GenAI4ELab/papercli-papers huggingface dataset:
Advancing General Multimodal Capability of Vision-language Models with Pyramid-descent Visual Position Encoding
Authors: Zhanpeng Chen; Mingxiao Li; Ziyang Chen; Nan Du; Xiaolong Li; Yuexian Zou
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Vision-language Models (VLMs) have shown remarkable capabilities in advancing general artificial intelligence, yet the irrational encoding of visual positions persists in inhibiting the models’ comprehensive perception performance across different levels of granularity. In this work, we propose Pyramid-descent Visual Position Encoding (PyPE), a novel approach designed to enhance the perception of visual tokens within VLMs. By assigning visual position indexes from the periphery to the center and expanding the central receptive field incrementally, PyPE addresses the limitations of traditional raster-scan methods and mitigates the long-term decay effects induced by Rotary Position Embedding (RoPE). Our method reduces the relative distance between interrelated visual elements and instruction tokens, promoting a more rational allocation of attention weights and allowing for a multi-granularity perception of visual elements and countering the over-reliance on anchor tokens. Extensive experimental evaluations demonstrate that PyPE consistently improves the general capabilities of VLMs across various sizes. Code is available at https://anonymous.4open.science/r/PyPE-34EE.
P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts
Authors: Yuhao Dan; Jie Zhou; Qin Chen; Junfeng Tian; Liang He
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Personalized large language models (LLMs) have attracted great attention in many applications, such as emotional support and role-playing. However, existing works primarily focus on modeling explicit character profiles, while ignoring the underlying personality traits that truly shape behaviors and decision-making, hampering the development of more anthropomorphic and psychologically-grounded AI systems. In this paper, we explore the modeling of Big Five personality traits, which is the most widely used trait theory in psychology, and propose P-React, a mixture of experts (MoE)-based personalized LLM. Particularly, we integrate a Personality Specialization Loss (PSL) to better capture individual trait expressions, providing a more nuanced and psychologically grounded personality simulacrum. To facilitate research in this field, we curate OCEAN-Chat, a high-quality, human-verified dataset designed to train LLMs in expressing personality traits across diverse topics. Extensive experiments demonstrate the effectiveness of P-React in maintaining consistent and real personality.
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
Authors: Jiamin Su; Yibo Yan; Fangteng Fu; Zhang Han; Jingheng Ye; Xiang Liu; Jiahao Huo; Huiyu Zhou; Xuming Hu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (i) reliance on handcrafted features that limit generalizability, (ii) difficulty in capturing fine-grained traits like coherence and argumentation, and (iii) inability to handle multimodal contexts. In the era of Multimodal Large Language Models (MLLMs), we propose **EssayJudge**, the **first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits**. By leveraging MLLMs’ strengths in trait-specific scoring and multimodal context understanding, EssayJudge aims to offer precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations. Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits, highlighting the need for further advancements in MLLM-based AES research. Our dataset and code will be available upon acceptance.
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks
Authors: Yuanjie Lyu; Chao Zhang; Yuhao Chen; Yong Chen; Tong Xu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: In Retrieval-Augmented Generation (RAG) and agent-based frameworks, the “Chain of Models” approach is widely used, where multiple specialized models work sequentially on distinct sub-tasks. This approach is effective but increases resource demands as each model must be deployed separately. Recent advancements attempt to address this by applying prompt tuning, which allows a shared base model to adapt to multiple tasks with minimal parameter changes. However, a key challenge remains: intermediate outputs, passed between models as plain text, require recomputation of hidden states (i.e., Key and Value (KV) states in Transformers) during inference. In this paper, we introduce FTHSS, a novel prompt-tuning method that enables models to share KV hidden states, eliminating redundant forward passes and reducing KV cache storage. By modifying input and attention masks during training, FTHSS allows models to effectively utilize KV hidden states from prior models in both single- and multi-round scenarios. Empirical results on four tasks show that FTHSS matches the performance of traditional model chains while improving inference efficiency.
Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks
Authors: Jiayi He; Hehai Lin; Qingyun Wang; Yi R. Fung; Heng Ji
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: While Vision-Language Models (VLMs) have shown remarkable abilities, they invariably generate flawed responses. Self-correction that instructs models to refine their outputs presents a promising solution to this issue. Previous studies have mainly concentrated on Large Language Models (LLMs), while the self-correction abilities of VLMs, particularly concerning both visual and linguistic information, remain largely unexamined. This study investigates the self-correction capabilities of VLMs during both inference and fine-tuning stages. We introduce a Self-Correction Learning (SCL) approach that enables VLMs to learn from their self-generated self-correction data through Direct Preference Optimization (DPO) without relying on external feedback, facilitating self-improvement. Experimental results demonstrate that although VLMs struggle to self-correct effectively during iterative inference without additional fine-tuning and external feedback, they can enhance their performance and avoid previous mistakes through preference fine-tuning when their generated self-correction data are categorized into preferred and disfavored samples. This study emphasizes that self-correction is not merely a refinement process; rather, it should enhance models’ reasoning ability through additional training, enabling them to generate high-quality responses directly without further refinement.
Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
Authors: Chenkai Sun; Denghui Zhang; ChengXiang Zhai; Heng Ji
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understanding the far-reaching implications of their suggestions. We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems on a macroscopic scale over time, enabling more robust alignment. To assess the long-term safety awareness of language models, we also introduce a dataset of 100 indirect harm scenarios, testing models’ ability to foresee adverse, non-obvious outcomes from seemingly harmless user prompts. Our approach achieves not only over 20% improvement on the new dataset but also an average win rate exceeding 70% against strong baselines on existing safety benchmarks (AdvBench, SafeRLHF, WildGuardMix), suggesting a promising direction for safer agents.
Probability-Consistent Preference Optimization for Enhanced LLM Reasoning
Authors: Yunqiao Yang; Houxing Ren; Zimu Lu; Ke Wang; Weikang Shi; Aojun Zhou; Junting Pan; Mingjie Zhan; Hongsheng Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models (LLMs). While current approaches leverage high-quality pairwise preference data through outcome-based criteria like answer correctness or consistency, they fundamentally neglect the internal logical coherence of responses. To overcome this, we propose Probability-Consistent Preference Optimization (PCPO), a novel framework that establishes dual quantitative metrics for preference selection: (1) surface-level answer correctness and (2) intrinsic token-level probability consistency across responses. Extensive experiments show that our PCPO consistently outperforms existing outcome-only criterion approaches across a diverse range of LLMs and benchmarks. Our code is publicly available at https://github.com/YunqiaoYang/PCPO.
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
Authors: Hongcheng Guo; Wei Zhang; Junhao Chen; Yaonan Gu; Jian Yang; Junjia Du; Shaosheng Cao; Binyuan Hui; Tianyu Liu; Jianxin Ma; Chang Zhou; Zhoujun Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recently, advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of a robust benchmark specifically for assessing the image‐to‐web conversion proficiency of these large models. It is essential to ensure the integrity of the web elements generated, which comprise both visible and invisible categories. Previous evaluation methods (e.g., BLEU) are notably susceptible to significant alterations due to the presence of invisible elements. Furthermore, it is crucial to measure the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. To address these challenges, we have curated and aligned a benchmark of images and corresponding web codes (IW-bench). Specifically, we propose Element Accuracy, which tests the completeness of elements by parsing the Document Object Model (DOM) tree. We also introduce Layout Accuracy to analyze positional relationships by converting the DOM tree into a common subsequence. In addition, we design a five‐hop multimodal Chain‐of‐Thought prompting strategy for improved performance, consisting of: 1) SoM prompt injection, 2) inferring elements, 3) inferring layout, 4) inferring web code, and 5) reflection. Our benchmark comprises 1,200 image–code pairs with varying levels of difficulty. We have conducted extensive experiments on existing large multimodal models, providing insights into their performance and identifying areas for improvement in the image‐to‐web domain.
