  Generating accurate Structured Querying Language (SQL) is a long-standing
problem, especially in matching users' semantic queries with structured
databases and then generating structured SQL. Existing models typically input
queries and database schemas into the LLM and rely on the LLM to perform
semantic-structure matching and generate structured SQL. However, such
solutions overlook the structural information within user queries and
databases, which can be utilized to enhance the generation of structured SQL.
This oversight can lead to inaccurate or unexecutable SQL generation. To fully
exploit the structure, we propose a structure-to-SQL framework, which leverages
the inherent structure information to improve the SQL generation of LLMs.
Specifically, we introduce our Structure Guided SQL~(SGU-SQL) generation model.
SGU-SQL first links user queries and databases in a structure-enhanced manner.
It then decomposes complicated linked structures with grammar trees to guide
the LLM to generate the SQL step by step. Extensive experiments on two
benchmark datasets illustrate that SGU-SQL can outperform sixteen SQL
generation baselines.
