@inproceedings{wang-etal-2025-dac,
    title = "{DAC}: Decomposed Automation Correction for Text-to-{SQL}",
    author = "Wang, Dingzirui  and
      Dou, Longxu  and
      Zhang, Xuanliang  and
      Zhu, Qingfu  and
      Che, Wanxiang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.22/",
    doi = "10.18653/v1/2025.findings-emnlp.22",
    pages = "385--402",
    ISBN = "979-8-89176-335-7",
    abstract = "Text-to-SQL is an important task that helps access databases by generating SQL queries. Currently, correcting the generated SQL based on large language models (LLMs) automatically is an effective method to enhance the quality of the generated SQL. However, previous research shows that it is hard for LLMs to detect mistakes in SQL directly, leading to poor performance. Therefore, in this paper, we propose to employ the decomposed correction to enhance text-to-SQL performance. We first demonstrate that detecting and fixing mistakes based on the decomposed sub-tasks is easier than using SQL directly. Then, we introduce Decomposed Automation Correction (DAC), which first generates the entities and skeleton corresponding to the question, and then compares the differences between the initial SQL and the generated entities and skeleton as feedback for correction. Experimental results show that, compared with the previous automation correction method, DAC improves performance by 1.4{\%} of Spider, Bird, and KaggleDBQA on average, demonstrating the effectiveness of DAC."
}