Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning
Summary: Divide-and-conquer framework for few-shot Text-to-SQL: structure stage emits SQL skeletons; content stage fills identifiers. Hybrid prompts (learnable + fixed) with constrained and structure-guided decoding deliver strong few-shot gains; Spider shows ~5% accuracy boost with under 500 labels. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zihui Gu (Renmin University of China)
- 2. Ju Fan (Renmin University of China)
- 3. Nan Tang (Hong Kong University of Science and Technology; Qatar Computing Research Institute)
- 4. Lei Cao (Massachusetts Institute of Technology; University of Arizona)
- 5. Bowen Jia (Renmin University of China)
- 6. Sam Madden (Massachusetts Institute of Technology)
- 7. Xiaoyong Du (Renmin University of China)
BibTeX Citation
@inproceedings{gu_sigmod23,
title = {{Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning}},
author = {Gu, Zihui and Fan, Ju and Tang, Nan and Cao, Lei and Jia, Bowen and Madden, Sam and Du, Xiaoyong},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589292},
url = {https://dl.acm.org/doi/10.1145/3589292},
year = {2023}
}
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