一种基于图的事实核查验证框架
A Graph-based Verification Framework for Fact-Checking
AI总结：
本文提出基于声明三元组图的GraphFC框架，通过图构建、图引导规划和图引导核查解决声明分解不充分与提及歧义问题，在三个数据集上取得最先进的事实核查性能。
摘要：
事实核查在对抗错误信息中起着至关重要的作用。现有使用大语言模型（LLMs）进行声明分解的方法存在两个关键局限：（1）分解不充分，给验证过程带来不必要的复杂性；（2）提及存在歧义，导致验证结果不正确。为应对这些挑战，我们建议引入由三元组构成的声明图，以解决分解不充分的问题，并通过图结构减少提及歧义。基于这一核心思想，我们提出了一个基于图的框架GraphFC，用于事实核查。该框架包含三个关键组件：图构建，同时构建声明图和证据图；图引导规划，确定三元组验证顺序的优先级；以及图引导核查，在声明图和证据图之间逐一验证三元组。大量实验表明，GraphFC能够实现细粒度分解，同时通过关系约束解决指称歧义，在三个数据集上取得了最先进的性能。
英文摘要：
Fact-checking plays a crucial role in combating misinformation. Existing methods using large language models (LLMs) for claim decomposition face two key limitations: (1) insufficient decomposition, introducing unnecessary complexity to the verification process, and (2) ambiguity of mentions, leading to incorrect verification results. To address these challenges, we suggest introducing a claim graph consisting of triplets to address the insufficient decomposition problem and reduce mention ambiguity through graph structure. Based on this core idea, we propose a graph-based framework, GraphFC, for fact-checking. The framework features three key components: graph construction, which builds both claim and evidence graphs; graph-guided planning, which prioritizes the triplet verification order; and graph-guided checking, which verifies the triples one by one between claim and evidence graphs. Extensive experiments show that GraphFC enables fine-grained decomposition while resolving referential ambiguities through relational constraints, achieving state-of-the-art performance across three datasets.
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。
