BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking
This work addresses the challenge of disinformation detection for fact-checking systems, though it appears incremental as it builds on existing methods with a novel workflow.
The paper tackles the problem of complex claim fact-checking by addressing claim vagueness and evidence redundancy, proposing the BiDeV framework which achieves the best performance on benchmarks like Hover and Feverous-s under gold and open settings.
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