@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://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.107/",
    pages = "2145--2160",
    ISBN = "979-8-89176-189-6",
    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."
}