The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to-SQL tasks. In this paper, we introduce a synthetic data approach that combines data produced by larger, more powerful models (strong models) with error information data generated by smaller, not well-aligned models (weak models). The method not only enhances the domain generalization of text-to-SQL models but also explores the potential of error data supervision through preference learning. Furthermore, we employ the synthetic data approach for instruction tuning on open-source LLMs, resulting SENSE, a specialized text-to-SQL model. The effectiveness of SENSE is demonstrated through state-of-the-art results on the SPIDER and BIRD benchmarks, bridging the performance gap between open-source models and methods prompted by closed-source models.
@article{arxiv.2408.03256,
title = {Synthesizing Text-to-SQL Data from Weak and Strong LLMs},
author = {Jiaxi Yang and Binyuan Hui and Min Yang and Jian Yang and Junyang Lin and Chang Zhou},
journal= {arXiv preprint arXiv:2408.03256},
year = {2024}
}