用图结构分解事实核查，精准处理指代歧义
A Graph-based Verification Framework for Fact-Checking
- 构建包含三元组的声明图，实现细粒度事实拆解
- 在三个数据集上达到当前最优性能，显著减少指代模糊
- 适合需要高精度事实验证的研究者和内容审核人员
事实核查在对抗虚假信息中至关重要。现有基于大语言模型（LLMs）的事实分解方法存在两大局限：（1）分解不足，引入不必要的验证复杂性；（2）指代模糊，导致错误验证结果。为解决这些问题，我们提出使用由三元组构成的声明图，以缓解分解不足问题，并通过图结构降低指代歧义。基于此，我们设计了图基核查框架GraphFC，包含三个核心组件：图构建（构建声明图与证据图）、图引导规划（确定三元组验证顺序）和图引导核查（逐个验证声明图与证据图中的三元组）。大量实验表明，GraphFC在保持细粒度分解的同时，通过关系约束解决了指代模糊问题，在三个数据集上均取得当前最优表现。
原文摘要 · Abstract (English)
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.
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