@inproceedings{jabir-etal-2024-saama,
    title = "Saama Technologies at {EHRSQL} 2024: {SQL} Generation through Classification Answer Selector by {LLM}",
    author = "Jabir, Mohammed  and
      Kanakarajan, Kamal  and
      Sankarasubbu, Malaikannan",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Bitterman, Danielle",
    booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.clinicalnlp-1.63/",
    doi = "10.18653/v1/2024.clinicalnlp-1.63",
    pages = "655--671",
    abstract = "The EHRSQL task aims to develop a dependable text-to-SQL model for Electronic Health Records (EHR) databases, which are crucial sources of clinical data that store patients' medical histories in hospitals. Large language models (LLM) have been proven to exhibit state-of-the-art performance for text-to-SQL tasks across various domains. To this end, we have developed a framework, SQL Generation through Classification Answer Selector by LLM (SCAS), which comprises two modules. The CAS module determines the answerability of the question, while the SG model generates the SQL query exclusively for answerable questions. Our system ranked 7th on the leaderboard with a Reliability Score of 53.21 on the official test set."
}