OmniSQL: Synthesizing High-Quality Text-to-SQL Data at Scale
Open accessAbstract
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research.
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- Authors
- Li, H., Wu, S., Zhang, X., Huang, X., Zhang, J., Jiang, F., Wang, S., Zhang, T., Chen, J., Shi, R., Chen, H., & Li, C.
- Affiliations
- Renmin University of China (CN)
- ByteDance (China) (CN)
- ORCID
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2025-07
- Volume
- 18
- Issue
- 11
- Pages
- 4695-4709
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W4413978227
- Cited by
- 56
- References
- 12
- Citations by year
- 2026: 44 · 2025: 12
- FWCI
- 18.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the VLDB Endowment (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62276270, 62322214, 62172424)
- Renmin University of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Keywords
- text-to-SQL, synthetic data generation, chain-of-thought prompting, synthetic database
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