Paper · arXiv 2501.04306
LLM4SR: A Survey on Large Language Models for Scientific Research
Ziming Luo, Zonglin Yang, Zexin Xu, Wei Yang, Xinya Du
LLMs are transforming scientific research across hypothesis discovery, experiment planning, writing, and reviewing, offering new methodologies and benchmarks while highlighting challenges for future research.
Abstract
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