GenAI4ELab/papercli-papers huggingface dataset:
SConU: Selective Conformal Uncertainty in Large Language Models
Authors: Zhiyuan Wang; Qingni Wang; Yue Zhang; Tianlong Chen; Xiaofeng Zhu; Xiaoshuang Shi; Kaidi Xu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies have introduced various criteria of conformal uncertainty grounded in split conformal prediction, which offer user-specified correctness coverage. However, existing frameworks often fail to identify uncertainty data outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets. In this paper, we propose a novel approach termed Selective Conformal Uncertainty (SConU), which, for the first time, implements significance tests, by developing two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level. Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions. Furthermore, we comprehensively analyze the components of the conformal procedures, aiming to approximate conditional coverage, particularly in high-stakes question-answering tasks.
MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval
Authors: Junjie Zhou; Yongping Xiong; Zheng Liu; Ze Liu; Shitao Xiao; Yueze Wang; Bo Zhao; Chen Jason Zhang; Defu Lian
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Despite the rapidly growing demand for multimodal retrieval, progress in this field remains severely constrained by a lack of training data. In this paper, we introduce MegaPairs, a novel data synthesis method that leverages vision language models (VLMs) and open-domain images, together with a massive synthetic dataset generated from this method. Our empirical analysis shows that MegaPairs generates high-quality data, enabling the multimodal retriever to significantly outperform the baseline model trained on 70× more data from existing datasets. Moreover, since MegaPairs solely relies on general image corpora and open-source VLMs, it can be easily scaled up, enabling continuous improvements in retrieval performance. In this stage, we produced more than 26 million training instances and trained several models of varying sizes using this data. These new models achieve state-of-the-art zero-shot performance across 4 popular composed image retrieval (CIR) benchmarks and the highest overall performance on the 36 datasets provided by MMEB. They also demonstrate notable performance improvements with additional downstream fine-tuning. Our code, synthesized dataset, and pre-trained models are publicly available at https://github.com/VectorSpaceLab/MegaPairs.
When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs
Authors: Xinyue Shen; Yun Shen; Michael Backes; Yang Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Knowledge files have been widely used in large language model (LLM)-powered agents, such as GPTs, to improve response quality. However, concerns over the potential leakage of knowledge files have grown significantly. Existing studies demonstrate that adversarial prompts can induce GPTs to leak knowledge file content. Yet, it remains uncertain whether additional leakage vectors exist, particularly given the complex data flows across clients, servers, and databases in GPTs. In this paper, we present a comprehensive risk assessment of knowledge file leakage, leveraging a novel workflow inspired by Data Security Posture Management (DSPM). Through the analysis of 651,022 GPT metadata, 11,820 flows, and 1,466 responses, we identify five leakage vectors: metadata, GPT initialization, retrieval, sandboxed execution environments, and prompts. These vectors enable adversaries to extract sensitive knowledge file data such as titles, content, types, and sizes. Notably, the activation of the built-in tool Code Interpreter leads to a privilege escalation vulnerability, enabling adversaries to directly download original knowledge files with a 95.95% success rate. Further analysis reveals that 28.80% of leaked files are copyrighted, including digital copies from major publishers and internal materials from a listed company. In the end, we provide actionable solutions for GPT builders and platform providers to secure the GPT data supply chain.
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook
Authors: Yidi Jiang; Qian Chen; Shengpeng Ji; Yu Xi; Wen Wang; Chong Zhang; Xianghu Yue; ShiLiang Zhang; Haizhou Li
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The emergence of audio language models is empowered by neural audio codecs, which establish critical mappings between continuous waveforms and discrete tokens compatible with language model paradigms. The evolutionary trends from multi-layer residual vector quantizer to single-layer quantizer are beneficial for language-autoregressive decoding. However, the capability to handle multi-domain audio signals through a single codebook remains constrained by inter-domain distribution discrepancies. In this work, we introduce UniCodec, a unified audio codec with a single codebook to support multi-domain audio data, including speech, music, and sound. To achieve this, we propose a partitioned domain-adaptive codebook method based on domain Mixture-of-Experts strategy to capture the distinct characteristics of each audio domain. Furthermore, to enrich the semantic density of the codec without auxiliary modules, we propose a self-supervised mask prediction modeling approach. Comprehensive objective and subjective evaluations demonstrate that UniCodec achieves excellent audio reconstruction performance across the three audio domains, outperforming existing unified neural codecs with a single codebook, and even surpasses state-of-the-art domain-specific codecs on both acoustic and semantic representation capabilities.
