paper_id stringlengths 15 18 | title stringlengths 20 147 | paper_url stringlengths 41 44 | authors listlengths 1 92 | abstract large_stringlengths 291 1.99k | anthology_id stringlengths 15 18 | doi stringlengths 27 30 | award stringclasses 5
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
2025.acl-long.1 | EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association | https://aclanthology.org/2025.acl-long.1/ | [
"Weiqi Wang",
"Limeng Cui",
"Xin Liu",
"Sreyashi Nag",
"Wenju Xu",
"Chen Luo",
"Sheikh Muhammad Sarwar",
"Yang Li",
"Hansu Gu",
"Hui Liu",
"Changlong Yu",
"Jiaxin Bai",
"Yifan Gao",
"Haiyang Zhang",
"Qi He",
"Shuiwang Ji",
"Yangqiu Song"
] | Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-commerce, customers increasingly seek LLM-based assistants to generate scripts and recommend products at each step, thereby facilitating conven... | 2025.acl-long.1 | 10.18653/v1/2025.acl-long.1 | null | 2505.15196 | title_snapshot |
2025.acl-long.2 | GraphNarrator: Generating Textual Explanations for Graph Neural Networks | https://aclanthology.org/2025.acl-long.2/ | [
"Bo Pan",
"Zhen Xiong",
"Guanchen Wu",
"Zheng Zhang",
"Yifei Zhang",
"Yuntong Hu",
"Liang Zhao"
] | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despite advancements in graph learning methods, challenges still remain in explainability when graphs are associated with semantic features. In t... | 2025.acl-long.2 | 10.18653/v1/2025.acl-long.2 | null | 2410.15268 | title_snapshot |
2025.acl-long.3 | M-RewardBench: Evaluating Reward Models in Multilingual Settings | https://aclanthology.org/2025.acl-long.3/ | [
"Srishti Gureja",
"Lester James V. Miranda",
"Shayekh Bin Islam",
"Rishabh Maheshwary",
"Drishti Sharma",
"Gusti Winata",
"Nathan Lambert",
"Sebastian Ruder",
"Sara Hooker",
"Marzieh Fadaee"
] | Reward models (RMs) have driven the state-of-the-art performance of LLMs today by enabling the integration of human feedback into the language modeling process. However, RMs are primarily trained and evaluated in English, and their capabilities in multilingual settings remain largely understudied. In this work, we cond... | 2025.acl-long.3 | 10.18653/v1/2025.acl-long.3 | null | 2410.15522 | title_snapshot |
2025.acl-long.4 | ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming | https://aclanthology.org/2025.acl-long.4/ | [
"Xinwei Yang",
"Zhaofeng Liu",
"Chen Huang",
"Jiashuai Zhang",
"Tong Zhang",
"Yifan Zhang",
"Wenqiang Lei"
] | While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding remains elusive due to the fragmented nature of existing studies and their use of diverse, application-specific human feedback. Thus, our w... | 2025.acl-long.4 | 10.18653/v1/2025.acl-long.4 | null | 2505.16667 | title_snapshot |
2025.acl-long.5 | The Impossibility of Fair LLMs | https://aclanthology.org/2025.acl-long.5/ | [
"Jacy Reese Anthis",
"Kristian Lum",
"Michael Ekstrand",
"Avi Feller",
"Chenhao Tan"
] | The rise of general-purpose artificial intelligence (AI) systems, particularly large language models (LLMs), has raised pressing moral questions about how to reduce bias and ensure fairness at scale. Researchers have documented a sort of “bias” in the significant correlations between demographics (e.g., race, gender) i... | 2025.acl-long.5 | 10.18653/v1/2025.acl-long.5 | null | 2406.03198 | title_snapshot |
2025.acl-long.6 | Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process | https://aclanthology.org/2025.acl-long.6/ | [
"Ermo Hua",
"Biqing Qi",
"Kaiyan Zhang",
"Kai Tian",
"Xingtai Lv",
"Ning Ding",
"Bowen Zhou"
] | Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are key processes for aligning Language Models (LMs) with human preferences post pre-training. While SFT excels in efficiency and PO in effectiveness, they are often combined sequentially without integrating their optimization objectives. This approach ignor... | 2025.acl-long.6 | 10.18653/v1/2025.acl-long.6 | null | 2405.11870 | title_snapshot |
2025.acl-long.7 | Bias in Language Models: Beyond Trick Tests and Towards RUTEd Evaluation | https://aclanthology.org/2025.acl-long.7/ | [
"Kristian Lum",
"Jacy Reese Anthis",
"Kevin Robinson",
"Chirag Nagpal",
"Alexander Nicholas D’Amour"
] | Standard bias benchmarks used for large language models (LLMs) measure the association between social attributes in model inputs and single-word model outputs. We test whether these benchmarks are robust to lengthening the model outputs via a more realistic user prompt, in the commonly studied domain of gender-occupati... | 2025.acl-long.7 | 10.18653/v1/2025.acl-long.7 | null | 2402.12649 | title_judge |
2025.acl-long.8 | Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models | https://aclanthology.org/2025.acl-long.8/ | [
"Wenhan Liu",
"Xinyu Ma",
"Yutao Zhu",
"Ziliang Zhao",
"Shuaiqiang Wang",
"Dawei Yin",
"Zhicheng Dou"
] | Large Language Models (LLMs) have shown exciting performance in listwise passage ranking. Due to the limited input length, existing methods often adopt the sliding window strategy. Such a strategy, though effective, is inefficient as it involves repetitive and serialized processing, which usually re-evaluates relevant ... | 2025.acl-long.8 | 10.18653/v1/2025.acl-long.8 | null | 2412.14574 | title_snapshot |
2025.acl-long.9 | The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It | https://aclanthology.org/2025.acl-long.9/ | [
"Aaron Nicolson",
"Shengyao Zhuang",
"Jason Dowling",
"Bevan Koopman"
] | This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Traditionally, CXR report generation relies solely on data from a patient’s CXR exam, overlooking valuable information from patient electronic health records. Utilis... | 2025.acl-long.9 | 10.18653/v1/2025.acl-long.9 | null | 2406.13181 | title_snapshot |
2025.acl-long.10 | CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction | https://aclanthology.org/2025.acl-long.10/ | [
"Jingheng Ye",
"Zishan Xu",
"Yinghui Li",
"Linlin Song",
"Qingyu Zhou",
"Hai-Tao Zheng",
"Ying Shen",
"Wenhao Jiang",
"Hong-Gee Kim",
"Ruitong Liu",
"Xin Su",
"Zifei Shan"
] | The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we introduce **CLEME2.0**, a reference-based metric describing four fundamental aspects of GEC systems: hit-correction, wrong-correction, under-corr... | 2025.acl-long.10 | 10.18653/v1/2025.acl-long.10 | null | 2407.00934 | title_snapshot |
2025.acl-long.11 | StrucText-Eval: Evaluating Large Language Model’s Reasoning Ability in Structure-Rich Text | https://aclanthology.org/2025.acl-long.11/ | [
"Zhouhong Gu",
"Haoning Ye",
"Xingzhou Chen",
"Zeyang Zhou",
"Hongwei Feng",
"Yanghua Xiao"
] | The effective utilization of structured data, integral to corporate data strategies, has been challenged by the rise of large language models (LLMs) capable of processing unstructured information. This shift prompts the question: can LLMs interpret structured data directly in its unstructured form? We propose an automa... | 2025.acl-long.11 | 10.18653/v1/2025.acl-long.11 | null | 2406.10621 | title_snapshot |
2025.acl-long.12 | Literature Meets Data: A Synergistic Approach to Hypothesis Generation | https://aclanthology.org/2025.acl-long.12/ | [
"Haokun Liu",
"Yangqiaoyu Zhou",
"Mingxuan Li",
"Chenfei Yuan",
"Chenhao Tan"
] | AI holds promise for transforming scientific processes, including hypothesis generation. Prior work on hypothesis generation can be broadly categorized into theory-driven and data-driven approaches. While both have proven effective in generating novel and plausible hypotheses, it remains an open question whether they c... | 2025.acl-long.12 | 10.18653/v1/2025.acl-long.12 | null | 2410.17309 | title_snapshot |
