The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News
Liu, Yuhan Liu, Yuxuan Zhang, Xiaoqing Chen, Xiuying Yan, Rui
Liu, Yuhan
Liu, Yuxuan
Zhang, Xiaoqing
Chen, Xiuying
Yan, Rui
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
Organizational Units
Journal Issue
Abstract
In today’s digital environment, the rapid propagation of fake news via social networks poses significant social challenges. Most existing detection methods either employ traditional classification models, which suffer from low interpretability and limited generalization capabilities, or craft specific prompts for large language models (LLMs) to produce explanations and results directly, failing to leverage LLMs’ reasoning abilities fully. Inspired by the saying that “truth becomes clearer through debate,” our study introduces a novel multi-agent system with LLMs named TruEDebate (TED) to enhance the interpretability and effectiveness of fake news detection. TED employs a rigorous debate process inspired by formal debate settings. Central to our approach are two innovative components: the DebateFlow Agents and the InsightFlow Agents. The DebateFlow Agents organize agents into two teams, where one supports and the other challenges the truth of the news. These agents engage in opening statements, cross-examination, rebuttal, and closing statements, simulating a rigorous debate process akin to human discourse analysis, allowing for a thorough evaluation of news content. Concurrently, the InsightFlow Agents consist of two specialized sub-agents: the Synthesis Agent and the Analysis Agent. The Synthesis Agent summarizes the debates and provides an overarching viewpoint, ensuring a coherent and comprehensive evaluation. The Analysis Agent, which includes a role-aware encoder and a debate graph, integrates role embeddings and models the interactions between debate roles and arguments using an attention mechanism, providing the final judgment. Our extensive experiments on two datasets, ARG-EN and ARG-CN, demonstrate that the TED framework surpasses traditional methods across various metrics and, more importantly, enhances interpretable fake news detection by illuminating logical reasoning and structured debate processes leading to accurate conclusions. We release our code to support Information systems that use structured debate within responsible information systems for improved decision-making.
Citation
Y. Liu, Y. Liu, X. Zhang, X. Chen, and R. Yan, “The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News,” SIGIR 2025 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 504–514, Jul. 2025, doi: 10.1145/3726302.3730092
Source
SIGIR 2025 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
Conference
48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025
Keywords
Debate, Fake News, Large Language Models, Multi-Agent System
Subjects
Source
48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025
Publisher
Association for Computing Machinery