  Generating accurate SQL queries for user questions (text-to-SQL) has been a
long-standing challenge since it requires a deep understanding of both the
user's question and the corresponding database schema in order to retrieve the
desired content accurately. Existing methods rely on the comprehensive
capability of large language models (LLMs) to generate the SQL. However, some
necessary knowledge is not explicitly included in the database schema and user
question or has been learned by LLMs. Thus, the generated SQL of the
knowledge-insufficient questions may be inaccurate, negatively influencing the
text-to-SQL models' performance and robustness. To address this challenge, we
propose the Knowledge-to-SQL framework, which employs tailored Data Expert LLM
(DELLM) to provide helpful knowledge for all text-to-SQL models. Specifically,
we introduce the detailed implementation of DELLM regarding table reading and
the basic fine-tuning process. We further propose a Preference Learning via
Database Feedback (PLDBF) strategy, refining the DELLM to generate more helpful
knowledge for LLMs. Extensive experiments verify that DELLM can enhance the
state-of-the-art approaches for text-to-SQL tasks. The corresponding code of
DELLM is released for further research.
