用双阶段框架拆解复杂假话，提升事实核查精度
BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking
- 模拟专家查证流程，分两步化解模糊与冗余信息
- 在Hover和Feverous-s数据集上超越现有方法
- 适合处理含隐藏关系和多余信息的复杂声明
复杂声明的事实核查在虚假信息检测中至关重要。然而，现有方法难以应对声明模糊问题，尤其在处理隐含信息和复杂关系方面表现不足。此外，证据中的冗余信息会增加验证难度。为此，我们提出双边去芜验证（BiDeV）框架，通过多角色大模型协作，模拟人类专家的核查过程。该框架包含两个核心模块：模糊化解模块识别隐含信息并理清复杂关系以简化声明；冗余化解模块剔除无关内容以提升证据质量。在两个主流且具有挑战性的事实核查基准（Hover和Feverous-s）上的大量实验表明，BiDeV在黄金设置和开放设置下均达到最佳性能，验证了其在处理复杂声明与实现精准核查方面的有效性。
原文摘要 · Abstract (English)
Complex claim fact-checking performs a crucial role in disinformation detection. However, existing fact-checking methods struggle with claim vagueness, specifically in effectively handling latent information and complex relations within claims. Moreover, evidence redundancy, where nonessential information complicates the verification process, remains a significant issue. To tackle these limitations, we propose Bilateral Defusing Verification (BiDeV), a novel fact-checking working-flow framework integrating multiple role-played LLMs to mimic the human-expert fact-checking process. BiDeV consists of two main modules: Vagueness Defusing identifies latent information and resolves complex relations to simplify the claim, and Redundancy Defusing eliminates redundant content to enhance the evidence quality. Extensive experimental results on two widely used challenging fact-checking benchmarks (Hover and Feverous-s) demonstrate that our BiDeV can achieve the best performance under both gold and open settings. This highlights the effectiveness of BiDeV in handling complex claims and ensuring precise fact-checking
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