Boosting Text-to-SQL through Multi-grained Error Identification
Bo Xu, Shufei Li, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, Yanghua Xiao
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.
BibTeX
@inproceedings{xu-etal-2025-boosting,
title = "Boosting Text-to-{SQL} through Multi-grained Error Identification",
author = "Xu, Bo and
Li, Shufei and
Jing, Hongyu and
Du, Ming and
Song, Hui and
Wang, Hongya and
Xiao, Yanghua",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.289/",
pages = "4282--4292"
}