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)
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Authors
- 1. Zihui Gu
- 2. Ju Fan
- 3. Nan Tang
- 4. Lei Cao
- 5. Bowen Jia
- 6. Sam Madden
- 7. Xiaoyong Du
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