2024/06/13 by Jiawen Yi, Guo Chen, Yi, Jiawen +3
Computer Science · #Advanced Database Systems and Queries #Computation and Language (cs.CL) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2406.09133
openalex publication_date 2024/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.