RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL

• URL: http://arxiv.org/abs/2406.09133v1
• Date: Thu, 13 Jun 2024 14:04:34 GMT
• Title: RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL
• Authors: Jiawen Yi, Guo Chen, Zixiang Shen,
• Abstract summary: This paper introduces a method for Text-to- Execute based on Refined Execution Model and Hardness Prompt. It reduces storage and training costs while maintaining performance. Our experiments on the Spider dataset, specifically with large-scale LMs, achieved an exceptional accuracy (EX) of 82.6%.
• Score: 1.734218686180302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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