Intent Detection and Entity Extraction from BioMedical Literature
<|reference_start|>Intent Detection and Entity Extraction from BioMedical Literature: 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 endeavours 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.<|reference_end|>
@article{mullick2024intent,
    title={Intent Detection and Entity Extraction from BioMedical Literature},
    author={Ankan Mullick, Mukur Gupta, Pawan Goyal},
    journal={arXiv preprint arXiv:2404.03598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03598},
    primaryClass={cs.CL}
}