EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, and Qiang Liu, Shu Wu, Liang Wang

TL;DR
EX-FEVER introduces a large, high-quality dataset for multi-hop explainable fact verification, enabling research into explainability, reasoning paths, and the use of large language models in verifying claims.
Contribution
The paper presents EX-FEVER, a novel dataset with over 60,000 claims and explanations for multi-hop reasoning, addressing the lack of explainability in existing fact verification datasets.
Findings
A baseline system demonstrates effective document retrieval, explanation generation, and claim verification.
The dataset facilitates exploration of natural language explanations in fact verification.
Large Language Models show potential in improving fact verification tasks.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Biomedical Text Mining and Ontologies
