OpenSQL: Data-Efficient Text-to-SQL for Open-Source LLMs via Synthesized Intermediate Supervision
Summary: OpenSQL targets data-efficient Text-to-SQL with open-source LLMs by synthesizing intermediate supervision from scarce (question, SQL) pairs. Global-local schema linking + reasoning-enhanced, clause/semantic-level candidate selection + task-aware augmentation yield 70% Bird-dev with only 14K samples (32B), beating OmniSQL (2.5M). (summarized by gpt-5.4-mini on May 27 2026)
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Authors
- 1. Ruilin Hu (Tsinghua University)
- 2. Yuyu Luo (Hong Kong University of Science and Technology)
- 3. Guoliang Li (Tsinghua University)
- 4. Shuangqiao Wu (Tencent)
- 5. Yun Luo (Tencent)
BibTeX Citation
@article{hu_vldb26,
title = {{OpenSQL: Data-Efficient Text-to-SQL for Open-Source LLMs via Synthesized Intermediate Supervision}},
author = {Hu, Ruilin and Luo, Yuyu and Li, Guoliang and Wu, Shuangqiao and Luo, Yun},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1628--1642},
doi = {10.14778/3801059.3801074},
url = {https://doi.org/10.14778/3801059.3801074},
year = {2026}
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