20206-08-12 ノースウェスタン大学
＜関連情報＞
- https://news.northwestern.edu/stories/2026/08-2/chatbots-are-changing-who-wins-research-grants-study-finds
- https://www.pnas.org/doi/10.1073/pnas.2601439123
大規模言語モデルの台頭と、米国連邦政府の研究資金の方向性および影響 The rise of large language models and the direction and impact of US federal research funding
Yifan Qian, Zhe Wen, Alexander C. Furnas, +2 , and Dashun Wang
Proceedings of the National Academy of Sciences Published:August 11, 2026
DOI:https://doi.org/10.1073/pnas.2601439123
Abstract
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 NSF and 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 nonhit 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.