2025.acl-long.13 | GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization | https://aclanthology.org/2025.acl-long.13/ | [
"Zhouhong Gu",
"Xingzhou Chen",
"Xiaoran Shi",
"Tao Wang",
"Suhang Zheng",
"Tianyu Li",
"Hongwei Feng",
"Yanghua Xiao"
] | Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to achieve this through either direct instruction-response synthesis or preferential response optimization, they often struggle with constrain... | 2025.acl-long.13 | 10.18653/v1/2025.acl-long.13 | null | 2503.20194 | title_snapshot |
2025.acl-long.14 | Tree-of-Evolution: Tree-Structured Instruction Evolution for Code Generation in Large Language Models | https://aclanthology.org/2025.acl-long.14/ | [
"Ziyang Luo",
"Kaixin Li",
"Hongzhan Lin",
"Yuchen Tian",
"Mohan Kankanhalli",
"Jing Ma"
] | Data synthesis has become a crucial research area in large language models (LLMs), especially for generating high-quality instruction fine-tuning data to enhance downstream performance. In code generation, a key application of LLMs, manual annotation of code instruction data is costly. Recent methods, such as Code Evol... | 2025.acl-long.14 | 10.18653/v1/2025.acl-long.14 | null | null | null |
2025.acl-long.15 | Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models | https://aclanthology.org/2025.acl-long.15/ | [
"Seunguk Yu",
"Juhwan Choi",
"YoungBin Kim"
] | Despite the recent strides in large language models, studies have underscored the existence of social biases within these systems. In this paper, we delve into the validation and comparison of the ethical biases of LLMs concerning globally discussed and potentially sensitive topics, hypothesizing that these biases may ... | 2025.acl-long.15 | 10.18653/v1/2025.acl-long.15 | null | 2505.19121 | title_snapshot |
2025.acl-long.16 | ReSCORE: Label-free Iterative Retriever Training for Multi-hop Question Answering with Relevance-Consistency Supervision | https://aclanthology.org/2025.acl-long.16/ | [
"Dosung Lee",
"Wonjun Oh",
"Boyoung Kim",
"Minyoung Kim",
"Joonsuk Park",
"Paul Hongsuck Seo"
] | Multi-hop question answering (MHQA) involves reasoning across multiple documents to answer complex questions. Dense retrievers typically outperform sparse methods like BM25 by leveraging semantic embeddings in many tasks; however, they require labeled query-document pairs for fine-tuning, which poses a significant chal... | 2025.acl-long.16 | 10.18653/v1/2025.acl-long.16 | null | 2505.21250 | title_snapshot |
2025.acl-long.17 | FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models | https://aclanthology.org/2025.acl-long.17/ | [
"Hongzhan Lin",
"Yang Deng",
"Yuxuan Gu",
"Wenxuan Zhang",
"Jing Ma",
"See-Kiong Ng",
"Tat-Seng Chua"
] | Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classification metrics, which fail to automatically evaluate the justification production and uncover the nuanced limitations of LLMs in fact-check... | 2025.acl-long.17 | 10.18653/v1/2025.acl-long.17 | null | 2502.17924 | title_snapshot |
2025.acl-long.18 | Statistical Deficiency for Task Inclusion Estimation | https://aclanthology.org/2025.acl-long.18/ | [
"Loïc Fosse",
"Frederic Bechet",
"Benoit Favre",
"Géraldine Damnati",
"Gwénolé Lecorvé",
"Maxime Darrin",
"Philippe Formont",
"Pablo Piantanida"
] | Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any task. Even though transfer learning and multitask learning try to leverage the underlying task space, no well-founded tools are available to ... | 2025.acl-long.18 | 10.18653/v1/2025.acl-long.18 | null | 2503.05491 | title_snapshot |
2025.acl-long.19 | Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients | https://aclanthology.org/2025.acl-long.19/ | [
"Jabin Koo",
"Minwoo Jang",
"Jungseul Ok"
] | Federated fine-tuning for Large Language Models (LLMs) has recently gained attention due to the heavy communication overhead of transmitting large model updates. Low Rank Adaptation (LoRA) has been proposed as a solution, yet its application in federated learning is complicated by discordance in aggregation. Existing m... | 2025.acl-long.19 | 10.18653/v1/2025.acl-long.19 | null | 2410.22815 | title_snapshot |
2025.acl-long.20 | LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs | https://aclanthology.org/2025.acl-long.20/ | [
"Kaibo Liu",
"Zhenpeng Chen",
"Yiyang Liu",
"Jie M. Zhang",
"Mark Harman",
"Yudong Han",
"Yun Ma",
"Yihong Dong",
"Ge Li",
"Gang Huang"
] | Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this problem, we propose TrickCatcher, an LLM-powered approach to generating test cases for uncovering bugs in plausible programs. TrickCatcher operat... | 2025.acl-long.20 | 10.18653/v1/2025.acl-long.20 | null | 2404.10304 | title_snapshot |
2025.acl-long.21 | Capture the Key in Reasoning to Enhance CoT Distillation Generalization | https://aclanthology.org/2025.acl-long.21/ | [
"Chengwei Dai",
"Kun Li",
"Wei Zhou",
"Songlin Hu"
] | As Large Language Models (LLMs) scale up and gain powerful Chain-of-Thoughts (CoTs) reasoning abilities, practical resource constraints drive efforts to distill these capabilities into more compact Smaller Language Models (SLMs). We find that CoTs consist mainly of simple reasoning forms, with a small proportion (4.7%)... | 2025.acl-long.21 | 10.18653/v1/2025.acl-long.21 | null | 2405.19737 | title_judge |
2025.acl-long.22 | How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond | https://aclanthology.org/2025.acl-long.22/ | [
"Chen Huang",
"Yang Deng",
"Wenqiang Lei",
"Jiancheng Lv",
"Tat-Seng Chua",
"Jimmy Xiangji Huang"
] | With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with humans. This evolution has birthed a novel paradigm in NLP, i.e., human-model cooperation, that has yielded remarkable progress in numerous ... | 2025.acl-long.22 | 10.18653/v1/2025.acl-long.22 | null | 2501.05714 | title_snapshot |
2025.acl-long.23 | Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge | https://aclanthology.org/2025.acl-long.23/ | [
"Li Zheng",
"Sihang Wang",
"Hao Fei",
"Zuquan Peng",
"Fei Li",
"Jianming Fu",
"Chong Teng",
"Donghong Ji"
] | Text-based hyperbole and metaphor detection are of great significance for natural language processing (NLP) tasks. However, due to their semantic obscurity and expressive diversity, it is rather challenging to identify them. Existing methods mostly focus on superficial text features, ignoring the associations of hyperb... | 2025.acl-long.23 | 10.18653/v1/2025.acl-long.23 | null | 2506.15504 | title_snapshot |
2025.acl-long.24 | UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation | https://aclanthology.org/2025.acl-long.24/ | [
"Jun Gao",
"Qi Lv",
"Zili Wang",
"Tianxiang Wu",
"Ziqiang Cao",
"Wenjie Li"
] | In-context learning (ICL) enhances the reasoning abilities of Large Language Models (LLMs) by prepending a few demonstrations. It motivates researchers to introduce more examples to provide additional contextual information for the generation. However, existing methods show a significant limitation due to the problem o... | 2025.acl-long.24 | 10.18653/v1/2025.acl-long.24 | null | 2405.17062 | title_judge |
2025.acl-long.25 | BelarusianGLUE: Towards a Natural Language Understanding Benchmark for Belarusian | https://aclanthology.org/2025.acl-long.25/ | [
"Maksim Aparovich",
"Volha Harytskaya",
"Vladislav Poritski",
"Oksana Volchek",
"Pavel Smrz"
