SCoT2S: Self-correcting Text-to-SQL parsing by leveraging LLMs

Abstract

Text-to-SQL parsing, which converts natural language questions into executable SQL queries, has emerged as a critical technology for enabling non-technical users to interact with databases effectively. Although recent advances in this field have shown promise, existing models still struggle with complex semantic understanding and accurate SQL generation, particularly in handling schema relationships and join operations. To address these challenges, we propose SCoT2S (Self-Correcting Text-to-SQL), a novel framework that leverages large language models to automatically identify and rectify errors in SQL query generation. Through systematic error analysis of existing Text-to-SQL models, we identify that schema linking and join operations account for more than 70% of parsing errors. Our SCoT2S framework addresses these issues through a three-stage approach: initial SQL generation, comprehensive error detection, and targeted correction using large language models. This approach enables real-time error identification and correction during the parsing process. Extensive experiments demonstrate the effectiveness of the proposed SCoT2S in the Spider benchmark data set. Specifically, SCoT2S shows significant improvements, with a 2.8% increase in EM scores and a 4.0% increase in EX scores compared to current state-of-the-art methods.