@inproceedings{duan-etal-2025-dsqg,
    title = "{DSQG}-Syn: Synthesizing High-quality Data for Text-to-{SQL} Parsing by Domain Specific Question Generation",
    author = "Duan, Shaoming  and
      Wu, Youxuan  and
      Liu, Chuanyi  and
      Zhang, Yuhao  and
      Wang, Zirui  and
      Han, Peiyi  and
      Yu, Shengyuan  and
      Yan, Liang  and
      Liang, Yingwei",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.findings-naacl.162/",
    pages = "2971--2989",
    ISBN = "979-8-89176-195-7",
    abstract = "Synthetic data has recently proven effective in enhancing the accuracy of Text-to-SQL parsers. However, existing methods generate SQL queries first by randomly sampling tables and columns based on probability and then synthesize natural language questions (NLQs). This approach often produces a large number of NLQ-SQL pairs that are irrelevant to the target domain and inconsistent in query intent, significantly diminishing the fine-tuning effectiveness of LLMs. In this paper, we introduce DSQG-Syn, a novel text-to-SQL data synthesis framework that based on domain-specific question generation. Specifically, we design a question generation method that creates domain-relevant questions based on predefined question types, ensuring coverage of major SQL operations. Guided by these questions, we synthesize NLQ-SQL pairs that are both domain-relevant and intent-consistent. To further enhance data quality, we filter out noisy samples from the generated pairs. When popular open-source LLMs are fine-tuned on our high-quality synthesized dataset, they achieve significant accuracy improvements, surpassing the performance of closed-source LLM-based approaches. Moreover, we demonstrate that our method outperforms existing state-of-the-art (SOTA) data synthesis techniques."
}