@inproceedings{sinha-etal-2024-domain,
title = "Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?",
author = "Sinha, Aman and
Mickus, Timothee and
Clausel, Marianne and
Constant, Mathieu and
Coubez, Xavier",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.16",
pages = "202--211",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?
%A Sinha, Aman
%A Mickus, Timothee
%A Clausel, Marianne
%A Constant, Mathieu
%A Coubez, Xavier
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F sinha-etal-2024-domain
%X 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.
%U https://aclanthology.org/2024.bionlp-1.16
%P 202-211
Markdown (Informal)
[Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?](https://aclanthology.org/2024.bionlp-1.16) (Sinha et al., BioNLP-WS 2024)
ACL