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    <abstract>Text-to-SQL is a technology that converts natural language questions into executable SQL queries, allowing users to query and manage relational databases more easily. In recent years, large language models have significantly advanced the development of text-to-SQL. However, existing methods often overlook validation of the generated results during the SQL generation process. Current error identification methods are mainly divided into self-correction approaches based on large models and feedback methods based on SQL execution, both of which have limitations. We categorize SQL errors into three main types: system errors, skeleton errors, and value errors, and propose a multi-grained error identification method. Experimental results demonstrate that this method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities.</abstract>
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%0 Conference Proceedings
%T Boosting Text-to-SQL through Multi-grained Error Identification
%A Xu, Bo
%A Li, Shufei
%A Jing, Hongyu
%A Du, Ming
%A Song, Hui
%A Wang, Hongya
%A Xiao, Yanghua
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F xu-etal-2025-boosting
%X Text-to-SQL is a technology that converts natural language questions into executable SQL queries, allowing users to query and manage relational databases more easily. In recent years, large language models have significantly advanced the development of text-to-SQL. However, existing methods often overlook validation of the generated results during the SQL generation process. Current error identification methods are mainly divided into self-correction approaches based on large models and feedback methods based on SQL execution, both of which have limitations. We categorize SQL errors into three main types: system errors, skeleton errors, and value errors, and propose a multi-grained error identification method. Experimental results demonstrate that this method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities.
%U https://aclanthology.org/2025.coling-main.289/
%P 4282-4292

Markdown (Informal)

[Boosting Text-to-SQL through Multi-grained Error Identification](https://aclanthology.org/2025.coling-main.289/) (Xu et al., COLING 2025)

• Boosting Text-to-SQL through Multi-grained Error Identification (Xu et al., COLING 2025)

ACL

• Bo Xu, Shufei Li, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, and Yanghua Xiao. 2025. Boosting Text-to-SQL through Multi-grained Error Identification. In Proceedings of the 31st International Conference on Computational Linguistics, pages 4282–4292, Abu Dhabi, UAE. Association for Computational Linguistics.