TDCSA: LLM-Guided Top-Down Approach for Robust Citation Sentiment Analysis
Authors: Fan Gao; Jieyang Peng; Xiaoming Tao; Wang Youzheng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Citation Sentiment Analysis (CSA) plays a crucial role in understanding academic influence and knowledge diffusion. While pre-trained language models (PLMs) and large language models (LLMs) showed remarkable success in general sentiment analysis, they encounter specialized challenges in CSA due to the less significant and implicit sentiment expressions in academic writing, as well as complex sentiment transitions. % importance & limitations In order to address the challenges, We propose TDCSA, a Top-Down framework that leverages LLMs’ semantic understanding capabilities to enhance PLM-based CSA, which transforms the traditional bottom-up feature engineering paradigm into a top-down architecture. % what we do Our framework consists of three key components: (1) a Dual LLM Feature Generation module for robust quadruple extraction, (2) a Multi-view Feature Representation mechanism for neutral citation processing, and (3) a Quad Feature Enhanced PLM. % how we do Experiments demonstrate that TDCSA significantly outperforms existing methods, achieving state-of-the-art performance while maintaining robustness to quadruple quality variations.
DeepRTL2: A Versatile Model for RTL-Related Tasks
Authors: Yi Liu; Hongji Zhang; Yunhao Zhou; Zhengyuan Shi; Changran Xu; Qiang Xu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in register transfer level (RTL) code generation and understanding. While previous studies have demonstrated the efficacy of fine-tuning LLMs for these generation-based tasks, embedding-based tasks, which are equally critical to EDA workflows, have been largely overlooked. These tasks, including natural language code search, RTL code functionality equivalence checking, and performance prediction, are essential for accelerating and optimizing the hardware design process. To address this gap, we present DeepRTL2, a family of versatile LLMs that unifies both generation- and embedding-based tasks related to RTL. By simultaneously tackling a broad range of tasks, DeepRTL2 represents the first model to provide a comprehensive solution to the diverse challenges in EDA. Through extensive experiments, we show that DeepRTL2 achieves state-of-the-art performance across all evaluated tasks.
The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?
Authors: Yutao Sun; Mingshuai Chen; Tiancheng Zhao; Ruochen Xu; Zilun Zhang; Jianwei Yin
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Self-improving large language models (LLMs) – i.e., to improve the performance of an LLM by fine-tuning it with synthetic data generated by itself – is a promising way to advance the capabilities of LLMs while avoiding extensive supervision. Existing approaches to self-improvement often rely on external supervision signals in the form of seed data and/or assistance from third-party models. This paper presents Crescent – a simple yet effective framework for generating high-quality synthetic question-answer data in a fully autonomous manner. Crescent first elicits the LLM to generate raw questions via a bait prompt, then diversifies these questions leveraging a rejection sampling-based self-deduplication, and finally feeds the questions to the LLM and collects the corresponding answers by means of majority voting. We show that Crescent sheds light on the potential of true self-improvement with zero external supervision signals for math reasoning; in particular, Crescent-generated question-answer pairs suffice to (i) improve the reasoning capabilities of an LLM while preserving its general performance (especially in the 0-shot setting); and (ii) distill LLM knowledge to weaker models more effectively than existing methods based on seed-dataset augmentation.
Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer
Authors: Long Chen; Shuoyu Guan; Xiaohua Huang; Wen-Jing Wang; Cai Xu; Ziyu Guan; Wei Zhao
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Existing multimodal sentiment analysis (MSA) methods have achieved significant success, leveraging cross-modal large-scale models (LLMs) and extensive pre-training data. However, these methods struggle to handle MSA tasks in low-resource languages. While multilingual LLMs enable cross-lingual transfer, they are limited to textual data and cannot address multimodal scenarios. To achieve MSA in low-resource languages, we propose a novel transfer learning framework named Language Family Disentanglement and Rethinking Transfer (LFD-RT). During pre-training, we establish cross-lingual and cross-modal alignments, followed by a language family disentanglement module that enhances the sharing of language universals within families while reducing noise from cross-family alignments. We propose a rethinking strategy for unsupervised fine-tuning that adapts the pre-trained model to MSA tasks in low-resource languages. Experimental results demonstrate the superiority of our method and its strong language-transfer capability on target low-resource languages. We commit to making our code and data publicly available, and the access link will be provided here.
Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking?
