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

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

URL: http://arxiv.org/abs/2407.15186v4
Date: Thu, 7 Nov 2024 03:26:58 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: The increasing volume of data in relational databases pose challenges for users to access and analyze data. Text-to-sql (Text2) solves the issues by utilizing natural language processing (NLP) techniques to convert natural language intosql queries. With the development of Large Language Models (LLMs), a range of LLM-based Text2 methods have emerged.
Score: 9.527891544418805
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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