Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore the semantic interaction between the claim and evidence at different granularity levels but fail to capture their topical consistency during the reasoning process, which we believe is crucial for verification; (ii) aggregate multiple pieces of evidence equally without considering their implicit stances to the claim, thereby introducing spurious information. To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim. Extensive experiments conducted on the two benchmark datasets demonstrate the superiority of the proposed model over several state-of-the-art approaches for fact verification. The source code can be obtained from https://github.com/jasenchn/TARSA.
StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件学院）；Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））；City University of Macau（澳门城市大学）
Collaborative Stance Detection via Small-Large Language Model Consistency Verification
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；People Public Security University of China（中国人民公安大学）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）
Tsinghua University（清华大学）；Tongji University（同济大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；University of Luxembourg（卢森堡大学）；University of Washington（华盛顿大学）；Cornell University（康奈尔大学）；Beihang University（北京航空航天大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Department of Computer Science, Tongji University（同济大学计算机科学系）；College of Computer Science and Technology, Taiyuan University of Technology（太原理工大学计算机科学与技术学院）
Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents
Department of Interactive Media, Hong Kong Baptist University（香港浸会大学互动媒体系）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）
CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China（上海交通大学计算机科学学院）；Shanghai KEY Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China（上海信息安全集成管理技术重点实验室）；School of Politics, National Defense University, Shanghai, China（国防大学政治学院）
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
Renmin University of China（中国人民大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
事实验证最近吸引了大量的研究关注，例如，在新闻业、市场营销和政策制定方面，网上的错误信息和虚假信息可以动摇一个人的观点，影响一个人的行动。虽然事实核查通常是一项艰巨的任务，但在许多情况下，虚假陈述可以很容易地根据对具有可靠信息的表格的分析来揭穿。因此，基于表格的事实验证最近已经成为一个重要的和不断增长的研究领域。然而，由于缺乏可用于预训练语言模型（LM）以了解常见表操作（例如聚合列或比较元组）的数据集，因此进展有限。为了弥补这一差距，在本文中，我们介绍了PASTA，一个新的国家的最先进的框架，基于表格的事实验证通过预训练与合成的表格完形填空题。特别是，我们设计了六种类型的常见的表完形填空任务，包括过滤，聚合，最高级，比较，顺序，和唯一的，基于此，我们合成了一个大型语料库，由120万个表对从WikiTables。PASTA使用最近预训练的LM DeBERTaV 3，并在我们的语料库上进一步预训练它。我们的实验结果表明，PASTA实现了新的国家的最先进的性能基于表的事实验证基准：TabFact和SEM-TAB-FACTS。特别是，在包含多个操作的TabFact复杂集上，PASTA的表现大大优于先前的最新技术水平4.7分（85.6% vs. 80.9%），并且PASTA和人类在小型TabFact测试集上的表现之间的差距缩小到仅1.5分（90.6% vs. 92.1%）。
FARCA: Fact-Aligned Reliability-Aware Credit Assignment for Reinforcement Learning with Factual Supervision
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
Seoul National University（首尔大学）；Samaya AI；Allen Institute for AI（艾伦人工智能研究所）；Yonsei University（延世大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；New York University（纽约大学）
TAGS: A Test-Time Generalist-Specialist Framework with Retrieval-Augmented Reasoning and Verification
Mohamed bin Zayed University of Artificial Intelligence（莫莫德·本·扎耶德人工智能大学）；Monash University（墨尔本大学）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）；University of Southampton（南安普顿大学）
