MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh
Abstract
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.
BibTeX
@inproceedings{gorti-etal-2025-msc,
title = "{MS}c-{SQL}: Multi-Sample Critiquing Small Language Models For Text-To-{SQL} Translation",
author = {Gorti, Satya Krishna and
Gofman, Ilan and
Liu, Zhaoyan and
Wu, Jiapeng and
Vouitsis, No{\"e}l and
Yu, Guangwei and
Cresswell, Jesse C. and
Hosseinzadeh, Rasa},
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.107/",
pages = "2145--2160",
ISBN = "979-8-89176-189-6"
}