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arXiv · 2501.09310

Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors

Jiawei Shen·Chengcheng Wan·Ruoyi Qiao·Jiazhen Zou·Hang Xu·Yuchen Shao·Yueling Zhang·Weikai Miao·Geguang Pu
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Abstract

Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries. However, such a technique faces correctness problems. In this paper, we conduct the first comprehensive study of text-to-SQL errors of ICL-based techniques. Our study covers four representative ICL-based techniques, five basic repairing methods, two benchmarks, and two LLM settings. We find that text-to-SQL errors are widespread and summarize 27 error types of 7 categories. We also find that existing repairing attempts have limited correctness improvement while having high computational overhead and many mis-repairs. Based on these findings, we propose MapleDoctor, a novel text-to-SQL error detection and repairing framework. The evaluation demonstrates that MapleDoctor outperforms existing solutions by repairing 13.8% more queries with a negligible number of mis-repairs and reducing 67.4% repair latency. The artifact is publicly available at GitHub.

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Jiawei Shen, Chengcheng Wan, Ruoyi Qiao, Jiazhen Zou, Hang Xu, Yuchen Shao, Yueling Zhang, Weikai Miao, Geguang Pu. 2025-01-16. Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors. https://arxiv.org/abs/2501.09310

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