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 atthis https URL.
View on arXiv@article{gorti2025_2410.12916,
title={ MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation },
author={ Satya Krishna Gorti and Ilan Gofman and Zhaoyan Liu and Jiapeng Wu and Noël Vouitsis and Guangwei Yu and Jesse C. Cresswell and Rasa Hosseinzadeh },
journal={arXiv preprint arXiv:2410.12916},
year={ 2025 }
}