SUCEA: Reasoning-Intensive Retrieval for Adversarial Fact-checking through Claim Decomposition and Editing

• URL: http://arxiv.org/abs/2506.04583v1
• Date: Thu, 05 Jun 2025 02:58:15 GMT
• Title: SUCEA: Reasoning-Intensive Retrieval for Adversarial Fact-checking through Claim Decomposition and Editing
• Authors: Hongjun Liu, Yilun Zhao, Arman Cohan, Chen Zhao,
• Abstract summary: adversarial claims are intentionally designed by humans to challenge fact-checking systems.<n>We propose a training-free method designed to rephrase the original claim, making it easier to locate supporting evidence.<n>Our framework significantly improves on both retrieval and entailment label accuracy, outperforming four strong claim-decomposition-based baselines.
• Score: 30.84752573088322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic fact-checking has recently received more attention as a means of combating misinformation. Despite significant advancements, fact-checking systems based on retrieval-augmented language models still struggle to tackle adversarial claims, which are intentionally designed by humans to challenge fact-checking systems. To address these challenges, we propose a training-free method designed to rephrase the original claim, making it easier to locate supporting evidence. Our modular framework, SUCEA, decomposes the task into three steps: 1) Claim Segmentation and Decontextualization that segments adversarial claims into independent sub-claims; 2) Iterative Evidence Retrieval and Claim Editing that iteratively retrieves evidence and edits the subclaim based on the retrieved evidence; 3) Evidence Aggregation and Label Prediction that aggregates all retrieved evidence and predicts the entailment label. Experiments on two challenging fact-checking datasets demonstrate that our framework significantly improves on both retrieval and entailment label accuracy, outperforming four strong claim-decomposition-based baselines.

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