  Large Language Models (LLMs) have demonstrated strong performance on various
tasks. To unleash their power on the Text-to-SQL task, we propose $R^3$
(Review-Rebuttal-Revision), a consensus-based multi-agent system for
Text-to-SQL tasks. $R^3$ outperforms the existing single LLM Text-to-SQL
systems as well as the multi-agent Text-to-SQL systems by $1.3\%$ to $8.1\%$ on
Spider and Bird. Surprisingly, we find that for Llama-3-8B, $R^3$ outperforms
chain-of-thought prompting by over 20\%, even outperforming GPT-3.5 on the
development set of Spider.
