EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；ByteDance Inc.（字节跳动公司）
Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu, Liang Wang
Fact verification aims to automatically probe the veracity of a claim based on several pieces of evidence. Existing works are always engaging in accuracy improvement, let alone explainability, a critical capability of fact verification systems. Constructing an explainable fact verification system in a complex multi-hop scenario is consistently impeded by the absence of a relevant, high-quality dataset. Previous datasets either suffer from excessive simplification or fail to incorporate essential considerations for explainability. To address this, we present EXFEVER, a pioneering dataset for multi-hop explainable fact verification. With over 60,000 claims involving 2-hop and 3-hop reasoning, each is created by summarizing and modifying information from hyperlinked Wikipedia documents. Each instance is accompanied by a veracity label and an explanation that outlines the reasoning path supporting the veracity classification. Additionally, we demonstrate a novel baseline system on our EX-FEVER dataset, showcasing document retrieval, explanation generation, and claim verification, and validate the significance of our dataset. Furthermore, we highlight the potential of utilizing Large Language Models in the fact verification task. We hope our dataset could make a significant contribution by providing ample opportunities to explore the integration of natural language explanations in the domain of fact verification.
HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering
University of Macau（澳门大学）；Shenzhen Kaihong Digital Industry Development Co., Ltd.（深圳开鸿数字产业发展有限公司）；University of New South Wales（新南威尔士大学）；Zhejiang University（浙江大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
School of Computer Science and Technology, Xinjiang University, Urumqi, China（新疆大学计算机科学与技术学院，乌鲁木齐，中国）；Beijing Institute of Technology, Beijing, China（北京理工大学，北京，中国）；Military Science Information Research Center, Academy of Military Science, Beijing, China（军事科学院军事科学信息研究中心，北京，中国）
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations
Tsinghua University（清华大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；KIRC, Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院知识图谱与推理中心（KIRC））；Beijing National Research Center for Information Science and Technology（北京国家信息科学与技术研究中心）
METER: Multi-modal Evidence-based Thinking and Explainable Reasoning -- Algorithm and Benchmark
Institute of Artificial Intelligence (TeleAI), China Telecom（电信人工智能研究院）；Institute of Artificial Intelligence and Robotics(IAIR), Xi’an Jiaotong University（人工智能与机器人研究院）；Advanced Technique of Artificial Intelligence(ATAI), Chongqing University of Technology（人工智能先进技术研究院）
TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery
Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Tsukuba（筑波大学）；Central China Normal University（华中师范大学）
SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 Gallagher人工智能学院）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
