Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks

Understand

Large language models (LLMs) excel in natural language generation but often confidently produce incorrect responses, especially in tasks like mathematical reasoning.

• Chain-of-thought prompting, self-verification, and multi-agent debate are among the strategies proposed to improve the reasoning and factual accuracy of LLMs.
• Building on Du et al.'s multi-agent debate framework, we find that multi-agent debate helps at any model scale, and that diversity of thought elicits stronger reasoning in debating LLMs.
• Across various model sizes, performance on mathematical reasoning tasks benefits most when diverse trained models are used.