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
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
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
Externí odkaz:	http://arxiv.org/abs/2410.12916 Zobrazit plný text záznamu View this record from Arxiv