A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?

• URL: http://arxiv.org/abs/2408.05109v5
• Date: Sun, 15 Jun 2025 15:53:09 GMT
• Title: A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?
• Authors: Xinyu Liu, Shuyu Shen, Boyan Li, Peixian Ma, Runzhi Jiang, Yuxin Zhang, Ju Fan, Guoliang Li, Nan Tang, Yuyu Luo,
• Abstract summary: We provide a review of Text-to- translation techniques powered by Large Language Models (LLMs)<n>We discuss the research challenges and open problems of Text-to- evaluation in the LLMs era.
• Score: 32.84561352339466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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