] | In the epoch of multilingual large language models (LLMs), it is still challenging to evaluate the models’ understanding of lower-resourced languages, which motivates further development of expert-crafted natural language understanding benchmarks. We introduce BelarusianGLUE — a natural language understanding benchmark... | 2025.acl-long.25 | 10.18653/v1/2025.acl-long.25 | null | null | null |
2025.acl-long.26 | A Survey on Foundation Language Models for Single-cell Biology | https://aclanthology.org/2025.acl-long.26/ | [
"Fan Zhang",
"Hao Chen",
"Zhihong Zhu",
"Ziheng Zhang",
"Zhenxi Lin",
"Ziyue Qiao",
"Yefeng Zheng",
"Xian Wu"
] | The recent advancements in language models have significantly catalyzed progress in computational biology. A growing body of research strives to construct unified foundation models for single-cell biology, with language models serving as the cornerstone. In this paper, we systematically review the developments in found... | 2025.acl-long.26 | 10.18653/v1/2025.acl-long.26 | null | null | null |
2025.acl-long.27 | RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios | https://aclanthology.org/2025.acl-long.27/ | [
"Ruiwen Zhou",
"Wenyue Hua",
"Liangming Pan",
"Sitao Cheng",
"Xiaobao Wu",
"En Yu",
"William Yang Wang"
] | This paper introduces RuleArena, a novel and challenging benchmark designed to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. Covering three practical domains – airline baggage fees, NBA transactions, and tax regulations – RuleArena assesses LLMs’ proficiency in h... | 2025.acl-long.27 | 10.18653/v1/2025.acl-long.27 | null | 2412.08972 | title_snapshot |
2025.acl-long.28 | Extending LLM Context Window with Adaptive Grouped Positional Encoding: A Training-Free Method | https://aclanthology.org/2025.acl-long.28/ | [
"Xinhao Xu",
"Jiaxin Li",
"Hui Chen",
"Zijia Lin",
"Jungong Han",
"Guiguang Ding"
] | Processing long input remains a significant challenge for large language models (LLMs) due to the scarcity of large-scale long-context training data and the high computational cost of training models for extended context windows. In this paper, we propose **Ada**ptive **Gro**uped **P**ositional **E**ncoding (AdaGroPE),... | 2025.acl-long.28 | 10.18653/v1/2025.acl-long.28 | null | null | null |
2025.acl-long.29 | Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models | https://aclanthology.org/2025.acl-long.29/ | [
"Sungjae Lee",
"Hyejin Park",
"Jaechang Kim",
"Jungseul Ok"
] | Recent advancements in large language models (LLMs) have shown remarkable potential in various complex tasks requiring multi-step reasoning methods like tree search to explore diverse reasoning paths. However, existing methods often suffer from computational inefficiency and redundancy. First, they overlook the diversi... | 2025.acl-long.29 | 10.18653/v1/2025.acl-long.29 | null | 2501.05752 | title_snapshot |
2025.acl-long.30 | HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval | https://aclanthology.org/2025.acl-long.30/ | [
"Arian Askari",
"Emmanouil Stergiadis",
"Ilya Gusev",
"Moran Beladev"
] | We present HotelMatch-LLM, a multimodal dense retrieval model for the travel domain that enables natural language property search, addressing the limitations of traditional travel search engines which require users to start with a destination and editing search parameters. HotelMatch-LLM features three key innovations:... | 2025.acl-long.30 | 10.18653/v1/2025.acl-long.30 | null | 2506.07296 | title_snapshot |
2025.acl-long.31 | Can Multimodal Large Language Models Understand Spatial Relations? | https://aclanthology.org/2025.acl-long.31/ | [
"Jingping Liu",
"Ziyan Liu",
"Zhedong Cen",
"Yan Zhou",
"Yinan Zou",
"Weiyan Zhang",
"Haiyun Jiang",
"Tong Ruan"
] | Spatial relation reasoning is a crucial task for multimodal large language models (MLLMs) to understand the objective world. However, current benchmarks have issues like relying on bounding boxes, ignoring perspective substitutions, or allowing questions to be answered using only the model’s prior knowledge without ima... | 2025.acl-long.31 | 10.18653/v1/2025.acl-long.31 | null | 2505.19015 | title_snapshot |
2025.acl-long.32 | S^3 - Semantic Signal Separation | https://aclanthology.org/2025.acl-long.32/ | [
"Márton Kardos",
"Jan Kostkan",
"Kenneth Enevoldsen",
"Arnault-Quentin Vermillet",
"Kristoffer Nielbo",
"Roberta Rocca"
] | Topic models are useful tools for discovering latent semantic structures in large textual corpora. Recent efforts have been oriented at incorporating contextual representations in topic modeling and have been shown to outperform classical topic models. These approaches are typically slow, volatile, and require heavy pr... | 2025.acl-long.32 | 10.18653/v1/2025.acl-long.32 | null | 2406.09556 | title_snapshot |
2025.acl-long.33 | TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs | https://aclanthology.org/2025.acl-long.33/ | [
"Lanxiang Hu",
"Tajana Rosing",
"Hao Zhang"
] | Specializing large language models (LLMs) for local deployment in domain-specific use cases is necessary for strong performance while meeting latency and privacy constraints. However, conventional task-specific adaptation approaches do not show simultaneous memory saving and inference speedup at deployment time. Practi... | 2025.acl-long.33 | 10.18653/v1/2025.acl-long.33 | null | 2412.11242 | title_snapshot |
2025.acl-long.34 | JuStRank: Benchmarking LLM Judges for System Ranking | https://aclanthology.org/2025.acl-long.34/ | [
"Ariel Gera",
"Odellia Boni",
"Yotam Perlitz",
"Roy Bar-Haim",
"Lilach Eden",
"Asaf Yehudai"
] | Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and versatility of such evaluations make the use of LLM-based judges a compelling solution for this challenge. Crucially, this approach requires fi... | 2025.acl-long.34 | 10.18653/v1/2025.acl-long.34 | null | 2412.09569 | title_snapshot |
2025.acl-long.35 | Generating Diverse Training Samples for Relation Extraction with Large Language Models | https://aclanthology.org/2025.acl-long.35/ | [
"Zexuan Li",
"Hongliang Dai",
"Piji Li"
] | Using Large Language Models (LLMs) to generate training data can potentially be a preferable way to improve zero or few-shot NLP tasks. However, many problems remain to be investigated for this direction. For the task of Relation Extraction (RE), we find that samples generated by directly prompting LLMs may easily have... | 2025.acl-long.35 | 10.18653/v1/2025.acl-long.35 | null | 2505.23108 | title_snapshot |
2025.acl-long.36 | MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts | https://aclanthology.org/2025.acl-long.36/ | [
"Dominik Macko",
"Jakub Kopál",
"Robert Moro",
"Ivan Srba"
] | Recent LLMs are able to generate high-quality multilingual texts, indistinguishable for humans from authentic human-written ones. Research in machine-generated text detection is however mostly focused on the English language and longer texts, such as news articles, scientific papers or student essays. Social-media text... | 2025.acl-long.36 | 10.18653/v1/2025.acl-long.36 | null | 2406.12549 | title_snapshot |
2025.acl-long.37 | Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection | https://aclanthology.org/2025.acl-long.37/ | [
"Cilin Yan",
"Jingyun Wang",
"Lin Zhang",
"Ruihui Zhao",
"Xiaopu Wu",
"Kai Xiong",
"Qingsong Liu",
"Guoliang Kang",
"Yangyang Kang"
] | Automatic prompt engineering aims to enhance the generation quality of large language models (LLMs). Recent works utilize feedbacks generated from erroneous cases to guide the prompt optimization. During inference, they may further retrieve several semantically-related exemplars and concatenate them to the optimized pr... | 2025.acl-long.37 | 10.18653/v1/2025.acl-long.37 | null | 2411.07446 | title_snapshot |
