SchemaRAG: A Schema-aware Retrieval-Augmented Generation Framework for Text-to-SQL
Summary: Schema-aware RAG for Text-to-SQL: fine-tuned SchemaLinker distills CoT/GRPO to align NL with schema items, plus schema-conditioned example retrieval. Pareto-optimal candidate selection boosts robustness/validity, outperforming prior LLM Text-to-SQL baselines. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Di Wu
- 2. Zetong Tang
- 3. Yi He
- 4. Xin Luo
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 998 | CodeS: Towards Building Open-source Language Models for Text-to-SQL | 2024 | SIGMOD | 0.00014729379 |
| 1,732 | CatSQL: Towards Real World Natural Language to SQL Applications | 2023 | VLDB | 0.00010732004 |
| 5,033 | FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis | 2024 | SIGMOD | 5.7486224e-05 |
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