A Survey of Text-to-SQL in the Era of LLMs: Where Are We, and Where Are We Going?
Translating users' natural language queries (NL) into SQL queries (i.e., Text-to-SQL, a.k.a. NL2SQL) can significantly reduce barriers to accessing relational databases and support various commercial applications. The performance of Text-to-SQL has been greatly enhanced with the emergence of Large Language Models (LLMs). In this survey, we provide a comprehensive review of Text-to-SQL techniques powered by LLMs, covering its entire lifecycle from the following four aspects: (1) <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Model:</i><bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> Text-to-SQL translation techniques that tackle not only NL ambiguity and under-specification, but also properly map NL with database schema and instances; (2) <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data:</i><bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> From the collection of training data, data synthesis due to training data scarcity, to Text-to-SQL benchmarks; (3) <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Evaluation:</i><bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> Evaluating Text-to-SQL methods from multiple angles using different metrics and granularities; and (4) <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Error Analysis:</i><bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> analyzing Text-to-SQL errors to find the root cause and guiding Text-to-SQL models to evolve. Moreover, we offer a rule of thumb for developing Text-to-SQL solutions. Finally, we discuss the research challenges and open problems of Text-to-SQL in the LLMs era.
Authors
- Ju Fan
- Jianguo Liu (ORCID: https://orcid.org/0000-0001-6344-0087)
- Guoliang Li (ORCID: https://orcid.org/0000-0002-0599-5356)
- Boyan Li
- Shuyu Shen
- Yuyu Luo
- Nan Tang (ORCID: https://orcid.org/0000-0003-2832-0295)
- Yuxin Zhang (ORCID: https://orcid.org/0009-0005-4541-6098)
- Runzhi Jiang
- Peidong Ma
Institutions
- Renmin University of China (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1109/tkde.2025.3592032
- Citations
- 24
- Primary Topic
- Mathematics, Computing, and Information Processing
- Type
- article
- Field-Weighted Citation Impact
- 36.10