LLM正重塑美国科研资助方向，影响项目立项与产出。
The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding
- 分析联邦资助提案与获奖数据，发现2023年起LLM使用激增且呈两极分化。
- 高LLM使用项目语义相似度更低，更贴近近期已获资助课题。
- 在NIH中，LLM提升立项成功率与早期发文量，但主要贡献于普通论文。
联邦科研资助塑造了美国科学事业的方向、多样性和影响力。大语言模型（LLMs）正迅速渗透到科研实践中，潜力巨大但引发广泛担忧。尽管对AI在科研写作与评价中的应用日益关注，但其如何重塑公共资助格局仍不清楚。本文结合两个大型美国研究型大学的保密NSF和NIH项目申请数据（含已资助、未资助及待审项目），以及公开的全部NSF和NIH资助记录，分析了LLM在联邦资助流程关键阶段的参与情况。结果显示，自2023年起LLM使用率显著上升，并呈现双峰分布，表明使用程度存在明显两极分化。无论在私密提交还是公开资助中，更高的LLM参与度均与较低的语义独特性相关，使项目更接近同机构近期获批的研究。这一转变的影响因机构而异：在NIH中，LLM使用与提案成功及早期发表数量正相关；而在NSF中则无显著关联。值得注意的是，NIH中的生产率提升集中体现在非高被引论文上。这些发现提供了大规模证据，表明LLM正在改变科研选题定位、遴选机制与公共资助转化路径，对研究组合治理、多样性及科学长期影响力具有深远意义。
原文摘要 · Abstract (English)
Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly diffusing into scientific practice, holding substantial promise while raising widespread concerns. Despite growing attention to AI use in scientific writing and evaluation, little is known about how the rise of LLMs is reshaping the public funding landscape. Here, we examine LLM involvement at key stages of the federal funding pipeline by combining two complementary data sources: confidential National Science Foundation (NSF) and National Institutes of Health (NIH) proposal submissions from two large US R1 universities, including funded, unfunded, and pending proposals, and the full population of publicly released NSF and NIH awards. We find that LLM use rises sharply beginning in 2023 and exhibits a bimodal distribution, indicating a clear split between minimal and substantive use. Across both private submissions and public awards, higher LLM involvement is consistently associated with lower semantic distinctiveness, positioning projects closer to recently funded work within the same agency. The consequences of this shift are agency-dependent. LLM use is positively associated with proposal success and higher early-stage publication output at NIH, whereas no comparable associations are observed at NSF. Notably, the productivity gains at NIH are concentrated in non-hit papers rather than the most highly cited work. Together, these findings provide large-scale evidence that the rise of LLMs is reshaping how scientific ideas are positioned, selected, and translated into publicly funded research, with implications for portfolio governance, research diversity, and the long-run impact of science.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。