Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?
<|reference_start|>Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?: The success of pretrained language models (PLMs) across a spate of use-cases has led to significant investment from the NLP community towards building domain-specific foundational models. On the other hand, in mission critical settings such as biomedical applications, other aspects also factor in-chief of which is a model's ability to produce reasonable estimates of its own uncertainty. In the present study, we discuss these two desiderata through the lens of how they shape the entropy of a model's output probability distribution. We find that domain specificity and uncertainty awareness can often be successfully combined, but the exact task at hand weighs in much more strongly.<|reference_end|>
@article{sinha2024domain-specific,
    title={Domain-specific or Uncertainty-aware models: Does it really make a
  difference for biomedical text classification?},
    author={Aman Sinha, Timothee Mickus, Marianne Clausel, Mathieu Constant and
  Xavier Coubez},
    journal={arXiv preprint arXiv:2407.12626},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12626},
    primaryClass={cs.CL}
}