  Large Language Models (LLMs) have shown great ability in solving traditional
natural language tasks and elementary reasoning tasks with appropriate
prompting techniques. However, their ability is still limited in solving
complicated science problems. In this work, we aim to push the upper bound of
the reasoning capability of LLMs by proposing a collaborative multi-agent,
multi-reasoning-path (CoMM) prompting framework. Specifically, we prompt LLMs
to play different roles in a problem-solving team, and encourage different
role-play agents to collaboratively solve the target task. In particular, we
discover that applying different reasoning paths for different roles is an
effective strategy to implement few-shot prompting approaches in the
multi-agent scenarios. Empirical results demonstrate the effectiveness of the
proposed methods on two college-level science problems over competitive
baselines. Our further analysis shows the necessity of prompting LLMs to play
different roles or experts independently. We release the code at:
https://github.com/amazon-science/comm-prompt
