LLM Signals in Funded Grants: Evidence from International Funding Agencies
TL;DR
This brief report draws on an analysis of 221,425 funded grant abstracts from three major science-funding agencies in two countries: the U.S. National Science Foundation (NSF), the U.S. National Institutes of Health (NIH), and UK Research and Innovation (UKRI).
Abstract
The release of ChatGPT in November 2022 introduced a writing tool of unprecedented fluency into the daily routines of researchers across the sciences. Prior work has measured what follows in journal abstracts and in peer reviews by documenting an upward shift in the frequency of words and short phrases that large language models (LLMs) tend to overproduce. However, a much less-examined question concerns whether the same shift extends to grant proposals, the documents through which researchers compete for public funding, and, in particular, to the subset of proposals that succeed. Such a distinction is important to note because funded grants represent decisions to allocate public research resources, and because the proposing population spans every scientific discipline in which a country supports research. This brief report draws on an analysis of 221,425 funded grant abstracts from three major science-funding agencies in two countries: the U.S. National Science Foundation (NSF), the U.S. National Institutes of Health (NIH), and UK Research and Innovation (UKRI). The window covers late 2017 through mid-2026, providing roughly 5 years of pre-ChatGPT baseline and 3.5 years of post-release observation in each corpus.