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

IF 28 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2025-06-03 DOI:10.1145/3737873

Liang Shi, Zhengju Tang, Nan Zhang, Xiaotong Zhang, Zhi Yang

引用次数: 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.

查看原文本刊更多论文

文本转sql任务中使用大型语言模型的研究

随着大型语言模型（llm）的发展，出现了一系列基于llm的文本到sql （Text2SQL）方法。本调查提供了基于法学硕士的Text2SQL研究的全面回顾。我们首先列举经典基准和评估指标。对于两种主流方法，快速工程和微调，我们介绍了一个全面的分类，并提供了每个子类别的实际见解。我们对上述方法和各种模型在已知数据集上的评估进行了全面分析，并提取了一些特征。最后，讨论了该领域面临的挑战和未来的发展方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.