A Survey of Pre-trained Language Models for Processing Scientific Text
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929230281965568 |
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| author | Ho, Xanh Nguyen, Anh Khoa Duong Dao, An Tuan Jiang, Junfeng Chida, Yuki Sugimoto, Kaito To, Huy Quoc Boudin, Florian Aizawa, Akiko |
| author_facet | Ho, Xanh Nguyen, Anh Khoa Duong Dao, An Tuan Jiang, Junfeng Chida, Yuki Sugimoto, Kaito To, Huy Quoc Boudin, Florian Aizawa, Akiko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17824 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey of Pre-trained Language Models for Processing Scientific Text Ho, Xanh Nguyen, Anh Khoa Duong Dao, An Tuan Jiang, Junfeng Chida, Yuki Sugimoto, Kaito To, Huy Quoc Boudin, Florian Aizawa, Akiko Computation and Language 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. |
| title | A Survey of Pre-trained Language Models for Processing Scientific Text |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.17824 |