On the Discussion of Large Language Models: Symmetry of Agents and
  Interplay with Prompts
Qineng Wang Zihao Wang Ying Su Yangqiu Song
Two ways has been discussed to unlock the reasoning capability of a large
language model. The first one is prompt engineering and the second one is to
combine the multiple inferences of large language models, or the multi-agent
discussion. Theoretically, this paper justifies the multi-agent discussion
mechanisms from the symmetry of agents. Empirically, this paper reports the
empirical results of the interplay of prompts and discussion mechanisms,
revealing the empirical state-of-the-art performance of complex multi-agent
mechanisms can be approached by carefully developed prompt engineering. This
paper also proposes a scalable discussion mechanism based on conquer and merge,
providing a simple multi-agent discussion solution with simple prompts but
state-of-the-art performance.