The use of large language models (LLM) has increased dramatically in US research funding proposals since 2023 and has led to less original and more generic ideas that often replicate prior projects, new analysis finds.
A paper published in Proceedings of the National Academy of Sciences (PNAS) on 11 August examined 5,700 confidential grant proposal submissions and 131,000 publicly released awards for grants from the US National Science Foundation (NSF) and National Institutes of Health (NIH) between 2021 and 2025.
Using textual analysis, the authors, from the Center for Science of Science and Innovation at the Kellogg School of Management, Northwestern University, found that LLM use rises sharply from 2023 across both the point of submission and among funded awards that have passed peer review.
The authors found that grant proposals with high LLM involvement were “less semantically distinctive” from projects that had been recently funded at both agencies.
However, the implications of LLM use in funding proposals differed at the NIH and NSF. At the NIH, research proposals that involved the use of LLMs were more likely to be funded and to result in more early-stage publications. By contrast, no such associations were observed at the NSF.
