Claim verification plays a crucial role in combating misinformation. While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale. Additionally, it is important for models to provide comprehensive explanations that can justify their decisions and assist human fact-checkers. This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK) Reasoning that can verify complex claims and generate explanations without the need for annotated evidence using Large Language Models (LLMs). FOLK leverages the in-context learning ability of LLMs to translate the claim into a First-Order-Logic (FOL) clause consisting of predicates, each corresponding to a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning over a set of knowledge-grounded question-and-answer pairs to make veracity predictions and generate explanations to justify its decision-making process. This process makes our model highly explanatory, providing clear explanations of its reasoning process in human-readable form. Our experiment results indicate that FOLK outperforms strong baselines on three datasets encompassing various claim verification challenges. Our code and data are available.

翻译：声明验证在打击虚假信息中扮演着关键角色。尽管现有声明验证研究已取得可喜成果，但一个尚未解决的核心难题是：如何在无需依赖人工标注数据（大规模创建成本高昂）的情况下验证声明。此外，模型需要能够提供全面解释以佐证其决策过程并协助人工事实核查员。本文提出一阶逻辑引导的知识基础推理方法（FOLK），该方法可利用大型语言模型（LLM）验证复杂声明并生成解释，无需标注证据。FOLK利用LLM的上下文学习能力，将声明转化为由多个谓词构成的一阶逻辑（FOL）子句，每个谓词对应一个需要验证的子声明。随后，FOLK通过对一组知识基础问答对进行FOL引导推理，做出真实性预测并生成解释以阐述其决策过程。该流程使模型具有高度可解释性，能以人类可读形式清晰展现推理过程。实验结果表明，FOLK在涵盖多种声明验证挑战的三个数据集上均优于强基线模型。我们的代码与数据已公开。