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ImmunoFOMO: Are Language Models missing what oncologists see?

arXiv 2506.11478 ↗First posted 13 Jun 2025cs.CLcited by 0

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Abstract (arXiv)

Language models (LMs) capabilities have grown with a fast pace over the past decade leading researchers in various disciplines, such as biomedical research, to increasingly explore the utility of LMs in their day-to-day applications. Domain specific language models have already been in use for biomedical natural language processing (NLP) applications. Recently however, the interest has grown towards medical language models and their understanding capabilities. In this paper, we investigate the medical conceptual grounding of various language models against expert clinicians for identification of hallmarks of immunotherapy in breast cancer abstracts. Our results show that pre-trained language models have potential to outperform large language models in identifying very specific (low-level) concepts.

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References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  3. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
  4. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  5. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  6. 2024BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domainscited by 301
  7. 2022Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequencescited by 69
  8. 2022Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approachescited by 39
  9. 2024Large Language Model Benchmarks in Medical Taskscited by 17
  10. 2024Large Language Models in the Clinic: A Comprehensive Benchmarkcited by 13
  11. 2024Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methodscited by 4

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