.png){kind=icon}
.png){kind=icon}
Return
Debating to Verify: A Robust and Explainable Multi-Agent LLM System for Fact-Checking
.png){kind=icon}
OA
AI
T
B
T
DOI：10.1016/j.icte.2026.05.017.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Large language models have advanced automated fact verification, yet single-agent prompting remains prone to hallucination, while fine-tuned models suffer from limited scalability and cross-domain generalization. This paper proposes FC-MAD, a training-free multi-agent debate framework that coordinates multiple LLMs through structured critique, context summarization, and judge-guided consensus reasoning. Extensive experiments on Vietnamese (ViFactCheck), multilingual (X-Fact), and English (FEVER) benchmarks show that FC-MAD consistently outperforms strong fine-tuned and prompting-based baselines, achieving state-of-the-art performance on ViFactCheck and FEVER, while delivering robust gains across X-Fact languages. These results highlight the effectiveness of structured multi-agent reasoning for reliable AI-based fact-checking systems.
Keywords:
automated fact verification
multi-agent systems
consensus reasoning
hallucination mitigation
large language models
debate-based framework
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
960
Citations:
2.5K
Organization
Cited Papers