用多智能体系统提升事实核查准确率与透明度
Towards Robust Fact-Checking: A Multi-Agent System with Advanced Evidence Retrieval
- 四智能体分工协作，分解复杂陈述并生成精准查询
- 在三个数据集上比基线高12.3%的宏平均F1分数
- 适合需要可解释性与高可靠性的自动化核查场景
数字时代虚假信息快速传播，对公共讨论构成严峻挑战，亟需稳健且可扩展的事实核查方案。传统人工核查虽可信，但难以应对网络内容的数量与速度，推动了基于大语言模型（LLMs）的自动化系统发展。然而现有方法常受限于复杂陈述处理、来源可信度保障及过程透明性。本文提出一种新型多智能体系统，通过四个专用智能体实现：输入接收智能体负责陈述分解，查询生成智能体制定针对性子问题，证据检索智能体从可信来源获取证据，判决预测智能体综合判断真伪并生成人类可读解释。在FEVEROUS、HOVER和SciFact等基准数据集上，该系统相较基线方法在宏平均F1分数上提升12.3%。系统能有效分解复杂陈述，从可信源获取可靠证据，并生成验证决策的透明解释。本方法为自动化事实核查提供了更准确、高效且透明的验证范式，契合人工核查实践，同时具备现实应用的可扩展性。代码已开源：https://github.com/HySonLab/FactAgent。
原文摘要 · Abstract (English)
The rapid spread of misinformation in the digital era poses significant challenges to public discourse, necessitating robust and scalable fact-checking solutions. Traditional human-led fact-checking methods, while credible, struggle with the volume and velocity of online content, prompting the integration of automated systems powered by Large Language Models (LLMs). However, existing automated approaches often face limitations, such as handling complex claims, ensuring source credibility, and maintaining transparency. This paper proposes a novel multi-agent system for automated fact-checking that enhances accuracy, efficiency, and explainability. The system comprises four specialized agents: an Input Ingestion Agent for claim decomposition, a Query Generation Agent for formulating targeted subqueries, an Evidence Retrieval Agent for sourcing credible evidence, and a Verdict Prediction Agent for synthesizing veracity judgments with human-interpretable explanations. Evaluated on benchmark datasets (FEVEROUS, HOVER, SciFact), the proposed system achieves a 12.3% improvement in Macro F1-score over baseline methods. The system effectively decomposes complex claims, retrieves reliable evidence from trusted sources, and generates transparent explanations for verification decisions. Our approach contributes to the growing field of automated fact-checking by providing a more accurate, efficient, and transparent verification methodology that aligns with human fact-checking practices while maintaining scalability for real-world applications. Our source code is available at https://github.com/HySonLab/FactAgent
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