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models
Authors: Fnu Mohbat; Mohammed J Zaki
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Recent advances in large language models (LLMs) and the abundance of food data have resulted in studies to improve food understanding using LLMs. Despite several recommendation systems utilizing LLMs and Knowledge Graphs (KGs), there has been limited research on integrating food related KGs with LLMs. We introduce KERL, a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generates recipes with associated micro-nutritional information. Given a natural language question, KERL extracts entities, retrieves subgraphs from the KG, which are then fed into the LLM as context to select the recipes that satisfy the constraints. Next, our system generates the cooking steps and nutritional information for each recipe. To evaluate our approach, we also develop a benchmark dataset by curating recipe related questions, combined with constraints and personal preferences. Through extensive experiments, we show that our proposed KG-augmented LLM significantly outperforms existing approaches, offering a complete and coherent solution for food recommendation, recipe generation, and nutritional analysis. Our code and benchmark datasets are publicly available at https://github.com/mohbattharani/KERL.
Multilingual Arbitration: Optimizing Data Pools to Accelerate Multilingual Progress
Authors: Ayomide Odumakinde; Daniel D’souza; Pat Verga; Beyza Ermis; Sara Hooker
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Synthetic data has driven recent state-of-the-art advancements, but reliance on a single oracle teacher model can lead to model collapse and bias propagation. These issues are particularly severe in multilingual settings, where no single model excels across all languages. In this study, we propose multilingual arbitration, which exploits performance variations among multiple models for each language. By strategically routing samples through a diverse set of models, each with unique strengths, we mitigate these challenges and enhance multilingual performance. Extensive experiments with state-of-the-art models demonstrate that our approach significantly surpasses single-teacher distillation, achieving up to 80% win rates over proprietary and open-weight models like Gemma 2, Llama 3.1, and Mistral v0.3, with the largest improvements in low-resource languages.
Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models
Authors: Yuheng Lu; Bingshuo Qian; Caixia Yuan; Huixing Jiang; Xiaojie Wang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks, where adaptation to a new domain leads to a substantial decline in performance on previous tasks. In this paper, we propose Controlled LoRA (CLoRA), a subspace regularization method on LoRA structure. Aiming to reduce the scale of output change while introducing minimal constraint on model capacity, CLoRA imposes constraints on the direction of updating matrix’s null space. Experimental results on one-stage LLM finetuning tasks and continual learning settings highlight the superiority of CLoRA as an effective parameter-efficient finetuning method with catastrophic forgetting mitigating. Further investigation for model parameters indicates that CLoRA effectively balances the trade-off between model capacity and degree of forgetting. The code for implementing CLoRA will be publicly available.
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models
Authors: Yancheng He; Shilong Li; Jiaheng Liu; Yingshui Tan; Weixun Wang; Hui Huang; Xingyuan Bu; Hangyu Guo; Chengwei Hu; Boren Zheng; Zhuoran Lin; Dekai Sun; Zhicheng Zheng; Wenbo Su; Bo Zheng
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: New LLM benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive Chinese benchmark to evaluate the factuality ability of LLMs to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate). Specifically, first, we focus on the Chinese language over 6 major topics with 99 diverse subtopics. Second, we conduct a comprehensive quality control process to achieve high-quality questions and answers, where the reference answers are static and cannot be changed over time. Third, following SimpleQA, the questions and answers are very short, and the grading process is easy-to-evaluate. Based on Chinese SimpleQA, we perform a comprehensive evaluation of the factuality abilities of existing LLMs. Finally, we hope that Chinese SimpleQA could guide the developers to better understand the Chinese factuality abilities of their models and facilitate the growth of LLMs.
