Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs

TL;DR

This paper evaluates retrieval-augmented language models for fact-checking when sources conflict, introduces a new dataset, and proposes methods to improve model reliability by considering source credibility.

Contribution

It provides the first systematic analysis of RAG models under conflicting evidence conditions and introduces the CONFACT dataset for this purpose.

Findings

State-of-the-art RAG models struggle with conflicting evidence.

Incorporating source credibility improves fact-checking accuracy.

The proposed strategies mitigate vulnerabilities in conflict resolution.

Abstract

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.