MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915153614733312 |
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| author | Gorti, Satya Krishna Gofman, Ilan Liu, Zhaoyan Wu, Jiapeng Vouitsis, Noël Yu, Guangwei Cresswell, Jesse C. Hosseinzadeh, Rasa |
| author_facet | Gorti, Satya Krishna Gofman, Ilan Liu, Zhaoyan Wu, Jiapeng Vouitsis, Noël Yu, Guangwei Cresswell, Jesse C. Hosseinzadeh, Rasa |
| contents | Text-to-SQL generation enables non-experts to interact with databases via natural language. Recent advances rely on large closed-source models like GPT-4 that present challenges in accessibility, privacy, and latency. To address these issues, we focus on developing small, efficient, and open-source text-to-SQL models. We demonstrate the benefits of sampling multiple candidate SQL generations and propose our method, MSc-SQL, to critique them using associated metadata. Our sample critiquing model evaluates multiple outputs simultaneously, achieving state-of-the-art performance compared to other open-source models while remaining competitive with larger models at a much lower cost. Full code can be found at https://github.com/layer6ai-labs/msc-sql. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation Gorti, Satya Krishna Gofman, Ilan Liu, Zhaoyan Wu, Jiapeng Vouitsis, Noël Yu, Guangwei Cresswell, Jesse C. Hosseinzadeh, Rasa Computation and Language Text-to-SQL generation enables non-experts to interact with databases via natural language. Recent advances rely on large closed-source models like GPT-4 that present challenges in accessibility, privacy, and latency. To address these issues, we focus on developing small, efficient, and open-source text-to-SQL models. We demonstrate the benefits of sampling multiple candidate SQL generations and propose our method, MSc-SQL, to critique them using associated metadata. Our sample critiquing model evaluates multiple outputs simultaneously, achieving state-of-the-art performance compared to other open-source models while remaining competitive with larger models at a much lower cost. Full code can be found at https://github.com/layer6ai-labs/msc-sql. |
| title | MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.12916 |