Generating accurate SQL according to natural language questions (text-to-SQL)
is a long-standing problem since it is challenging in user question
understanding, database schema comprehension, and SQL generation. Conventional
text-to-SQL systems include human engineering and deep neural networks.
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 and corresponding user questions more
challenging, PLMs with limited comprehension capabilities can lead to incorrect
SQL generation. This necessitates more sophisticated and tailored optimization
methods, which, in turn, restricts the applications of PLM-based systems. Most
recently, large language models (LLMs) have demonstrated significant abilities
in natural language understanding as the model scale remains increasing.
Therefore, integrating the LLM-based implementation can bring unique
opportunities, challenges, 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 current 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 directions.

根据自然语言问题（文本到 SQL）生成准确的 SQL 是一个长期存在的问题，传统的文本到 SQL 系统使用人工工程和深度神经网络，而最近的大型语言模型（LLMs）已经展示出在自然语言理解方面的显著能力，因此，将 LLM-based 实现整合到文本到 SQL 研究中可以带来独特的机遇、挑战和解决方案。