我们如何反驳主张：通过缺陷识别与解释实现自动事实核查
How We Refute Claims: Automatic Fact-Checking through Flaw Identification and Explanation
AI总结：
针对现有自动事实核查在应对复杂现实谣言上的不足，该研究提出面向缺陷的事实核查新任务，推出专用框架RefuteClaim，构建FlawCheck数据集，实验验证了其在虚假主张分类与解释上的有效性。
摘要：
自动事实核查是互联网内容治理中的一项关键任务。尽管已有诸多研究采用先进模型来解决这一问题，但在应对复杂的现实世界谣言与欺骗性主张方面仍存在显著差距。为应对这一挑战，本文探索了面向缺陷的事实核查这一全新任务，涵盖方面生成与缺陷识别两个环节。我们还推出了专门为该任务设计的新框架RefuteClaim。鉴于目前缺乏相关数据集，我们构建了FlawCheck数据集，该数据集通过从专家评审中提取并转化见解，生成对应的核查方面与识别出的缺陷。实验结果凸显了RefuteClaim的有效性，尤其在对虚假主张进行分类与阐释方面表现突出。
英文摘要：
Automated fact-checking is a crucial task in the governance of internet content. Although various studies utilize advanced models to tackle this issue, a significant gap persists in addressing complex real-world rumors and deceptive claims. To address this challenge, this paper explores the novel task of flaw-oriented fact-checking, including aspect generation and flaw identification. We also introduce RefuteClaim, a new framework designed specifically for this task. Given the absence of an existing dataset, we present FlawCheck, a dataset created by extracting and transforming insights from expert reviews into relevant aspects and identified flaws. The experimental results underscore the efficacy of RefuteClaim, particularly in classifying and elucidating false claims.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