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings
Authors: Junseo Kim; Jongwook Han; Dongmin Choi; Jongwook Yoon; Eun-Ju Lee; Yohan Jo
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Visual persuasion, which uses visual elements to influence cognition and behaviors, is crucial in fields such as advertising and politicalcommunication. With recent advancements in artificial intelligence, there is growing potential to develop persuasive systems that automatically generate persuasive images tailored to individuals. However, a significant bottleneck in this area is the lack of comprehensivedatasets that connect the persuasiveness of images with the personal information about those who evaluated the images. To address this gap and facilitate technological advancements in personalized visual persuasion, we release the Personalized Visual Persuasion (PVP) dataset, comprising 28,454 persuasive images across 596 messages and 9 persuasion strategies. Importantly, the PVP dataset provides persuasiveness scores of images evaluated by 2,521 human annotators, along with their demographic and psychological characteristics (personality traits and values). We demonstrate the utility of our dataset by developing a persuasive image generator and an automated evaluator, and establish benchmark baselines. Our experiments reveal that incorporating psychological characteristics enhances the generation and evaluation of persuasive images, providing valuable insights for personalized visual persuasion.
Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval
Authors: Zheng Liu; Ze Liu; Zhengyang Liang; Junjie Zhou; Shitao Xiao; Chao Gao; Chen Jason Zhang; Defu Lian
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: With the popularity of multimodal techniques, it receives growing interests to acquire useful information in visual forms. In this work, we formally define an emerging IR paradigm called Visualized Information Retrieval, or Vis-IR, where multimodal information, such as texts, images, tables and charts, is jointly represented by a unified visual format called Screenshots, for various retrieval applications. We further make three key contributions for Vis-IR. First, we create VIRA (Vis-IR Aggregation), a large-scale dataset comprising a vast collection of screenshots from diverse sources, carefully curated into captioned and question-answer formats. Second, we develop UniSE (Universal Screenshot Embeddings), a family of retrieval models that enable screenshots to query or be queried across arbitrary data modalities. Finally, we construct MVRB (Massive Visualized IR Benchmark), a comprehensive benchmark covering a variety of task forms and application scenarios. Through extensive evaluations on MVRB, we highlight the deficiency from existing multimodal retrievers and the substantial improvements made by UniSE. Our data, model and benchmark have been made publicly available, which lays a solid foundation for this emerging field.
Tunable LLM-based Proactive Recommendation Agent
Authors: Mingze Wang; Chongming Gao; Wenjie Wang; Yangyang Li; Fuli Feng
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Recommender systems are indispensable on various digital platforms. However, traditional methods often reinforce existing user interests, which leads to echo chambers and limits diversity. Proactive Recommendation Systems (PRS) aim to address this issue by cultivating users’ latent interests through multi-step recommendations. Despite advancements, challenges persist particularly in optimizing long-term rewards and adapting to real-time user feedback. In this study, we propose an LLM-based Actor-Critic Agent framework to enhance PRS. This framework utilizes the LLM-based agent to adjust recommendations in real time based on feedback and employs agent-tuning methods to optimize long-term rewards using three proposed reward functions. Extensive experiments validate the significant superiority of this framework over existing methods by optimizing long-term rewards and dynamically evolving with user feedback.
AgentRM: Enhancing Agent Generalization with Reward Modeling
Authors: Yu Xia; Jingru Fan; Weize Chen; Siyu Yan; Xin Cong; Zhong Zhang; Yaxi Lu; Yankai Lin; Zhiyuan Liu; Maosong Sun
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning the policy model with more diverse tasks to improve the generalizability. In this work, we find that finetuning a reward model to guide the policy model is more robust than directly finetuning the policy model.Based on this finding, we propose AgentRM, a 8B generalizable reward model, to guide the policy model for effective test-time search.We comprehensively investigate three approaches to construct the reward model, including explicit reward modeling, implicit reward modeling and LLM-as-a-judge.We then use AgentRM to guide the answer generation with Best-of-N sampling and beam search.We show that AgentRM is robust to paraphrasings of task instructions and can generalize to unseen tasks that require novel optimal behavior.Through extensive evaluation across nine tasks spanning four categories, AgentRM enhances the non-finetuned 8B policy model by 8.8 points on average, surpassing the top general agent by 4.0.Moreover, it demonstrates weak-to-strong generalization, yielding greater improvement on more powerful policy models.As for the specializability, AgentRM can also boost a finetuned policy model and outperform the top specialized agent by 11.4 on three held-in tasks.Further analysis verifies its effectiveness in test-time scaling.We release the code and data at https://github.com/thunlp/AgentRM.
