LLM-Consensus: Multi-Agent Debate for Visual Misinformation Detection

• URL: http://arxiv.org/abs/2410.20140v2
• Date: Fri, 31 Jan 2025 20:55:12 GMT
• Title: LLM-Consensus: Multi-Agent Debate for Visual Misinformation Detection
• Authors: Kumud Lakara, Georgia Channing, Juil Sock, Christian Rupprecht, Philip Torr, John Collomosse, Christian Schroeder de Witt,
• Abstract summary: LLM-Consensus is a novel multi-agent debate system for misinformation detection.<n>Our framework enables explainable detection with state-of-the-art accuracy.
• Score: 26.84072878231029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the most challenging forms of misinformation involves the out-of-context (OOC) use of images paired with misleading text, creating false narratives. Existing AI-driven detection systems lack explainability and require expensive finetuning. We address these issues with LLM-Consensus, a multi-agent debate system for OOC misinformation detection. LLM-Consensus introduces a novel multi-agent debate framework where multimodal agents collaborate to assess contextual consistency and request external information to enhance cross-context reasoning and decision-making. Our framework enables explainable detection with state-of-the-art accuracy even without domain-specific fine-tuning. Extensive ablation studies confirm that external retrieval significantly improves detection accuracy, and user studies demonstrate that LLM-Consensus boosts performance for both experts and non-experts. These results position LLM-Consensus as a powerful tool for autonomous and citizen intelligence applications.

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