  Large Language Models (LLMs) still struggle with complex reasoning tasks.
Motivated by the society of minds (Minsky, 1988), we propose ReConcile, a
multi-model multi-agent framework designed as a round table conference among
diverse LLM agents to foster diverse thoughts and discussion for improved
consensus. ReConcile enhances the reasoning capabilities of LLMs by holding
multiple rounds of discussion, learning to convince other agents to improve
their answers, and employing a confidence-weighted voting mechanism. In each
round, ReConcile initiates discussion between agents via a 'discussion prompt'
that consists of (a) grouped answers and explanations generated by each agent
in the previous round, (b) their uncertainties, and (c) demonstrations of
answer-rectifying human explanations, used for convincing other agents. This
discussion prompt enables each agent to revise their responses in light of
insights from other agents. Once a consensus is reached and the discussion
ends, ReConcile determines the final answer by leveraging the confidence of
each agent in a weighted voting scheme. We implement ReConcile with ChatGPT,
Bard, and Claude2 as the three agents. Our experimental results on various
benchmarks demonstrate that ReConcile significantly enhances the reasoning
performance of the agents (both individually and as a team), surpassing prior
single-agent and multi-agent baselines by 7.7% and also outperforming GPT-4 on
some of these datasets. We also experiment with GPT-4 itself as one of the
agents in ReConcile and demonstrate that its initial performance also improves
by absolute 10.0% through discussion and feedback from other agents. Finally,
we also analyze the accuracy after every round and observe that ReConcile
achieves better and faster consensus between agents, compared to a multi-agent
debate baseline. Our code is available at: https://github.com/dinobby/ReConcile

Computation and Language;Artificial Intelligence;Machine Learning