This paper introduces a multi-agent debate framework designed to enhance the factuality and reasoning capabilities of large language models (LLMs). The core idea involves multiple instances of an LLM proposing and critiquing solutions in iterative rounds until a consensus is reached. The authors demonstrate that this "society of minds" approach significantly improves performance across various tasks, including mathematical reasoning, strategic game play, and generating factually accurate biographies, by reducing errors and hallucinations often seen in single-model outputs. This method is directly applicable to existing black-box LLMs and can even lead to correct answers when individual models initially err, suggesting a powerful avenue for LLM self-improvement.

Source: https://arxiv.org/pdf/2305.14325