2025.acl-long.38 | Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation | https://aclanthology.org/2025.acl-long.38/ | [
"Aneta Zugecova",
"Dominik Macko",
"Ivan Srba",
"Robert Moro",
"Jakub Kopál",
"Katarína Marcinčinová",
"Matúš Mesarčík"
] | The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined nar... | 2025.acl-long.38 | 10.18653/v1/2025.acl-long.38 | null | 2412.13666 | title_snapshot |
2025.acl-long.39 | EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents | https://aclanthology.org/2025.acl-long.39/ | [
"Cheng Qian",
"Peixuan Han",
"Qinyu Luo",
"Bingxiang He",
"Xiusi Chen",
"Yuji Zhang",
"Hongyi Du",
"Jiarui Yao",
"Xiaocheng Yang",
"Denghui Zhang",
"Yunzhu Li",
"Heng Ji"
] | Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative adaptation in unfamiliar environments. To address this, we introduce EscapeBench—a benchmark suite of room escape game environments designed to... | 2025.acl-long.39 | 10.18653/v1/2025.acl-long.39 | null | 2412.13549 | title_snapshot |
2025.acl-long.40 | BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving | https://aclanthology.org/2025.acl-long.40/ | [
"Teng Wang",
"Wing Yin Yu",
"Zhenqi He",
"Zehua Liu",
"HaileiGong HaileiGong",
"Han Wu",
"Xiongwei Han",
"Wei Shi",
"Ruifeng She",
"Fangzhou Zhu",
"Tao Zhong"
] | LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in operations research domain lack detailed annotations of the modeling process, such as variable definitions, focusing solely on objective values... | 2025.acl-long.40 | 10.18653/v1/2025.acl-long.40 | null | 2411.17404 | title_snapshot |
2025.acl-long.41 | LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation | https://aclanthology.org/2025.acl-long.41/ | [
"Jakub Šmíd",
"Pavel Priban",
"Pavel Kral"
] | Cross-lingual aspect-based sentiment analysis (ABSA) involves detailed sentiment analysis in a target language by transferring knowledge from a source language with available annotated data. Most existing methods depend heavily on often unreliable translation tools to bridge the language gap. In this paper, we propose ... | 2025.acl-long.41 | 10.18653/v1/2025.acl-long.41 | null | 2508.09515 | title_snapshot |
2025.acl-long.42 | Fusing Highly Specialized Language Models for Comprehensive Expertise | https://aclanthology.org/2025.acl-long.42/ | [
"Ning Ding",
"Yulin Chen",
"Ganqu Cui",
"Xingtai Lv",
"Weilin Zhao",
"Kaiyan Zhang",
"Ruobing Xie",
"Bowen Zhou",
"Zhiyuan Liu",
"Maosong Sun"
] | Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that strive to achieve high performance across all three domains simultaneously. Achieving a very high level of proficiency for an LLM within a speci... | 2025.acl-long.42 | 10.18653/v1/2025.acl-long.42 | null | null | null |
2025.acl-long.43 | HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases | https://aclanthology.org/2025.acl-long.43/ | [
"Meng-Chieh Lee",
"Qi Zhu",
"Costas Mavromatis",
"Zhen Han",
"Soji Adeshina",
"Vassilis N. Ioannidis",
"Huzefa Rangwala",
"Christos Faloutsos"
] | Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions?Retrieval-Augmented Generation (RAG) retrieves documents to assist large language models (LLMs) in question answering; while Graph RAG (GRAG) ... | 2025.acl-long.43 | 10.18653/v1/2025.acl-long.43 | null | 2412.16311 | title_snapshot |
2025.acl-long.44 | Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms | https://aclanthology.org/2025.acl-long.44/ | [
"Rajvardhan Oak",
"Muhammad Haroon",
"Claire Wonjeong Jo",
"Magdalena Wojcieszak",
"Anshuman Chhabra"
] | Social media platforms utilize Machine Learning (ML) and Artificial Intelligence (AI) powered recommendation algorithms to maximize user engagement, which can result in inadvertent exposure to harmful content. Current moderation efforts, reliant on classifiers trained with extensive human-annotated data, struggle with ... | 2025.acl-long.44 | 10.18653/v1/2025.acl-long.44 | null | 2501.13977 | title_snapshot |
2025.acl-long.45 | Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review | https://aclanthology.org/2025.acl-long.45/ | [
"Yidong Gan",
"Maciej Rybinski",
"Ben Hachey",
"Jonathan K. Kummerfeld"
] | Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, sh... | 2025.acl-long.45 | 10.18653/v1/2025.acl-long.45 | null | 2412.18043 | title_snapshot |
2025.acl-long.46 | MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection | https://aclanthology.org/2025.acl-long.46/ | [
"Ziyan Liu",
"Chunxiao Fan",
"Haoran Lou",
"Yuexin Wu",
"Kaiwei Deng"
] | The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a mult... | 2025.acl-long.46 | 10.18653/v1/2025.acl-long.46 | null | 2507.06908 | title_snapshot |
2025.acl-long.47 | EvoWiki: Evaluating LLMs on Evolving Knowledge | https://aclanthology.org/2025.acl-long.47/ | [
"Wei Tang",
"Yixin Cao",
"Yang Deng",
"Jiahao Ying",
"Bo Wang",
"Yizhe Yang",
"Yuyue Zhao",
"Qi Zhang",
"Xuanjing Huang",
"Yu-Gang Jiang",
"Yong Liao"
] | Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as... | 2025.acl-long.47 | 10.18653/v1/2025.acl-long.47 | null | 2412.13582 | title_snapshot |
2025.acl-long.48 | Rethinking Repetition Problems of LLMs in Code Generation | https://aclanthology.org/2025.acl-long.48/ | [
"Yihong Dong",
"Yuchen Liu",
"Xue Jiang",
"Bin Gu",
"Zhi Jin",
"Ge Li"
] | With the advent of neural language models, the performance of code generation has been significantly boosted. However, the problem of repetitions during the generation process continues to linger. Previous work has primarily focused on content repetition, which is merely a fraction of the broader repetition problem in ... | 2025.acl-long.48 | 10.18653/v1/2025.acl-long.48 | null | 2505.10402 | title_snapshot |
2025.acl-long.49 | PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension | https://aclanthology.org/2025.acl-long.49/ | [
"Kun Ouyang",
"Yuanxin Liu",
"Shicheng Li",
"Yi Liu",
"Hao Zhou",
"Fandong Meng",
"Jie Zhou",
"Xu Sun"
] | Multimodal punchlines, which involve humor or sarcasm conveyed in image-caption pairs, are a popular way of communication on online multimedia platforms. With the rapid development of multimodal large language models (MLLMs), it is essential to assess their ability to effectively comprehend these punchlines. However, e... | 2025.acl-long.49 | 10.18653/v1/2025.acl-long.49 | null | 2412.11906 | title_snapshot |
2025.acl-long.50 | ProcessBench: Identifying Process Errors in Mathematical Reasoning | https://aclanthology.org/2025.acl-long.50/ | [
"Chujie Zheng",
"Zhenru Zhang",
"Beichen Zhang",
"Runji Lin",
"Keming Lu",
"Bowen Yu",
"Dayiheng Liu",
"Jingren Zhou",
"Junyang Lin"
] | As language models regularly make mistakes when solving math problems, automated identification of errors in the reasoning process becomes increasingly significant for their scalable oversight. In this paper, we introduce ProcessBench for measuring the ability to identify erroneous steps in mathematical reasoning. It c... | 2025.acl-long.50 | 10.18653/v1/2025.acl-long.50 | null | 2412.06559 | title_snapshot |
2025.acl-long.51 | Model Extrapolation Expedites Alignment | https://aclanthology.org/2025.acl-long.51/ | [
"Chujie Zheng",
"Ziqi Wang",
"Heng Ji",
"Minlie Huang",
"Nanyun Peng"
] | Given the high computational cost of preference alignment training of large language models (LLMs), exploring efficient methods to reduce the training overhead remains an important and compelling research problem. Motivated by the observation that alignment training typically involves only small parameter changes witho... | 2025.acl-long.51 | 10.18653/v1/2025.acl-long.51 | null | 2404.16792 | title_snapshot |
