BiDeV：面向复杂声明事实核查的双边消解验证
BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking
AI总结：
BiDeV提出一种集成多角色LLM的双边消解验证框架，通过模糊消解与冗余消解处理复杂声明，在Hover和Feverous-s基准上取得最优性能。
摘要：
复杂声明的事实核查在虚假信息检测中扮演着关键角色。然而，现有的事实核查方法在处理声明模糊性方面存在困难，特别是在有效处理声明中的潜在信息和复杂关系方面。此外，证据冗余——即非必要信息使验证过程复杂化——仍然是一个重要问题。为解决这些局限，我们提出了双边消解验证（BiDeV），一种新颖的事实核查工作流框架，集成了多个角色扮演的大型语言模型（LLM）以模拟人类专家的事实核查过程。BiDeV包含两个主要模块：模糊消解（Vagueness Defusing）识别潜在信息并解决复杂关系以简化声明，以及冗余消解（Redundancy Defusing）消除冗余内容以提升证据质量。在两个广泛使用的具有挑战性的事实核查基准（Hover和Feverous-s）上的大量实验结果表明，我们的BiDeV在黄金设置和开放设置下均能达到最佳性能。这凸显了BiDeV在处理复杂声明和确保精确事实核查方面的有效性。
英文摘要：
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
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。
