基于大语言模型知识驱动推理的可解释主张验证
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models
AI总结：
本文提出一阶逻辑引导的知识驱动推理（FOLK），利用大语言模型将主张转化为FOL子句并在问答对上推理，无需标注证据即可验证复杂主张并生成可解释说明，在三个数据集上优于强基线。
摘要：
主张验证在对抗虚假信息中发挥着关键作用。尽管现有工作已展现出可喜的成果，但尚未解决的难题是如何在不依赖人工标注数据的情况下验证主张，因为大规模创建此类数据成本高昂。此外，模型提供全面解释以证明其决策并协助人工事实核查员也十分重要。本文提出了一阶逻辑引导的知识驱动（FOLK）推理，可使用大语言模型（LLMs）验证复杂主张并生成解释，且无需标注证据。FOLK利用LLMs的上下文学习能力，将主张转化为由谓词组成的一阶逻辑（FOL）子句，每个谓词对应一个需验证的子主张。随后，FOLK在一组知识驱动的问答对上执行FOL引导的推理，以做出真实性预测并生成解释来证明其决策过程。该过程使模型具有高度可解释性，以人类可读的形式清晰呈现推理过程。实验结果表明，FOLK在涵盖各种主张验证挑战的三个数据集上均优于强基线。代码和数据已公开。
英文摘要：
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.
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。
