  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.
