$R^3$: "This is My SQL, Are You With Me?" A Consensus-Based Multi-Agent System for Text-to-SQL Tasks

Hanchen Xia, Feng Jiang, Naihao Deng, Cunxiang Wang, Guojiang Zhao, Rada Mihalcea, Yue Zhang

Keywords: Text-to-SQL, Multi-Agent System

TL;DR: We propose $R^3$ (Review-Rebuttal-Revision), a consensus-based multi-agent system for Text-to-SQL tasks.

Abstract: Large Language Models (LLMs) have demon- strated exceptional performance across diverse tasks. To harness their capabilities for Text- to-SQL, we introduce R3 (Review-Rebuttal- Revision), a consensus-based multi-agent sys- tem for Text-to-SQL tasks. R3 achieves the new state-of-the-art performance of 89.9 on the Spider test set. In the meantime, R3 achieves 61.80 on the Bird development set. R3 out- performs existing single-LLM and multi-agent Text-to-SQL systems by 1.3% to 8.1% on Spi- der and Bird, respectively. Surprisingly, we find that for Llama-3-8B, R3 outperforms chain-of- thought prompting by over 20%, even outper- forming GPT-3.5 on the Spider development set. We open-source our codebase at https: //github.com/1ring2rta/R3.

Include In Proceedings: Yes

Submission Number: 4