2025.acl-long.52 | ATLANTIS: Weak-to-Strong Learning via Importance Sampling | https://aclanthology.org/2025.acl-long.52/ | [
"Yi Liu",
"Guoyin Wang",
"Shicheng Li",
"Feifan Song",
"Xu Sun"
] | Supervised fine-tuning (SFT) enables large language models to align with training data for better performance in many aspects. Nevertheless, the gap between the distribution of current datasets from human annotations or model generations and the real-world data distribution heavily limits the capacities and potentials ... | 2025.acl-long.52 | 10.18653/v1/2025.acl-long.52 | null | null | null |
2025.acl-long.53 | MPVStance: Mitigating Hallucinations in Stance Detection with Multi-Perspective Verification | https://aclanthology.org/2025.acl-long.53/ | [
"ZhaoDan Zhang",
"Zhao Zhang",
"Jin Zhang",
"Hui Xu",
"Xueqi Cheng"
] | Stance detection is a pivotal task in Natural Language Processing (NLP), identifying textual attitudes toward various targets. Despite advances in using Large Language Models (LLMs), challenges persist due to hallucination-models generating plausible yet inaccurate content. Addressing these challenges, we introduce MPV... | 2025.acl-long.53 | 10.18653/v1/2025.acl-long.53 | null | null | null |
2025.acl-long.54 | Personality-Guided Code Generation Using Large Language Models | https://aclanthology.org/2025.acl-long.54/ | [
"Yaoqi Guo",
"Zhenpeng Chen",
"Jie M. Zhang",
"Yang Liu",
"Yun Ma"
] | Code generation, the automatic creation of source code from natural language descriptions, has garnered significant attention due to its potential to streamline software development. Inspired by research that links task-personality alignment with improved development outcomes, we conduct an empirical study on personali... | 2025.acl-long.54 | 10.18653/v1/2025.acl-long.54 | null | 2411.00006 | title_snapshot |
2025.acl-long.55 | PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling | https://aclanthology.org/2025.acl-long.55/ | [
"Haojie Xie",
"Yirong Chen",
"Xiaofen Xing",
"Jingkai Lin",
"Xiangmin Xu"
] | Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that different psychological counselors exhibit different personal styles, including linguistic style and th... | 2025.acl-long.55 | 10.18653/v1/2025.acl-long.55 | null | 2412.13660 | title_snapshot |
2025.acl-long.56 | BIPro: Zero-shot Chinese Poem Generation via Block Inverse Prompting Constrained Generation Framework | https://aclanthology.org/2025.acl-long.56/ | [
"Xu Zou"
] | Recently, generative pre-trained models have made significant strides, particularly highlighted by the release of ChatGPT and GPT-4, which exhibit superior cross-domain capabilities. However, these models still face challenges on constrained writing tasks like poem generation under open-domain titles via direct generat... | 2025.acl-long.56 | 10.18653/v1/2025.acl-long.56 | null | 2411.13237 | title_snapshot |
2025.acl-long.57 | LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating | https://aclanthology.org/2025.acl-long.57/ | [
"Chao Deng",
"Jiale Yuan",
"Pi Bu",
"Peijie Wang",
"Zhong-Zhi Li",
"Jian Xu",
"Xiao-Hui Li",
"Yuan Gao",
"Jun Song",
"Bo Zheng",
"Cheng-Lin Liu"
] | Large vision language models (LVLMs) have improved the document understanding capabilities remarkably, enabling the handling of complex document elements, longer contexts, and a wider range of tasks. However, existing document understanding benchmarks have been limited to handling only a small number of pages and fail ... | 2025.acl-long.57 | 10.18653/v1/2025.acl-long.57 | null | 2412.18424 | title_snapshot |
2025.acl-long.58 | ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks | https://aclanthology.org/2025.acl-long.58/ | [
"Yu Lin",
"Ruining Yang",
"Yunlong Mao",
"Qizhi Zhang",
"Jue Hong",
"Quanwei Cai",
"Ye Wu",
"Huiqi Liu",
"Zhiyu Chen",
"Bing Duan",
"Sheng Zhong"
] | As the rapid expansion of Machine Learning as a Service (MLaaS) for language models, concerns over the privacy of client inputs during inference or fine-tuning have correspondingly escalated. Recently, solutions have been proposed to safeguard client privacy by obfuscation techniques. However, the solutions incur notab... | 2025.acl-long.58 | 10.18653/v1/2025.acl-long.58 | null | null | null |
2025.acl-long.59 | Interlocking-free Selective Rationalization Through Genetic-based Learning | https://aclanthology.org/2025.acl-long.59/ | [
"Federico Ruggeri",
"Gaetano Signorelli"
] | A popular end-to-end architecture for selective rationalization is the select-then-predict pipeline, comprising a generator to extract highlights fed to a predictor. Such a cooperative system suffers from suboptimal equilibrium minima due to the dominance of one of the two modules, a phenomenon known as interlocking. W... | 2025.acl-long.59 | 10.18653/v1/2025.acl-long.59 | null | 2412.10312 | title_snapshot |
2025.acl-long.60 | Re-identification of De-identified Documents with Autoregressive Infilling | https://aclanthology.org/2025.acl-long.60/ | [
"Lucas Georges Gabriel Charpentier",
"Pierre Lison"
] | Documents revealing sensitive information about individuals must typically be de-identified. This de-identification is often done by masking all mentions of personally identifiable information (PII), thereby making it more difficult to uncover the identity of the person(s) in question. To investigate the robustness of ... | 2025.acl-long.60 | 10.18653/v1/2025.acl-long.60 | null | 2505.12859 | title_snapshot |
2025.acl-long.61 | Modeling Uncertainty in Composed Image Retrieval via Probabilistic Embeddings | https://aclanthology.org/2025.acl-long.61/ | [
"Haomiao Tang",
"Jinpeng Wang",
"Yuang Peng",
"GuangHao Meng",
"Ruisheng Luo",
"Bin Chen",
"Long Chen",
"Yaowei Wang",
"Shu-Tao Xia"
] | Composed Image Retrieval (CIR) enables users to search for images using multimodal queries that combine text and reference images. While metric learning methods have shown promise, they rely on deterministic point embeddings that fail to capture the inherent uncertainty in the input data, in which user intentions may b... | 2025.acl-long.61 | 10.18653/v1/2025.acl-long.61 | null | null | null |
2025.acl-long.62 | Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models | https://aclanthology.org/2025.acl-long.62/ | [
"Junfeng Tian",
"Da Zheng",
"Yang Chen",
"Rui Wang",
"Colin Zhang",
"Debing Zhang"
] | Large language models (LLM) have prioritized expanding the context window from which models can incorporate more information. However, training models to handle long contexts presents significant challenges. These include the scarcity of high-quality natural long-context data, the potential for performance degradation ... | 2025.acl-long.62 | 10.18653/v1/2025.acl-long.62 | null | 2409.04774 | title_snapshot |
2025.acl-long.63 | APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts | https://aclanthology.org/2025.acl-long.63/ | [
"Honghua Dong",
"Qidong Su",
"Yubo Gao",
"Zhaoyu Li",
"Yangjun Ruan",
"Gennady Pekhimenko",
"Chris J. Maddison",
"Xujie Si"
] | Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexity rises, the workflow involving LLMs can be complicated and thus challenging to implement and maintain. To address this challenge, we propo... | 2025.acl-long.63 | 10.18653/v1/2025.acl-long.63 | null | 2406.13161 | title_snapshot |
2025.acl-long.64 | Evaluating Lexical Proficiency in Neural Language Models | https://aclanthology.org/2025.acl-long.64/ | [
"Cristiano Ciaccio",
"Alessio Miaschi",
"Felice Dell’Orletta"
