A Survey of Pre-trained Language Models for Processing Scientific Text
- URL: http://arxiv.org/abs/2401.17824v1
- Date: Wed, 31 Jan 2024 13:35:07 GMT
- Title: A Survey of Pre-trained Language Models for Processing Scientific Text
- Authors: Xanh Ho, Anh Khoa Duong Nguyen, An Tuan Dao, Junfeng Jiang, Yuki
Chida, Kaito Sugimoto, Huy Quoc To, Florian Boudin and Akiko Aizawa
- Abstract summary: The number of Language Models (LMs) dedicated to processing scientific text is on the rise.
This work provides a comprehensive review of SciLMs, including an analysis of their effectiveness across different domains, tasks and datasets.
- Score: 26.986805626077892
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The number of Language Models (LMs) dedicated to processing scientific text
is on the rise. Keeping pace with the rapid growth of scientific LMs (SciLMs)
has become a daunting task for researchers. To date, no comprehensive surveys
on SciLMs have been undertaken, leaving this issue unaddressed. Given the
constant stream of new SciLMs, appraising the state-of-the-art and how they
compare to each other remain largely unknown. This work fills that gap and
provides a comprehensive review of SciLMs, including an extensive analysis of
their effectiveness across different domains, tasks and datasets, and a
discussion on the challenges that lie ahead.
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