Multi-Agent Collaboration for Improved Reasoning in Large Language Models

The article explores the potential of multi-agent collaboration in improving the reasoning abilities of large language models (LLMs). The authors propose a new method called "Self-Refine" that enables LLMs to iteratively refine their responses through a process of mutual review and correction. This approach is inspired by the academic peer review process, where multiple experts evaluate and improve each other’s work.
The Self-Refine method consists of three stages: creation, review, and revision. In the first stage, each LLM independently generates an initial response to a given question. In the second stage, each LLM reviews its peers’ responses and provides feedback in the form of rationales for improvement. Finally, in the third stage, each LLM revises its initial response based on the feedback received from its peers and submits an improved version.
The authors evaluate Self-Refine using several benchmark tasks and show that it leads to significant improvements in the accuracy and diversity of LLM responses. They also compare their approach with other self-correction strategies, demonstrating that Self-Refine outperforms these methods in terms of both accuracy and efficiency.
The authors conclude that multi-agent collaboration has the potential to significantly enhance the reasoning abilities of LLMs, and that their proposed method provides a promising approach for achieving this goal. By enabling LLMs to learn from each other and improve their responses through mutual review and correction, Self-Refine offers a new perspective on the role of collaboration in advancing the field of natural language processing.