DSQG-Syn: Synthesizing High-quality Data for Text-to-SQL Parsing by Domain Specific Question Generation
Shaoming Duan, Youxuan Wu, Chuanyi Liu, Yuhao Zhang, Zi-Rui Wang, Peiyi Han, Shengyuan Yu, Liang Yan, Yingwei Liang · 2025
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 domainrelevant questions based on predefined question types, ensuring coverage of major SQL operations.Guided by these questions, we synthesize NLQ-SQL pairs that are both domainrelevant 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 highquality 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.* These authors contributed equally to this work.