] | We present a novel evaluation framework designed to assess the lexical proficiency and linguistic creativity of Transformer-based Language Models (LMs). We validate the framework by analyzing the performance of a set of LMs of different sizes, in both mono- and multilingual configuration, across tasks involving the gen... | 2025.acl-long.64 | 10.18653/v1/2025.acl-long.64 | null | null | null |
2025.acl-long.65 | Autoregressive Speech Synthesis without Vector Quantization | https://aclanthology.org/2025.acl-long.65/ | [
"Lingwei Meng",
"Long Zhou",
"Shujie Liu",
"Sanyuan Chen",
"Bing Han",
"Shujie Hu",
"Yanqing Liu",
"Jinyu Li",
"Sheng Zhao",
"Xixin Wu",
"Helen M. Meng",
"Furu Wei"
] | We present MELLE, a novel continuous-valued token based language modeling approach for text-to-speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram frames directly from text condition, bypassing the need for vector quantization, which is typically designed for audio compression and sacrif... | 2025.acl-long.65 | 10.18653/v1/2025.acl-long.65 | null | 2407.08551 | title_snapshot |
2025.acl-long.66 | Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM’s Nest | https://aclanthology.org/2025.acl-long.66/ | [
"Letian Peng",
"Zilong Wang",
"Feng Yao",
"Jingbo Shang"
] | Massive high-quality data, both pre-training raw texts and post-training annotations, have been carefully prepared to incubate advanced large language models (LLMs). In contrast, for information extraction (IE), pre-training data, such as BIO-tagged sequences, are hard to scale up. We show that IE models can act as fre... | 2025.acl-long.66 | 10.18653/v1/2025.acl-long.66 | null | 2502.11275 | title_snapshot |
2025.acl-long.67 | FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models | https://aclanthology.org/2025.acl-long.67/ | [
"Raghav Singhal",
"Kaustubh Ponkshe",
"Praneeth Vepakomma"
] | Low-Rank Adaptation (LoRA) is a popular technique for efficient fine-tuning of foundation models. However, applying LoRA in federated learning environments, where data is distributed across multiple clients, presents unique challenges. Existing methods rely on traditional federated averaging of LoRA adapters, resulting... | 2025.acl-long.67 | 10.18653/v1/2025.acl-long.67 | null | 2410.09432 | title_judge |
2025.acl-long.68 | Measuring Social Biases in Masked Language Models by Proxy of Prediction Quality | https://aclanthology.org/2025.acl-long.68/ | [
"Rahul Zalkikar",
"Kanchan Chandra"
] | Innovative transformer-based language models produce contextually-aware token embeddings and have achieved state-of-the-art performance for a variety of natural language tasks, but have been shown to encode unwanted biases for downstream applications. In this paper, we evaluate the social biases encoded by transformers... | 2025.acl-long.68 | 10.18653/v1/2025.acl-long.68 | null | 2402.13954 | title_snapshot |
2025.acl-long.69 | Capturing Author Self Beliefs in Social Media Language | https://aclanthology.org/2025.acl-long.69/ | [
"Siddharth Mangalik",
"Adithya V. Ganesan",
"Abigail Wheeler",
"Nicholas Kerry",
"Jeremy D. W. Clifton",
"H. Andrew Schwartz",
"Ryan L. Boyd"
] | Measuring the prevalence and dimensions of self beliefs is essential for understanding human self-perception and various psychological outcomes. In this paper, we develop a novel task for classifying language that contains explicit or implicit mentions of the author’s self beliefs. We contribute a set of 2,000 human-an... | 2025.acl-long.69 | 10.18653/v1/2025.acl-long.69 | null | null | null |
2025.acl-long.70 | Neural Topic Modeling with Large Language Models in the Loop | https://aclanthology.org/2025.acl-long.70/ | [
"Xiaohao Yang",
"He Zhao",
"Weijie Xu",
"Yuanyuan Qi",
"Jueqing Lu",
"Dinh Phung",
"Lan Du"
] | Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, their direct application to topic modeling suffers from issues such as incomplete top... | 2025.acl-long.70 | 10.18653/v1/2025.acl-long.70 | null | 2411.08534 | title_snapshot |
2025.acl-long.71 | HALoGEN: Fantastic LLM Hallucinations and Where to Find Them | https://aclanthology.org/2025.acl-long.71/ | [
"Abhilasha Ravichander",
"Shrusti Ghela",
"David Wadden",
"Yejin Choi"
] | Despite their impressive ability to generate high-quality and fluent text, generative large language models (LLMs) also produce hallucinations: statements that are misaligned with established world knowledge or provided input context. However, measuring hallucination can be challenging, as having humans verify model ge... | 2025.acl-long.71 | 10.18653/v1/2025.acl-long.71 | Outstanding Paper | 2501.08292 | title_snapshot |
2025.acl-long.72 | Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection | https://aclanthology.org/2025.acl-long.72/ | [
"Shuguo Hu",
"Jun Hu",
"Huaiwen Zhang"
] | Large Language Models (LLMs) can assist multimodal fake news detection by predicting pseudo labels. However, LLM-generated pseudo labels alone demonstrate poor performance compared to traditional detection methods, making their effective integration non-trivial. In this paper, we propose Global Label Propagation Networ... | 2025.acl-long.72 | 10.18653/v1/2025.acl-long.72 | null | 2506.00488 | title_snapshot |
2025.acl-long.73 | “Yes, My LoRD.” Guiding Language Model Extraction with Locality Reinforced Distillation | https://aclanthology.org/2025.acl-long.73/ | [
"Zi Liang",
"Qingqing Ye",
"Yanyun Wang",
"Sen Zhang",
"Yaxin Xiao",
"RongHua Li",
"Jianliang Xu",
"Haibo Hu"
] | Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extraction strategies originally developed for deep neural networks (DNNs). They neglect the underlying inconsistency between the training tasks of ... | 2025.acl-long.73 | 10.18653/v1/2025.acl-long.73 | null | 2409.02718 | title_snapshot |
2025.acl-long.74 | Jailbreak Large Vision-Language Models Through Multi-Modal Linkage | https://aclanthology.org/2025.acl-long.74/ | [
"Yu Wang",
"Xiaofei Zhou",
"Yichen Wang",
"Geyuan Zhang",
"Tianxing He"
] | With the rapid advancement of Large Vision-Language Models (VLMs), concerns about their potential misuse and abuse have grown rapidly. Prior research has exposed VLMs’ vulnerability to jailbreak attacks, where carefully crafted inputs can lead the model to produce content that violates ethical and legal standards. How... | 2025.acl-long.74 | 10.18653/v1/2025.acl-long.74 | null | 2412.00473 | title_snapshot |
2025.acl-long.75 | Wait, that’s not an option: LLMs Robustness with Incorrect Multiple-Choice Options | https://aclanthology.org/2025.acl-long.75/ | [
"Gracjan Góral",
"Emilia Wiśnios",
"Piotr Sankowski",
"Paweł Budzianowski"
] | This work introduces a novel framework for evaluating LLMs’ capacity to balance instruction-following with critical reasoning when presented with multiple-choice questions containing no valid answers. Through systematic evaluation across arithmetic, domain-specific knowledge, and high-stakes medical decision tasks, we ... | 2025.acl-long.75 | 10.18653/v1/2025.acl-long.75 | null | 2409.00113 | title_snapshot |
2025.acl-long.76 | The Hidden Attention of Mamba Models | https://aclanthology.org/2025.acl-long.76/ | [
"Ameen Ali Ali",
"Itamar Zimerman",
"Lior Wolf"
] | The Mamba layer offers an efficient selective state-space model (SSM) that is highly effective in modeling multiple domains, includingNLP, long-range sequence processing, and computer vision. Selective SSMs are viewed as dual models, in which one trains in parallel on the entire sequence via an IO-aware parallel scan, ... | 2025.acl-long.76 | 10.18653/v1/2025.acl-long.76 | null | 2403.01590 | title_snapshot |
