A Survey of Text-to-SQL in the Era of LLMs: Where Are We, and Where Are We Going?
Xinyu Liu, Shuyu Shen, Boyan Li, Peidong Ma, Runzhi Jiang, Yuxin Zhang, Ju Fan, Guoliang Li, Nan Tang, Yuyu Luo · IEEE Transactions on Knowledge and Data Engineering · 2025
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)Model: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)Data:From the collection of training data, data synthesis due to training data scarcity, to Text-to-SQL benchmarks; (3)Evaluation:Evaluating Text-to-SQL methods from multiple angles using different metrics and granularities; and (4)Error Analysis: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.