Synthesizing Text-to-SQL Data from Weak and Strong LLMs

Jiaxi Yang, Binyuan Hui, Min Yang, Yang Jian, Junyang Lin, chang zhou

Synthesizing Text-to-SQL Data from Weak and Strong LLMs: 10 upvotes on Hugging Face Daily Papers, #11 of 12 papers on 2024-08-07. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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