Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the
  Key?
Qineng Wang Zihao Wang Ying Su Hanghang Tong Yangqiu Song
Recent progress in LLMs discussion suggests that multi-agent discussion
improves the reasoning abilities of LLMs. In this work, we reevaluate this
claim through systematic experiments, where we propose a novel group discussion
framework to enrich the set of discussion mechanisms. Interestingly, our
results show that a single-agent LLM with strong prompts can achieve almost the
same performance as the best existing discussion approach on a wide range of
reasoning tasks and backbone LLMs. We observe that the multi-agent discussion
performs better than a single agent only when there is no demonstration in the
prompt. Further study reveals the common interaction mechanisms of LLMs during
the discussion.