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.

该研究论文介绍了一种基于精炼模式和难度提示的文本到SQL的方法，通过过滤低相关性的模式信息和使用语言模型通过难度提示来减少存储和训练成本，同时保持性能，实现了在大规模语言模型上82.6%的出色执行准确度，证明了我们方法在实际应用中的有效性和更大的适用性。

RH-SQL：精确模式和难度提示的文本转SQL