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Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models

Created by
  • Haebom

Author

Chen Han, Wenzhen Zheng, Xijin Tang

Outline

This paper presents Debate-to-Detect (D2D), a novel misinformation detection framework that overcomes the limitations of existing static classification methods to address the proliferation of misinformation on digital platforms. D2D is based on multi-agent debate (MAD) and reframes misinformation detection as a structured adversarial debate. Each agent is assigned a domain-specific profile and undergoes a five-stage debate process: opening remarks, rebuttals, open discussion, closing remarks, and judgment. Beyond simple binary classification, it introduces a multidimensional evaluation mechanism that evaluates arguments across five dimensions: factuality, source credibility, reasoning quality, clarity, and ethical considerations. Experimental results using GPT-4o on two datasets demonstrate significant performance improvements over existing methods. Case studies highlight D2D's ability to iteratively refine evidence and enhance decision transparency.

Takeaways, Limitations

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Takeaways:
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We present a novel multi-agent discussion-based framework that overcomes the limitations of existing static fake information detection methods.
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A more sophisticated and comprehensive fake information detection capability is possible through a multidimensional evaluation mechanism.
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Improving decision-making transparency through an evidence-based, iterative verification process.
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Improving fake information detection performance by leveraging the inference capabilities of large-scale language models (LLMs).
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Limitations:
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Currently, only experimental results using GPT-4o are presented, so generalizability to other LLMs requires further verification.
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Accessibility restrictions exist as the code release is scheduled for after the official announcement.
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Robust verification of various types of fake information and platform environments is required.
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Further research is needed on the objectivity and reliability of the five-dimensional evaluation criteria.
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