Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs
It addresses reliability issues in automated fact-checking for users dealing with misinformation, but is incremental as it builds on existing RAG methods.
This paper tackles the problem of fact-checking with conflicting evidence by evaluating Retrieval-Augmented Generation (RAG) models, revealing vulnerabilities in handling source credibility differences, and shows that integrating source credibility information improves performance.
Large Language Models (LLMs) augmented with retrieval mechanisms have demonstrated significant potential in fact-checking tasks by integrating external knowledge. However, their reliability decreases when confronted with conflicting evidence from sources of varying credibility. This paper presents the first systematic evaluation of Retrieval-Augmented Generation (RAG) models for fact-checking in the presence of conflicting evidence. To support this study, we introduce \textbf{CONFACT} (\textbf{Con}flicting Evidence for \textbf{Fact}-Checking) (Dataset available at https://github.com/zoeyyes/CONFACT), a novel dataset comprising questions paired with conflicting information from various sources. Extensive experiments reveal critical vulnerabilities in state-of-the-art RAG methods, particularly in resolving conflicts stemming from differences in media source credibility. To address these challenges, we investigate strategies to integrate media background information into both the retrieval and generation stages. Our results show that effectively incorporating source credibility significantly enhances the ability of RAG models to resolve conflicting evidence and improve fact-checking performance.