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

TL;DR

This survey reviews the development, benchmarks, and methods of employing Large Language Models for Text-to-SQL tasks, highlighting current approaches, evaluation metrics, and future challenges.

Contribution

It provides a comprehensive taxonomy of prompt engineering and finetuning methods for LLM-based Text2SQL, along with analysis of models and datasets.

Findings

Overview of classic benchmarks and evaluation metrics

Taxonomy of prompt engineering and finetuning methods

Discussion of challenges and future directions

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.