证据链增强的多智能体分组讨论事实核查框架研究OA

Fact-checking serves as a critical approach for identifying and mitigating misinformation.However,individual large language models(LLMs)suffer from inherent limitations in fact-checking tasks,including hallucination issues,insufficient interpretability,and limited accuracy.This study proposes an Evidence Chain Enhanced Multi-Agent Group Discussion Fact-Checking Framework to improve the accuracy,robustness,and explainability of fact-checking systems.The proposed framework consists of a moderator agent responsible for task orchestration,result aggregation,and consensus adjudication,along with three specialized fact-checking agent groups focusing on scientific evidence,media evidence,and factual evidence,respectively.Within each group,agents follow a standardized fact-checking workflow involving evidence retrieval,evidence chain construction,truthfulness assessment,and report generation,thereby establishing a closed-loop mechanism of"Coordination-Specialization-Integration"Experimental results on benchmark fact-checking datasets demonstrate that the proposed framework achieves competitive performance in both veracity classification accuracy and fact-checking report generation quality,substantially enhancing the credibility of fact-checking results.