  In sophisticated existing Text-to-SQL methods exhibit errors in various
proportions, including schema-linking errors (incorrect columns, tables, or
extra columns), join errors, nested errors, and group-by errors. Consequently,
there is a critical need to filter out unnecessary tables and columns,
directing the language models attention to relevant tables and columns with
schema-linking, to reduce errors during SQL generation. Previous approaches
have involved sorting tables and columns based on their relevance to the
question, selecting the top-ranked ones for sorting, or directly identifying
the necessary tables and columns for SQL generation. However, these methods
face challenges such as lengthy model training times, high consumption of
expensive GPT-4 tokens in few-shot prompts, or suboptimal performance in schema
linking. Therefore, we propose an inventive schema linking method in two steps:
Firstly, generate an initial SQL query by utilizing the complete database
schema. Subsequently, extract tables and columns from the initial SQL query to
create a concise schema. Using CodeLlama-34B, when comparing the schemas
obtained by mainstream methods with ours for SQL generation, our schema
performs optimally. Leveraging GPT4, our SQL generation method achieved results
that are comparable to mainstream Text-to-SQL methods on the Spider dataset.
