Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking

• URL: http://arxiv.org/abs/2411.05375v1
• Date: Fri, 08 Nov 2024 07:05:06 GMT
• Title: Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking
• Authors: Mubashara Akhtar, Michael Schlichtkrull, Andreas Vlachos,
• Abstract summary: Ev2R is an evaluation framework for automated fact-checking (AFC) It comprises three types of approaches for evidence evaluation: reference-based, proxy-reference, and reference-less. We evaluate their effectiveness through agreement with human ratings and adversarial tests.
• Score: 11.300523252168327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current automated fact-checking (AFC) approaches commonly evaluate evidence either implicitly via the predicted verdicts or by comparing retrieved evidence with a predefined closed knowledge source, such as Wikipedia. However, these methods suffer from limitations, resulting from their reliance on evaluation metrics developed for different purposes and constraints imposed by closed knowledge sources. Recent advances in natural language generation (NLG) evaluation offer new possibilities for evidence assessment. In this work, we introduce Ev2R, an evaluation framework for AFC that comprises three types of approaches for evidence evaluation: reference-based, proxy-reference, and reference-less. We evaluate their effectiveness through agreement with human ratings and adversarial tests, and demonstrate that prompt-based scorers, particularly those leveraging LLMs and reference evidence, outperform traditional evaluation approaches.

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