A Survey on Employing Large Language Models for Text-to-SQL Tasks
Liang Shi, Zhengju Tang, Nan Zhang, Xiaotong Zhang, Zhi Yang
Unverified — Be the first to reproduce this paper.
ReproduceAbstract
The increasing volume of data in relational databases and the expertise needed for writing SQL queries pose challenges for users to access and analyze data. Text-to-SQL (Text2SQL) solves the issues by utilizing natural language processing (NLP) techniques to convert natural language into SQL queries. With the development of Large Language Models (LLMs), a range of LLM-based Text2SQL methods have emerged. This survey provides a comprehensive review of LLMs in Text2SQL tasks. We review benchmark datasets, prompt engineering methods, fine-tuning methods, and base models in LLM-based Text2SQL methods. We provide insights in each part and discuss future directions in this field.