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.- Anthology ID:
- 2024.cl4health-1.33
- Volume:
- Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Dina Demner-Fushman, Sophia Ananiadou, Paul Thompson, Brian Ondov
- Venues:
- CL4Health | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 271–278
- Language:
- URL:
- https://aclanthology.org/2024.cl4health-1.33
- DOI:
- Cite (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.
- Cite (Informal):
- Intent Detection and Entity Extraction from Biomedical Literature (Mullick et al., CL4Health-WS 2024)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2024.cl4health-1.33.pdf