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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.09310 |
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| _version_ | 1866915367477051392 |
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| author | Shen, Jiawei Wan, Chengcheng Qiao, Ruoyi Zou, Jiazhen Xu, Hang Shao, Yuchen Zhang, Yueling Miao, Weikai Pu, Geguang |
| author_facet | Shen, Jiawei Wan, Chengcheng Qiao, Ruoyi Zou, Jiazhen Xu, Hang Shao, Yuchen Zhang, Yueling Miao, Weikai Pu, Geguang |
| contents | Large language models (LLMs) have been adopted to perform text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into structured query language (SQL). However, such a technique faces correctness problems and requires efficient repairing solutions. In this paper, we conduct the first comprehensive study of text-to-SQL errors. 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 29 error types of 7 categories. We also find that existing repairing attempts have limited correctness improvement at the cost of high computational overhead with many mis-repairs. Based on the findings, we propose MapleRepair, a novel text-to-SQL error detection and repairing framework. The evaluation demonstrates that MapleRepair outperforms existing solutions by repairing 13.8% more queries with neglectable mis-repairs and 67.4% less overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Study of In-Context-Learning-Based Text-to-SQL Errors Shen, Jiawei Wan, Chengcheng Qiao, Ruoyi Zou, Jiazhen Xu, Hang Shao, Yuchen Zhang, Yueling Miao, Weikai Pu, Geguang Computation and Language Artificial Intelligence Software Engineering Large language models (LLMs) have been adopted to perform text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into structured query language (SQL). However, such a technique faces correctness problems and requires efficient repairing solutions. In this paper, we conduct the first comprehensive study of text-to-SQL errors. 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 29 error types of 7 categories. We also find that existing repairing attempts have limited correctness improvement at the cost of high computational overhead with many mis-repairs. Based on the findings, we propose MapleRepair, a novel text-to-SQL error detection and repairing framework. The evaluation demonstrates that MapleRepair outperforms existing solutions by repairing 13.8% more queries with neglectable mis-repairs and 67.4% less overhead. |
| title | A Study of In-Context-Learning-Based Text-to-SQL Errors |
| topic | Computation and Language Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2501.09310 |