  Large Language Model-based (LLM-based) Text-to-SQL methods have achieved
important progress in generating SQL queries for real-world applications. When
confronted with table content-aware questions in real-world scenarios,
ambiguous data content keywords and non-existent database schema column names
within the question leads to the poor performance of existing methods. To solve
this problem, we propose a novel approach towards Table Content-aware
Text-to-SQL with Self-Retrieval (TCSR-SQL). It leverages LLM's in-context
learning capability to extract data content keywords within the question and
infer possible related database schema, which is used to generate Seed SQL to
fuzz search databases. The search results are further used to confirm the
encoding knowledge with the designed encoding knowledge table, including column
names and exact stored content values used in the SQL. The encoding knowledge
is sent to obtain the final Precise SQL following multi-rounds of
generation-execution-revision process. To validate our approach, we introduce a
table-content-aware, question-related benchmark dataset, containing 1,692
question-SQL pairs. Comprehensive experiments conducted on this benchmark
demonstrate the remarkable performance of TCSR-SQL, achieving an improvement of
at least 13.7% in execution accuracy compared to other state-of-the-art
methods.
