@inproceedings{joy-etal-2024-project,
title = "Project {PRIMUS} at {EHRSQL} 2024 : Text-to-{SQL} Generation using Large Language Model for {EHR} Analysis",
author = "Joy, Sourav and
Ahmed, Rohan and
Saha, Argha and
Habil, Minhaj and
Das, Utsho and
Bhowmik, Partha",
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-1.41/",
doi = "10.18653/v1/2024.clinicalnlp-1.41",
pages = "422--427",
abstract = "This paper explores the application of the sqlcoders model, a pre-trained neural network, for automatic SQL query generation from natural language questions. We focus on the model`s internal functionality and demonstrate its effectiveness on a domain-specific validation dataset provided by EHRSQL. The sqlcoders model, based on transformers with attention mechanisms, has been trained on paired examples of natural language questions and corresponding SQL queries. It takes advantage of a carefully crafted prompt that incorporates the database schema alongside the question to guide the model towards the desired output format."
}
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%0 Conference Proceedings
%T Project PRIMUS at EHRSQL 2024 : Text-to-SQL Generation using Large Language Model for EHR Analysis
%A Joy, Sourav
%A Ahmed, Rohan
%A Saha, Argha
%A Habil, Minhaj
%A Das, Utsho
%A Bhowmik, Partha
%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 joy-etal-2024-project
%X This paper explores the application of the sqlcoders model, a pre-trained neural network, for automatic SQL query generation from natural language questions. We focus on the model‘s internal functionality and demonstrate its effectiveness on a domain-specific validation dataset provided by EHRSQL. The sqlcoders model, based on transformers with attention mechanisms, has been trained on paired examples of natural language questions and corresponding SQL queries. It takes advantage of a carefully crafted prompt that incorporates the database schema alongside the question to guide the model towards the desired output format.
%R 10.18653/v1/2024.clinicalnlp-1.41
%U https://aclanthology.org/2024.clinicalnlp-1.41/
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.41
%P 422-427
Markdown (Informal)
[Project PRIMUS at EHRSQL 2024 : Text-to-SQL Generation using Large Language Model for EHR Analysis](https://aclanthology.org/2024.clinicalnlp-1.41/) (Joy et al., ClinicalNLP 2024)
ACL