How can LLMs improve multi-agent consensus?
Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models
November 26, 2024
https://arxiv.org/pdf/2411.16189This paper explores improving the accuracy and reducing "hallucinations" in multi-agent LLM systems by introducing a third-party LLM to act as a sort of "expert reviewer." This third-party LLM helps adjust the attention weights of the primary agents, allowing the system to better integrate diverse perspectives and reach a more informed consensus. Key points include using uncertainty estimation and confidence analysis to guide attention weight adjustments, demonstrating improved performance on arithmetic problem-solving compared to traditional multi-agent baselines, and suggesting potential for more robust and accurate multi-agent systems in complex tasks.