MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
Autor: | Gorti, Satya Krishna, Gofman, Ilan, Liu, Zhaoyan, Wu, Jiapeng, Vouitsis, Noël, Yu, Guangwei, Cresswell, Jesse C., Hosseinzadeh, Rasa |
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Rok vydání: | 2024 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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 github.com/layer6ai-labs/msc-sql. Comment: 3rd Table Representation Learning Workshop at NeurIPS 2024 |
Databáze: | arXiv |
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