A Survey of Pre-trained Language Models for Processing Scientific Text

Xanh Ho; Anh Khoa Duong Nguyen; An Tuan Dao; Junfeng Jiang; Yuki Chida; Kaito Sugimoto; Huy Quoc To; Florian Boudin; Akiko Aizawa

arXiv · 2401.17824

A Survey of Pre-trained Language Models for Processing Scientific Text

Xanh Ho·Anh Khoa Duong Nguyen·An Tuan Dao·Junfeng Jiang·Yuki Chida·Kaito Sugimoto·Huy Quoc To·Florian Boudin·Akiko Aizawa

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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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Xanh Ho, Anh Khoa Duong Nguyen, An Tuan Dao, Junfeng Jiang, Yuki Chida, Kaito Sugimoto, Huy Quoc To, Florian Boudin, Akiko Aizawa. 2024-01-31. A Survey of Pre-trained Language Models for Processing Scientific Text. https://arxiv.org/abs/2401.17824

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