  Text-to-SQL is a technology that converts natural language queries into the
structured query language SQL. A novel research approach that has recently
gained attention focuses on methods based on the complexity of SQL queries,
achieving notable performance improvements. However, existing methods entail
significant storage and training costs, which hampers their practical
application. To address this issue, this paper introduces a method for
Text-to-SQL based on Refined Schema and Hardness Prompt. By filtering out
low-relevance schema information with a refined schema and identifying query
hardness through a Language Model (LM) to form prompts, this method reduces
storage and training costs while maintaining performance. It's worth mentioning
that this method is applicable to any sequence-to-sequence (seq2seq) LM. Our
experiments on the Spider dataset, specifically with large-scale LMs, achieved
an exceptional Execution accuracy (EX) of 82.6%, demonstrating the
effectiveness and greater suitability of our method for real-world
applications.
