From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

October 18, 2023 Β· Declared Dead Β· πŸ› Conference on Empirical Methods in Natural Language Processing

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Authors Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng arXiv ID 2310.11675 Category cs.IR: Information Retrieval Citations 18 Venue Conference on Empirical Methods in Natural Language Processing Last Checked 4 months ago
Abstract
Retrieval-enhanced methods have become a primary approach in fact verification (FV); it requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a claim. To retrieve evidence, existing work often employs off-the-shelf retrieval models whose design is based on the probability ranking principle. We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence. We introduce the feedback-based evidence retriever(FER) that optimizes the evidence retrieval process by incorporating feedback from the claim verifier. As a feedback signal we use the divergence in utility between how effectively the verifier utilizes the retrieved evidence and the ground-truth evidence to produce the final claim label. Empirical studies demonstrate the superiority of FER over prevailing baselines.
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