Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
This addresses the challenge of enhancing reasoning capabilities in LLMs for AI applications, representing an incremental improvement over single-model strategies.
The paper tackles the problem of improving complex reasoning in large language models by introducing a multi-agent collaboration strategy that mimics academic peer review, resulting in superior accuracy across ten datasets compared to existing methods.
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have explored human-like problem-solving strategies, such as self-correct, to push further the boundary of single-model reasoning ability. In this work, we let a single model "step outside the box" by engaging multiple models to correct each other. We introduce a multi-agent collaboration strategy that emulates the academic peer review process. Each agent independently constructs its own solution, provides reviews on the solutions of others, and assigns confidence levels to its reviews. Upon receiving peer reviews, agents revise their initial solutions. Extensive experiments on three different types of reasoning tasks show that our collaboration approach delivers superior accuracy across all ten datasets compared to existing methods. Further study underscores the effectiveness of integrating confidence in reviews, demonstrates the superiority of feedback exchange over mere solution sharing, and highlights the role of capability and diversity in fostering successful collaboration.