Improving Factuality and Reasoning in Language Models through Multiagent
  Debate
Yilun Du Shuang Li Antonio Torralba Joshua B. Tenenbaum Igor Mordatch
Large language models (LLMs) have demonstrated remarkable capabilities in
language generation, understanding, and few-shot learning in recent years. An
extensive body of work has explored how their performance may be further
improved through the tools of prompting, ranging from verification,
self-consistency, or intermediate scratchpads. In this paper, we present a
complementary approach to improve language responses where multiple language
model instances propose and debate their individual responses and reasoning
processes over multiple rounds to arrive at a common final answer. Our findings
indicate that this approach significantly enhances mathematical and strategic
reasoning across a number of tasks. We also demonstrate that our approach
improves the factual validity of generated content, reducing fallacious answers
and hallucinations that contemporary models are prone to. Our approach may be
directly applied to existing black-box models and uses identical procedure and
prompts for all tasks we investigate. Overall, our findings suggest that such
"society of minds" approach has the potential to significantly advance the
capabilities of LLMs and pave the way for further breakthroughs in language
generation and understanding.