2025.acl-long.77 | KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding | https://aclanthology.org/2025.acl-long.77/ | [
"Shi Luohe",
"Zuchao Li",
"Lefei Zhang",
"Baoyuan Qi",
"Liu Guoming",
"Hai Zhao"
] | Large language models (LLMs) based on Transformer Decoders have become the preferred choice for conversational generative AI. Despite the overall superiority of the Decoder architecture, the gradually increasing Key-Value (KV) cache during inference has emerged as a primary efficiency bottleneck, both in aspects of mem... | 2025.acl-long.77 | 10.18653/v1/2025.acl-long.77 | null | 2507.11273 | title_snapshot |
2025.acl-long.78 | LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models | https://aclanthology.org/2025.acl-long.78/ | [
"Yan Wang",
"Ling Ding",
"Tien N Nguyen",
"Shaohua Wang",
"Yanan Zheng"
] | Large Language Models for code often entail significant computational complexity, which grows significantly with the length of the input code sequence. We propose LeanCode for code simplification to reduce training and prediction time, leveraging code contexts in utilizing attention scores to represent the tokens’ impo... | 2025.acl-long.78 | 10.18653/v1/2025.acl-long.78 | null | 2505.14759 | title_snapshot |
2025.acl-long.79 | MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset | https://aclanthology.org/2025.acl-long.79/ | [
"Weiqi Wang",
"Yangqiu Song"
] | To enable Large Language Models (LLMs) to function as conscious agents with generalizable reasoning capabilities, it is crucial that they possess the ability to ***comprehend situational changes (transitions) in distribution*** triggered by environmental factors or actions from other agents. Despite its fundamental sig... | 2025.acl-long.79 | 10.18653/v1/2025.acl-long.79 | null | 2406.02106 | title_snapshot |
2025.acl-long.80 | Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions | https://aclanthology.org/2025.acl-long.80/ | [
"Hang Li",
"Tianlong Xu",
"Kaiqi Yang",
"Yucheng Chu",
"Yanling Chen",
"Yichi Song",
"Qingsong Wen",
"Hui Liu"
] | The rise of large language models (LLMs) offers new opportunities for automatic error detection in education, particularly for math word problems (MWPs). While prior studies demonstrate the promise of LLMs as error detectors, they overlook the presence of multiple valid solutions for a single MWP. Our preliminary analy... | 2025.acl-long.80 | 10.18653/v1/2025.acl-long.80 | null | 2412.16838 | title_snapshot |
2025.acl-long.81 | Real-time Factuality Assessment from Adversarial Feedback | https://aclanthology.org/2025.acl-long.81/ | [
"Sanxing Chen",
"Yukun Huang",
"Bhuwan Dhingra"
] | We show that existing evaluations for assessing the factuality of news from conventional sources, such as claims on fact-checking websites, result in high accuracies over time for LLM-based detectors—even after their knowledge cutoffs. This suggests that recent popular false information from such sources can be easily ... | 2025.acl-long.81 | 10.18653/v1/2025.acl-long.81 | null | 2410.14651 | title_snapshot |
2025.acl-long.82 | Improve Vision Language Model Chain-of-thought Reasoning | https://aclanthology.org/2025.acl-long.82/ | [
"Ruohong Zhang",
"Bowen Zhang",
"Yanghao Li",
"Haotian Zhang",
"Zhiqing Sun",
"Zhe Gan",
"Yinfei Yang",
"Ruoming Pang",
"Yiming Yang"
] | Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes often relying on datasets dominated by short annotations with minimal rationales. In this work, we show that training VLM on short answers leads to poor gene... | 2025.acl-long.82 | 10.18653/v1/2025.acl-long.82 | null | 2410.16198 | title_snapshot |
2025.acl-long.83 | On the Mutual Influence of Gender and Occupation in LLM Representations | https://aclanthology.org/2025.acl-long.83/ | [
"Haozhe An",
"Connor Baumler",
"Abhilasha Sancheti",
"Rachel Rudinger"
] | We examine LLM representations of gender for first names in various occupational contexts to study how occupations and the gender perception of first names in LLMs influence each other mutually. We find that LLMs’ first-name gender representations correlate with real-world gender statistics associated with the name, an... | 2025.acl-long.83 | 10.18653/v1/2025.acl-long.83 | null | 2503.06792 | title_snapshot |
2025.acl-long.84 | Disentangling Memory and Reasoning Ability in Large Language Models | https://aclanthology.org/2025.acl-long.84/ | [
"Mingyu Jin",
"Weidi Luo",
"Sitao Cheng",
"Xinyi Wang",
"Wenyue Hua",
"Ruixiang Tang",
"William Yang Wang",
"Yongfeng Zhang"
] | Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks that require both extensive knowledge and reasoning abilities. However, the existing LLM inference pipeline operates as an opaque process without explicit separation between knowledge retrieval and reasoning steps, making the mo... | 2025.acl-long.84 | 10.18653/v1/2025.acl-long.84 | null | 2411.13504 | title_snapshot |
2025.acl-long.85 | Open-World Attribute Mining for E-Commerce Products with Multimodal Self-Correction Instruction Tuning | https://aclanthology.org/2025.acl-long.85/ | [
"Jiaqi Li",
"Yanming Li",
"Xiaoli Shen",
"Chuanyi Zhang",
"Guilin Qi",
"Sheng Bi"
] | In e-commerce, effective product Attribute Mining (AM) is essential for improving product features and aiding consumer decisions. However, current AM methods often focus on extracting attributes from unimodal text, underutilizing multimodal data. In this paper, we propose a novel framework called Multimodal Self-Correc... | 2025.acl-long.85 | 10.18653/v1/2025.acl-long.85 | null | null | null |
2025.acl-long.86 | Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attributions Explainability | https://aclanthology.org/2025.acl-long.86/ | [
"Joakim Edin",
"Andreas Geert Motzfeldt",
"Casper L. Christensen",
"Tuukka Ruotsalo",
"Lars Maaløe",
"Maria Maistro"
] | Deep neural network predictions are notoriously difficult to interpret. Feature attribution methods aim to explain these predictions by identifying the contribution of each input feature. Faithfulness, often evaluated using the area over the perturbation curve (AOPC), reflects feature attributions’ accuracy in describi... | 2025.acl-long.86 | 10.18653/v1/2025.acl-long.86 | null | 2408.08137 | title_judge |
2025.acl-long.87 | Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling | https://aclanthology.org/2025.acl-long.87/ | [
"Yang Yuguang",
"Yu Pan",
"Jixun Yao",
"Xiang Zhang",
"Jianhao Ye",
"Hongbin Zhou",
"Lei Xie",
"Lei Ma",
"Jianjun Zhao"
] | Expressive zero-shot voice conversion (VC) is a critical and challenging task that aims to transform the source timbre into an arbitrary unseen speaker while preserving the original content and expressive qualities. Despite recent progress in zero-shot VC, there remains considerable potential for improvements in speake... | 2025.acl-long.87 | 10.18653/v1/2025.acl-long.87 | null | 2410.01350 | title_snapshot |
2025.acl-long.88 | LangSAMP: Language-Script Aware Multilingual Pretraining | https://aclanthology.org/2025.acl-long.88/ | [
"Yihong Liu",
"Haotian Ye",
"Chunlan Ma",
"Mingyang Wang",
"Hinrich Schuetze"
] | Recent multilingual pretrained language models (mPLMs) often avoid using language embeddings – learnable vectors assigned to individual languages. However, this places a significant burden on token representations to encode all language-specific information, which may hinder language neutrality. To address this limitat... | 2025.acl-long.88 | 10.18653/v1/2025.acl-long.88 | null | 2409.18199 | title_snapshot |
