Generating accurate SQL for user queries (text-to-SQL) is a long-standing
problem since the generation of the SQL requires comprehending the query and
database and retrivale the accurate data from the database accordingly.
Existing models rely on the comprehensive ability of Large Language Models
(LLMs) to generate the SQL according to the database schema. However, there is
some necessary knowledge that is not explicitly included in the database schema
or has been learned by LLMs. Thus, the generated SQL of the
knowledge-insufficient queries may be inaccurate, which negatively impacts the
robustness of the text-to-SQL models. To deal with this situation, we propose
the Knowledge-to-SQL framework, which employs tailored Data Expert LLM (DELLM)
to provide helpful knowledge for all types of text-to-SQL models. Specifically,
we provide the detailed design of DELLM, in terms of table reading, and the
basic fine-tuning process. We further provide a Reinforcement Learning via
Database Feedback (RLDBF) training strategy to guide the DELLM to generate more
helpful knowledge for LLMs. Extensive experiments verify DELLM can enhance the
state-of-the-art LLMs on text-to-SQL tasks. The model structure and the
parameter weight of DELLM are released for further research.

利用知识生成 SQL 的框架对于 text-to-SQL 任务中缺乏知识的查询可以提供准确的 SQL 生成操作，并通过强化学习和数据库反馈进一步提高模型性能。