  Generating accurate SQL from natural language questions (text-to-SQL) is a
long-standing challenge due to the complexities in user question understanding,
database schema comprehension, and SQL generation. Conventional text-to-SQL
systems, comprising human engineering and deep neural networks, have made
substantial progress. Subsequently, pre-trained language models (PLMs) have
been developed and utilized for text-to-SQL tasks, achieving promising
performance. As modern databases become more complex, the corresponding user
questions also grow more challenging, causing PLMs with parameter constraints
to produce incorrect SQL. This necessitates more sophisticated and tailored
optimization methods, which, in turn, restricts the applications of PLM-based
systems. Recently, large language models (LLMs) have demonstrated significant
capabilities in natural language understanding as the model scale increases.
Therefore, integrating LLM-based implementation can bring unique opportunities,
improvements, and solutions to text-to-SQL research. In this survey, we present
a comprehensive review of LLM-based text-to-SQL. Specifically, we propose a
brief overview of the technical challenges and the evolutionary process of
text-to-SQL. Then, we provide a detailed introduction to the datasets and
metrics designed to evaluate text-to-SQL systems. After that, we present a
systematic analysis of recent advances in LLM-based text-to-SQL. Finally, we
discuss the remaining challenges in this field and propose expectations for
future research directions.
