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        <title>Intent Detection and Entity Extraction from Biomedical Literature</title>
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            <title>Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024</title>
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        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
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            <namePart type="given">Sophia</namePart>
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    <abstract>Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature. Despite recent research on large-language models (LLMs) motivated by endeavors to attain generalized intelligence, their efficacy in replacing task and domain-specific natural language understanding approaches remains questionable. In this paper, we address this question by conducting a comprehensive empirical evaluation of intent detection and named entity recognition (NER) tasks from biomedical text. We show that Supervised Fine Tuned approaches are still relevant and more effective than general-purpose LLMs. Biomedical transformer models such as PubMedBERT can surpass ChatGPT on NER task with only 5 supervised examples.</abstract>
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%0 Conference Proceedings
%T Intent Detection and Entity Extraction from Biomedical Literature
%A Mullick, Ankan
%A Gupta, Mukur
%A Goyal, Pawan
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Thompson, Paul
%Y Ondov, Brian
%S Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F mullick-etal-2024-intent
%X Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature. Despite recent research on large-language models (LLMs) motivated by endeavors to attain generalized intelligence, their efficacy in replacing task and domain-specific natural language understanding approaches remains questionable. In this paper, we address this question by conducting a comprehensive empirical evaluation of intent detection and named entity recognition (NER) tasks from biomedical text. We show that Supervised Fine Tuned approaches are still relevant and more effective than general-purpose LLMs. Biomedical transformer models such as PubMedBERT can surpass ChatGPT on NER task with only 5 supervised examples.
%U https://aclanthology.org/2024.cl4health-1.33
%P 271-278

Markdown (Informal)

[Intent Detection and Entity Extraction from Biomedical Literature](https://aclanthology.org/2024.cl4health-1.33) (Mullick et al., CL4Health-WS 2024)

• Intent Detection and Entity Extraction from Biomedical Literature (Mullick et al., CL4Health-WS 2024)

ACL

• Ankan Mullick, Mukur Gupta, and Pawan Goyal. 2024. Intent Detection and Entity Extraction from Biomedical Literature. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, pages 271–278, Torino, Italia. ELRA and ICCL.