LLM4SR: A Survey on Large Language Models for Scientific Research
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909451214127104 |
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| author | Luo, Ziming Yang, Zonglin Xu, Zexin Yang, Wei Du, Xinya |
| author_facet | Luo, Ziming Yang, Zonglin Xu, Zexin Yang, Wei Du, Xinya |
| contents | In recent years, the rapid advancement of Large Language Models (LLMs) has transformed the landscape of scientific research, offering unprecedented support across various stages of the research cycle. This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific research process. We analyze the unique roles LLMs play across four critical stages of research: hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing. Our review comprehensively showcases the task-specific methodologies and evaluation benchmarks. By identifying current challenges and proposing future research directions, this survey not only highlights the transformative potential of LLMs, but also aims to inspire and guide researchers and practitioners in leveraging LLMs to advance scientific inquiry. Resources are available at the following repository: https://github.com/du-nlp-lab/LLM4SR |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM4SR: A Survey on Large Language Models for Scientific Research Luo, Ziming Yang, Zonglin Xu, Zexin Yang, Wei Du, Xinya Computation and Language Digital Libraries In recent years, the rapid advancement of Large Language Models (LLMs) has transformed the landscape of scientific research, offering unprecedented support across various stages of the research cycle. This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific research process. We analyze the unique roles LLMs play across four critical stages of research: hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing. Our review comprehensively showcases the task-specific methodologies and evaluation benchmarks. By identifying current challenges and proposing future research directions, this survey not only highlights the transformative potential of LLMs, but also aims to inspire and guide researchers and practitioners in leveraging LLMs to advance scientific inquiry. Resources are available at the following repository: https://github.com/du-nlp-lab/LLM4SR |
| title | LLM4SR: A Survey on Large Language Models for Scientific Research |
| topic | Computation and Language Digital Libraries |
| url | https://arxiv.org/abs/2501.04306 |