2025.acl-long.89 | RelationalCoder: Rethinking Complex Tables via Programmatic Relational Transformation | https://aclanthology.org/2025.acl-long.89/ | [
"Haoyu Dong",
"Yue Hu",
"Huailiang Peng",
"Yanan Cao"
] | Semi-structured tables, with their varied layouts and formatting artifacts, remain a major obstacle for automated data processing and analytics. To address these challenges, we propose RelationalCoder, which uniformly converts semi-structured tables into relational data, enabling smooth integration with the rich ecosys... | 2025.acl-long.89 | 10.18653/v1/2025.acl-long.89 | null | null | null |
2025.acl-long.90 | Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study | https://aclanthology.org/2025.acl-long.90/ | [
"Bolei Ma",
"Berk Yoztyurk",
"Anna-Carolina Haensch",
"Xinpeng Wang",
"Markus Herklotz",
"Frauke Kreuter",
"Barbara Plank",
"Matthias Aßenmacher"
] | In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the ability to replicate the socio-cultural context and nuanced opinions of human participants. Using open-ended survey data from the German Longit... | 2025.acl-long.90 | 10.18653/v1/2025.acl-long.90 | null | 2412.13169 | title_snapshot |
2025.acl-long.91 | TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos | https://aclanthology.org/2025.acl-long.91/ | [
"Fanheng Kong",
"Jingyuan Zhang",
"Hongzhi Zhang",
"Shi Feng",
"Daling Wang",
"Linhao Yu",
"Xingguang Ji",
"Yu Tian",
"Victoria W.",
"Fuzheng Zhang"
] | Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing benchmarks for video understanding often treat these properties separately or narrowly focus on specific aspects, overlooking the holistic natu... | 2025.acl-long.91 | 10.18653/v1/2025.acl-long.91 | null | 2505.20124 | title_snapshot |
2025.acl-long.92 | Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs | https://aclanthology.org/2025.acl-long.92/ | [
"Zhuo Li",
"Yuhao Du",
"Jinpeng Hu",
"Xiang Wan",
"Anningzhe Gao"
] | Improving prompt quality is crucial for enhancing the performance of large language models (LLMs), particularly for Black-Box models like GPT4. Existing prompt refinement methods, while effective, often suffer from semantic inconsistencies between refined and original prompts, and fail to maintain users’ real intent. T... | 2025.acl-long.92 | 10.18653/v1/2025.acl-long.92 | null | 2409.01552 | title_snapshot |
2025.acl-long.93 | Binary Classifier Optimization for Large Language Model Alignment | https://aclanthology.org/2025.acl-long.93/ | [
"Seungjae Jung",
"Gunsoo Han",
"Daniel Wontae Nam",
"Kyoung-Woon On"
] | In real-world services such as ChatGPT, aligning models based on user feedback is crucial for improving model performance. However, due to the simplicity and convenience of providing feedback, users typically offer only basic binary signals, such as ‘thumbs-up’ or ‘thumbs-down’. Most existing alignment research, on the... | 2025.acl-long.93 | 10.18653/v1/2025.acl-long.93 | null | 2404.04656 | title_snapshot |
2025.acl-long.94 | UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization | https://aclanthology.org/2025.acl-long.94/ | [
"Md Nayem Uddin",
"Amir Saeidi",
"Divij Handa",
"Agastya Seth",
"Tran Cao Son",
"Eduardo Blanco",
"Steven Corman",
"Chitta Baral"
] | This paper introduces UnSeenTimeQA, a novel data contamination-free time-sensitive question-answering (TSQA) benchmark. It differs from existing TSQA benchmarks by avoiding web-searchable queries grounded in the real world. We present a series of time-sensitive event scenarios based on synthetically generated facts. It... | 2025.acl-long.94 | 10.18653/v1/2025.acl-long.94 | null | 2407.03525 | title_snapshot |
2025.acl-long.95 | From Information to Insight: Leveraging LLMs for Open Aspect-Based Educational Summarization | https://aclanthology.org/2025.acl-long.95/ | [
"Yang Zhong",
"Diane Litman"
] | This paper addresses the challenge of aspect-based summarization in education by introducing Reflective ASPect-based summarization (ReflectASP), a novel dataset that summarizes student reflections on STEM lectures. Despite the promising performance of large language models in general summarization, their application to... | 2025.acl-long.95 | 10.18653/v1/2025.acl-long.95 | null | null | null |
2025.acl-long.96 | AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset | https://aclanthology.org/2025.acl-long.96/ | [
"Charles Nimo",
"Tobi Olatunji",
"Abraham Toluwase Owodunni",
"Tassallah Abdullahi",
"Emmanuel Ayodele",
"Mardhiyah Sanni",
"Ezinwanne C. Aka",
"Folafunmi Omofoye",
"Foutse Yuehgoh",
"Timothy Faniran",
"Bonaventure F. P. Dossou",
"Moshood O. Yekini",
"Jonas Kemp",
"Katherine A Heller",
"... | Recent advancements in large language model (LLM) performance on medical multiplechoice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-andmiddle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potent... | 2025.acl-long.96 | 10.18653/v1/2025.acl-long.96 | Best Social Impact Paper | 2411.15640 | title_snapshot |
2025.acl-long.97 | Root Defense Strategies: Ensuring Safety of LLM at the Decoding Level | https://aclanthology.org/2025.acl-long.97/ | [
"Xinyi Zeng",
"Yuying Shang",
"Jiawei Chen",
"Jingyuan Zhang",
"Yu Tian"
] | Large language models (LLMs) have demonstrated immense utility across various industries. However, as LLMs advance, the risk of harmful outputs increases due to incorrect or malicious prompts. While current methods effectively address jailbreak risks, they share common limitations: 1) Judging harmful outputs from the p... | 2025.acl-long.97 | 10.18653/v1/2025.acl-long.97 | null | 2410.06809 | title_judge |
2025.acl-long.98 | In-the-wild Audio Spatialization with Flexible Text-guided Localization | https://aclanthology.org/2025.acl-long.98/ | [
"Tianrui Pan",
"Jie Liu",
"Zewen Huang",
"Jie Tang",
"Gangshan Wu"
] | Binaural audio enriches immersive experiences by enabling the perception of the spatial locations of sounding objects in AR, VR, and embodied AI applications. While existing audio spatialization methods can generally map any available monaural audio to binaural audio signals, they often lack the flexible and interactiv... | 2025.acl-long.98 | 10.18653/v1/2025.acl-long.98 | null | 2506.00927 | title_snapshot |
2025.acl-long.99 | L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models | https://aclanthology.org/2025.acl-long.99/ | [
"Hyesung Jeon",
"Yulhwa Kim",
"Jae-Joon Kim"
] | Due to the high memory and computational costs associated with large language models (LLMs), model compression techniques such as quantization, which reduces inference costs, and parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA), which reduce training costs, have gained significant populari... | 2025.acl-long.99 | 10.18653/v1/2025.acl-long.99 | null | 2402.04902 | title_snapshot |
2025.acl-long.100 | Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion | https://aclanthology.org/2025.acl-long.100/ | [
"Jianqing Zhu",
"Huang Huang",
"Zhihang Lin",
"Juhao Liang",
"Zhengyang Tang",
"Khalid Almubarak",
"Mosen Alharthi",
"Bang An",
"Juncai He",
"Xiangbo Wu",
"Fei Yu",
"Junying Chen",
"Ma Zhuoheng",
"Yuhao Du",
"He Zhang",
"Saied Alshahrani",
"Emad A. Alghamdi",
"Lian Zhang",
"Ruoyu... | This paper addresses the critical need for democratizing large language models (LLM) in the Arab world, a region that has seen slower progress in developing models comparable to state-of-the-art offerings like GPT-4 or GPT-3.5, due to a predominant focus on mainstream languages (e.g., English and Chinese). One practica... | 2025.acl-long.100 | 10.18653/v1/2025.acl-long.100 | null | 2412.12310 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.