  Modern large language models (LLMs) like ChatGPT have shown remarkable
performance on general language tasks but still struggle on complex reasoning
tasks, which drives the research on cognitive behaviors of LLMs to explore
human-like problem-solving strategies. Along this direction, one representative
strategy is self-reflection, which asks an LLM to refine the solution with the
feedback generated by itself iteratively. However, our study shows that such
reflection-style methods suffer from the Degeneration-of-Thought (DoT) problem:
once the LLM has established confidence in its solutions, it is unable to
generate novel thoughts later through reflection even if its initial stance is
incorrect. To address the DoT problem, we propose a Multi-Agent Debate (MAD)
framework, in which multiple agents express their arguments in the state of
"tit for tat" and a judge manages the debate process to obtain a final
solution. Clearly, our MAD framework encourages divergent thinking in LLMs
which would be helpful for tasks that require deep levels of contemplation.
Experiment results on two challenging datasets, commonsense machine translation
and counter-intuitive arithmetic reasoning, demonstrate the effectiveness of
our MAD framework. Extensive analyses suggest that the adaptive break of debate
and the modest level of "tit for tat" state are required for MAD to obtain good
performance. Moreover, we find that LLMs might not be a fair judge if different
LLMs are used for agents. Code is available at
https://github.com/Skytliang/Multi-Agents-Debate.
