A Comprehensive Review of LLM-based Text-to-SQL Systems: Methods, Datasets, and Trends

Translation of Natural Language to SQL queries (i.e. Text-to-SQL or NL2SQL) helps the user to easily access the relational database. It also helps in various commercial applications. In recent years, the development of Large Language has increased the performance of the NL2SQL system. It enhances the semantic understanding, schema linking, and SQL generation, even for complex and crossdomain queries. This paper reviews recently published research papers between 2018 - 2025, focusing on LLM-based methods for Text-to-SQL tasks. We examine the system pipelines covering pre-processing, translation, and post-processing stages, along with commonly used datasets and tools. We also discuss advances in schema linking, reasoning-based query generation, and the use of retrieval-augmented generation for providing additional context. Based on the surveyed literature, we summarize key trends, challenges, and future directions, aiming to provide an accessible overview for students and researchers interested in LLM-based NL2SQL systems.