Improving Factuality and Reasoning in Language Models through Multiagent Debate

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.

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.

these techniques are applied over a single model instance. Instead, we propose a complementary approach inspired by The Society of Mind [19] and multi-agent settings, where multiple language model instances (or agents) individually propose and jointly debate their responses and reasoning processes to arrive at a single common answer. More specifically, given a query, multiple instances of a language model first generate individual candidate answers to a query. Then each individual model instance reads and critiques the responses of all other models and uses this content to update its own answer. This step is then repeated over several rounds. This process induces models to construct answers that are consistent with both their internal critic as well as sensible in