Query and Conquer: Execution-Guided SQL Generation
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915219714867200 |
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| author | Borchmann, Łukasz Wydmuch, Marek |
| author_facet | Borchmann, Łukasz Wydmuch, Marek |
| contents | We propose a novel approach for generating complex outputs that significantly improves accuracy in text-to-SQL tasks. Our method leverages execution results to select the most semantically consistent query from multiple candidates, enabling smaller, cost-effective models to surpass computationally intensive reasoning methods such as o1, o3-mini, and DeepSeek R1 while reducing inference cost by as much as 30 times. It integrates effortlessly with existing models, offering a practical and scalable pathway to state-of-the-art SQL generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24364 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Query and Conquer: Execution-Guided SQL Generation Borchmann, Łukasz Wydmuch, Marek Computation and Language We propose a novel approach for generating complex outputs that significantly improves accuracy in text-to-SQL tasks. Our method leverages execution results to select the most semantically consistent query from multiple candidates, enabling smaller, cost-effective models to surpass computationally intensive reasoning methods such as o1, o3-mini, and DeepSeek R1 while reducing inference cost by as much as 30 times. It integrates effortlessly with existing models, offering a practical and scalable pathway to state-of-the-art SQL generation. |
| title | Query and Conquer: Execution-Guided SQL Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.24364 |