@inproceedings{kobayashi-etal-2025-read,
    title = "You Only Read Once ({YORO}): Learning to Internalize Database Knowledge for Text-to-{SQL}",
    author = "Kobayashi, Hideo  and
      Lan, Wuwei  and
      Shi, Peng  and
      Chang, Shuaichen  and
      Guo, Jiang  and
      Zhu, Henghui  and
      Wang, Zhiguo  and
      Ng, Patrick",
    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.94/",
    pages = "1889--1901",
    ISBN = "979-8-89176-189-6",
    abstract = "While significant progress has been made on the text-to-SQL task, recent solutions repeatedly encode the same database schema for every question, resulting in unnecessary high inference cost and often overlooking crucial database knowledge. To address these issues, we propose You Only Read Once (YORO), a novel paradigm that directly internalizes database knowledge into the parametric knowledge of a text-to-SQL model during training and eliminates the need for schema encoding during inference. YORO significantly reduces the input token length by 66{\%}-98{\%}. Despite its shorter inputs, our empirical results demonstrate YORO{'}s competitive performances with traditional systems on three benchmarks as well as its significant outperformance on large databases. Furthermore, YORO excels in handling questions with challenging value retrievals such as abbreviation."
}