@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Saama Technologies at EHRSQL 2024: SQL Generation through Classification Answer Selector by LLM
%A Jabir, Mohammed
%A Kanakarajan, Kamal
%A Sankarasubbu, Malaikannan
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Bitterman, Danielle
%S Proceedings of the 6th Clinical Natural Language Processing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F jabir-etal-2024-saama
%X 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.
%R 10.18653/v1/2024.clinicalnlp-1.63
%U https://aclanthology.org/2024.clinicalnlp-1.63/
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.63
%P 655-671
Markdown (Informal)
[Saama Technologies at EHRSQL 2024: SQL Generation through Classification Answer Selector by LLM](https://aclanthology.org/2024.clinicalnlp-1.63/) (Jabir et al., ClinicalNLP 2024)
ACL