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

TL;DR

This paper presents Debate-to-Detect, a multi-agent debate framework utilizing large language models to improve misinformation detection through structured adversarial discussions and multi-dimensional evaluation, enhancing interpretability and accuracy.

Contribution

Introduces a novel debate-based framework with multi-dimensional assessment for misinformation detection, inspired by fact-checking workflows, leveraging GPT-4o for improved performance.

Findings

Significant performance improvements over baseline methods.

Enhanced interpretability through iterative evidence refinement.

Effective multi-dimensional evaluation across five claim aspects.

Abstract

The proliferation of misinformation in digital platforms reveals the limitations of traditional detection methods, which mostly rely on static classification and fail to capture the intricate process of real-world fact-checking. Despite advancements in Large Language Models (LLMs) that enhance automated reasoning, their application to misinformation detection remains hindered by issues of logical inconsistency and superficial verification. In response, we introduce Debate-to-Detect (D2D), a novel Multi-Agent Debate (MAD) framework that reformulates misinformation detection as a structured adversarial debate. Inspired by fact-checking workflows, D2D assigns domain-specific profiles to each agent and orchestrates a five-stage debate process, including Opening Statement, Rebuttal, Free Debate, Closing Statement, and Judgment. To transcend traditional binary classification, D2D introduces a multi-dimensional evaluation mechanism that assesses each claim across five distinct dimensions: Factuality, Source Reliability, Reasoning Quality, Clarity, and Ethics. Experiments with GPT-4o on two datasets demonstrate significant improvements over baseline methods, and the case study highlight D2D's capability to iteratively refine evidence while improving decision transparency, representing a substantial advancement towards interpretable misinformation detection. The code will be released publicly after the official publication.