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

• URL: http://arxiv.org/abs/2408.03256v1
• Date: Tue, 6 Aug 2024 15:40:32 GMT
• Title: Synthesizing Text-to-SQL Data from Weak and Strong LLMs
• Authors: Jiaxi Yang, Binyuan Hui, Min Yang, Jian Yang, Junyang Lin, Chang Zhou,
• Abstract summary: The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to- tasks. We introduce a synthetic data approach that combines data produced by larger, more powerful models with error information data generated by smaller, not well-aligned models.
• Score: 68.69270834311259
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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