@inproceedings{sun-etal-2023-exploratory,
    title = "An Exploratory Study on Model Compression for Text-to-{SQL}",
    author = "Sun, Shuo  and
      Gao, Yuze  and
      Zhang, Yuchen  and
      Su, Jian  and
      Chen, Bin  and
      Lin, Yingzhan  and
      Sun, Shuqi",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.740",
    doi = "10.18653/v1/2023.findings-acl.740",
    pages = "11647--11654",
    abstract = "Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases. Recent approaches to Text-to-SQL rely on pre-trained language models that are computationally expensive and technically challenging to deploy in real-world applications that require real-time or on-device processing capabilities. In this paper, we perform a focused study on the feasibility of applying recent model compression techniques to sketch-based and sequence-to-sequence Text-to-SQL models. Our results reveal that sketch-based Text-to-SQL models generally have higher inference efficiency and respond better to model compression than sequence-to-sequence models, making them ideal for real-world deployments, especially in use cases with simple SQL statements.",
}

Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases. Recent approaches to Text-to-SQL rely on pre-trained language models that are computationally expensive and technically challenging to deploy in real-world applications that require real-time or on-device processing capabilities. In this paper, we perform a focused study on the feasibility of applying recent model compression techniques to sketch-based and sequence-to-sequence Text-to-SQL models. Our results reveal that sketch-based Text-to-SQL models generally have higher inference efficiency and respond better to model compression than sequence-to-sequence models, making them ideal for real-world deployments, especially in use cases with simple SQL statements.
Sun, Shuo Gao, Yuze Zhang, Yuchen Su, Jian Chen, Bin Lin, Yingzhan Sun, Shuqi
An Exploratory Study on Model Compression for Text-to-SQL