Intent Detection and Entity Extraction from BioMedical Literature
Ankan Mullick Mukur Gupta Pawan Goyal
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.