From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment
Authors: Bin Xie; Bingbing Xu; Yige Yuan; Shengmao Zhu; Huawei Shen
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Inference-time alignment methods have gained significant attention for their efficiency and effectiveness in aligning large language models (LLMs) with human preferences. However, existing dominant approaches using reward-guided search (RGS) primarily rely on outcome reward models (ORMs), which suffer from a critical granularity mismatch: ORMs are designed to provide outcome rewards for complete responses, while RGS methods rely on process rewards to guide the policy, leading to inconsistent scoring and suboptimal alignment. To address this challenge, we introduce process reward models (PRMs) into RGS and argue that an ideal PRM should satisfy two objectives: Score Consistency, ensuring coherent evaluation across partial and complete responses, and Preference Consistency, aligning partial sequence assessments with human preferences. Based on these, we propose SP-PRM, a novel dual-consistency framework integrating score consistency-based and preference consistency-based partial evaluation modules without relying on human annotation. Extensive experiments on dialogue, summarization, and reasoning tasks demonstrate that SP-PRM substantially enhances existing RGS methods, achieving a 3.6%-10.3% improvement in GPT-4 evaluation scores across all tasks. Code is publicly available at https://github.com/xiebin23/SP-PRM.
Segment-Based Attention Masking for GPTs
Authors: Shahar Katz; Liran Ringel; Yaniv Romano; Lior Wolf
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Causal masking is a fundamental component in Generative Pre-Trained Transformer (GPT) models, playing a crucial role during training. Although GPTs can process the entire user prompt at once, the causal masking is applied to all input tokens step-by-step, mimicking the generation process. This imposes an unnecessary constraint during the initial “prefill” phase when the model processes the input prompt and generates the internal representations before producing any output tokens. In this work, attention is masked based on the known block structure at the prefill phase, followed by the conventional token-by-token autoregressive process after that. For example, in a typical chat prompt, the system prompt is treated as one block, and the user prompt as the next one. Each of these is treated as a unit for the purpose of masking, such that the first tokens in each block can access the subsequent tokens in a non-causal manner. Then, the model answer is generated in the conventional causal manner. The Segment-by-Segment scheme entails no additional computational overhead. When integrated using a lightweight fine-tuning into already trained models such as Llama and Qwen, MAS quickly increases models’ performances.
Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity
Authors: Yuri Kuratov; Mikhail Arkhipov; Aydar Bulatov; Mikhail Burtsev
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: A range of recent works addresses the problem of compression of sequence of tokens into a shorter sequence of real-valued vectors to be used as inputs instead of token embeddings or key-value cache. These approaches are focused on reduction of the amount of compute in existing language models rather than minimization of number of bits needed to store text. Despite relying on powerful models as encoders, the maximum attainable lossless compression ratio is typically not higher than x10. This fact is highly intriguing because, in theory, the maximum information capacity of large real-valued vectors is far beyond the presented rates even for 16-bit precision and a modest vector size. In this work, we explore the limits of compression by replacing the encoder with a per-sample optimization procedure. We show that vectors with compression ratios up to x1500 exist, which highlights two orders of magnitude gap between existing and practically attainable solutions. Furthermore, we empirically show that the compression limits are determined not by the length of the input but by the amount of uncertainty to be reduced, namely, the cross-entropy loss on this sequence without any conditioning. The obtained limits highlight the substantial gap between the theoretical capacity of input embeddings and their practical utilization, suggesting significant room for optimization in model design.