DTS performance and enable case study using intermediate result. Our method employs a structured prompting approach for bidirectional context summarization, utterance intent classification, and deductive topic shift detection. In the intent classification process, we propose the generalizable intent list for domain-agnostic dialogue intent classification. Experiments in various dialogue settings demonstrate that Def-DTS consistently outperforms traditional and state-of-the-art approaches, with each subtask contributing to improved performance, particularly in reducing type 2 error. We also explore the potential for autolabeling, emphasizing the importance of LLM reasoning techniques in DTS.
Exploring Jailbreak Attacks on LLMs through Intent Concealment and Diversion
Authors: Tiehan Cui; Yanxu Mao; Peipei Liu; Congying Liu; Datao You
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Although large language models (LLMs) have achieved remarkable advancements, their security remains a pressing concern. One major threat is jailbreak attacks, where adversarial prompts bypass model safeguards to generate harmful or objectionable content. Researchers study jailbreak attacks to understand security and robustness of LLMs. However, existing jailbreak attack methods face two main challenges: (1) an excessive number of iterative queries, and (2) poor generalization across models. In addition, recent jailbreak evaluation datasets focus primarily on question-answering scenarios, lacking attention to text generation tasks that require accurate regeneration of toxic content.To tackle these challenges, we propose two contributions:(1) **ICE**, a novel black-box jailbreak method that employs **I**ntent **C**oncealment and div**E**rsion to effectively circumvent security constraints. **ICE** achieves high attack success rates (ASR) with a single query, significantly improving efficiency and transferability across different models.(2) **BiSceneEval**, a comprehensive dataset designed for assessing LLM robustness in question-answering and text-generation tasks. Experimental results demonstrate that **ICE** outperforms existing jailbreak techniques, revealing critical vulnerabilities in current defense mechanisms. Our findings underscore the necessity of a hybrid security strategy that integrates predefined security mechanisms with real-time semantic decomposition to enhance the security of LLMs.
Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning
Authors: Joonwon Jang; Jaehee Kim; Wonbin Kweon; Seonghyeon Lee; Hwanjo Yu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) rely on generating extensive intermediate reasoning units (e.g., tokens, sentences) to enhance final answer quality across a wide range of complex tasks. While this approach has proven effective, it inevitably increases substantial inference costs. Previous methods adopting token-level reduction without clear criteria result in poor performance compared to models trained with complete rationale. To address this challenge, we propose a novel sentence-level rationale reduction framework leveraging likelihood-based criteria, *verbosity*, to identify and remove redundant reasoning sentences. Unlike previous approaches, our method leverages *verbosity* to selectively remove redundant reasoning sentences while preserving reasoning capabilities. Our experimental results across various reasoning tasks demonstrate that our method improves performance by an average of 7.71% while reducing token generation by 19.87% compared to model trained with complete reasoning paths.