If Large Language Models debate their answers they can reach better answers. 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. The findings indicate that this approach significantly enhances mathematical and strategic reasoning across a number of tasks. They demonstrate that the approach improves the factual validity of generated content,reducing fallacious answers and hallucinations that contemporary models are prone to. The approach may be directly applied to existing black-box models and uses identical procedure and prompts for all tasks they investigate. The 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.
Google Brain researchers published Improving Factuality and Reasoning in Language Models through Multiagent Debate.
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