Authors: Chengda Lu; Xiaoyu Fan; Yu Huang; Rongwu Xu; Jijie Li; Wei Xu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Jailbreak attacks have been observed to largely fail against recent reasoning models enhanced by Chain-of-Thought (CoT) reasoning. However, the underlying mechanism remains underexplored, and relying solely on reasoning capacity may raise security concerns. In this paper, we try to answer the question: Does CoT reasoning really reduce harmfulness from jailbreaking? Through rigorous theoretical analysis, we demonstrate that CoT reasoning has dual effects on jailbreaking harmfulness. Based on the theoretical insights, we propose a novel jailbreak method, FicDetail, whose practical performance validates our theoretical findings.
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model
Authors: Yuhang Zang; Xiaoyi Dong; Pan Zhang; Yuhang Cao; Ziyu Liu; Shengyuan Ding; Shenxi Wu; Yubo Ma; Haodong Duan; Wenwei Zhang; Kai Chen; Dahua Lin; Jiaqi Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Despite the promising performance of Large Vision Language Models (LVLMs) in visual understanding, they occasionally generate incorrect outputs. While reward models (RMs) with reinforcement learning or test-time scaling offer the potential for improving generation quality, a critical gap remains: publicly available multi-modal RMs for LVLMs are scarce, and the implementation details of proprietary models are often unclear. We bridge this gap with InternLM-XComposer2.5-Reward (IXC-2.5-Reward), a simple yet effective multi-modal reward model that aligns LVLMs with human preferences. To ensure the robustness and versatility of IXC-2.5-Reward, we set up a high-quality multi-modal preference corpus spanning text, image, and video inputs across diverse domains, such as instruction following, general understanding, text-rich documents, mathematical reasoning, and video understanding. IXC-2.5-Reward achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model benchmarks. We further demonstrate three key applications of IXC-2.5-Reward: (1) Providing a supervisory signal for RL training. We integrate IXC-2.5-Reward with Proximal Policy Optimization (PPO) yields IXC-2.5-Chat, which shows consistent improvements in instruction following and multi-modal open-ended dialogue; (2) Selecting the best response from candidate responses for test-time scaling; and (3) Filtering outlier or noisy samples from existing image and video instruction tuning training data.
RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models
Authors: Junjie Li; Nan Zhang; Xiaoyang Qu; Kai Lu; Guokuan Li; Jiguang Wan; Jianzong Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Object Navigation (ObjectNav) is a fundamental task in embodied artificial intelligence. Although significant progress has been made in semantic map construction and target direction prediction in current research, redundant exploration and exploration failures remain inevitable. A critical but underexplored direction is the timely termination of exploration to overcome these challenges. We observe a diminishing marginal effect between exploration steps and exploration rates and analyze the cost-benefit relationship of exploration. Inspired by this, we propose RATE-Nav, a Region-Aware Termination-Enhanced method. It includes a geometric predictive region segmentation algorithm and region-Based exploration estimation algorithm for exploration rate calculation. By leveraging the visual question answering capabilities of visual language models (VLMs) and exploration rates enables efficient termination.RATE-Nav achieves a success rate of 67.8% and an SPL of 31.3% on the HM3D dataset. And on the more challenging MP3D dataset, RATE-Nav shows approximately 10% improvement over previous zero-shot methods.
RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation
Authors: Zhentao Xie; Chengcheng Han; Jinxin Shi; Wenjun Cui; Xin Zhao; Xingjiao Wu; Jiabao Zhao
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Although multi-agent systems based on large language models show strong capabilities on multiple tasks, they are still limited by high computational overhead, information loss, and robustness. Inspired by ResNet’s residual learning, we propose Residual Mixture-of-Agents (RMoA), integrating residual connections to optimize efficiency and reliability. To maximize information utilization from model responses while minimizing computational costs, we innovatively design an embedding-based diversity selection mechanism that greedily selects responses via vector similarity. Furthermore, to mitigate iterative information degradation, we introduce a Residual Extraction Agent to preserve cross-layer incremental information by capturing inter-layer response differences, coupled with a Residual Aggregation Agent for hierarchical information integration. Additionally, we propose an adaptive termination mechanism that dynamically halts processing based on residual convergence, further improving inference efficiency. RMoA achieves state-of-the-art performance on the benchmarks of across alignment, mathematical reasoning, code generation, and multitasking understanding, while significantly reducing computational overhead. Code is available at https://github.com/mindhunter01/RMoA.