A Survey on Employing Large Language Models for Text-to-SQL Tasks

• URL: http://arxiv.org/abs/2407.15186v5
• Date: Tue, 03 Jun 2025 14:40:01 GMT
• Title: A Survey on Employing Large Language Models for Text-to-SQL Tasks
• Authors: Liang Shi, Zhengju Tang, Nan Zhang, Xiaotong Zhang, Zhi Yang,
• Abstract summary: We present an overall analysis of the methods and various models evaluated on well-known datasets.<n>We discuss the challenges and future directions in this field.
• Score: 9.527891544418805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of the Large Language Models (LLMs), a large range of LLM-based Text-to-SQL(Text2SQL) methods have emerged. This survey provides a comprehensive review of LLM-based Text2SQL studies. We first enumerate classic benchmarks and evaluation metrics. For the two mainstream methods, prompt engineering and finetuning, we introduce a comprehensive taxonomy and offer practical insights into each subcategory. We present an overall analysis of the above methods and various models evaluated on well-known datasets and extract some characteristics. Finally, we discuss the challenges and future directions in this field.

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