发表机构
Artificial Intelligence Research Institute for Science and Engineering, Clemson University（克莱姆森大学科学与工程人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究分析221,425份来自NSF、NIH和UKRI的获资助拨款摘要，发现ChatGPT发布后LLM相关词汇频率显著上升，表明AI写作工具已渗透至成功获得公共资金的科研提案中。
AI 中文摘要
2022年11月ChatGPT的发布，为各科学领域研究人员的日常工作引入了一种具有空前流畅性的写作工具。先前的研究通过记录大型语言模型（LLM）倾向于过度生成的单词和短语频率的上移，来衡量期刊摘要和同行评审中的后续影响。然而，一个较少被审视的问题是，这种变化是否延伸到拨款提案——研究人员通过这些文件竞争公共资金——尤其是延伸到那些成功的提案子集。这一区分之所以重要，是因为获资助的拨款代表分配公共研究资源的决策，并且因为提案人群涵盖了国家支持研究的每一个科学学科。本简报基于对来自两个国家三个主要科学资助机构的221,425份获资助拨款摘要的分析：美国国家科学基金会（NSF）、美国国立卫生研究院（NIH）和英国研究与创新署（UKRI）。时间窗口覆盖2017年底至2026年中，在每个语料库中提供了大约5年的ChatGPT前基线和3.5年的发布后观察期。
英文摘要
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.
