首份综述系统解析大模型如何重塑科研全流程
LLM4SR: A Survey on Large Language Models for Scientific Research
- 分四个阶段分析大模型在科研中的应用机制
- 梳理任务方法与评估基准，覆盖假说生成到同行评审
- 适合关注AI赋能科研的研究者与实践者参考
近年来，大型语言模型（LLMs）的快速发展正在重塑科学研究的格局，在研究周期的多个阶段提供前所未有的支持。本文首次系统性综述大模型如何推动科学研究所发生的变革。我们分析了大模型在科研四大关键阶段——假说发现、实验规划与实施、科学写作、同行评审——中所扮演的独特角色。本综述全面展示了各任务的具体方法与评估基准。通过识别当前挑战并提出未来研究方向，该工作不仅凸显了大模型的变革潜力，也旨在激励和引导研究人员利用大模型推进科学探索。相关资源可在以下仓库获取：https://github.com/du-nlp-lab/LLM4SR
原文摘要 · Abstract (English)
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
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