Mapping the Increasing Use of LLMs in Scientific Papers
Weixin Liang Yaohui Zhang Zhengxuan Wu Haley Lepp Wenlong Ji Xuandong Zhao Hancheng Cao Sheng Liu Siyu He Zhi Huang Diyi Yang Christopher Potts Christopher D Manning James Y. Zou
Scientific publishing lays the foundation of science by disseminating
research findings, fostering collaboration, encouraging reproducibility, and
ensuring that scientific knowledge is accessible, verifiable, and built upon
over time. Recently, there has been immense speculation about how many people
are using large language models (LLMs) like ChatGPT in their academic writing,
and to what extent this tool might have an effect on global scientific
practices. However, we lack a precise measure of the proportion of academic
writing substantially modified or produced by LLMs. To address this gap, we
conduct the first systematic, large-scale analysis across 950,965 papers
published between January 2020 and February 2024 on the arXiv, bioRxiv, and
Nature portfolio journals, using a population-level statistical framework to
measure the prevalence of LLM-modified content over time. Our statistical
estimation operates on the corpus level and is more robust than inference on
individual instances. Our findings reveal a steady increase in LLM usage, with
the largest and fastest growth observed in Computer Science papers (up to
17.5%). In comparison, Mathematics papers and the Nature portfolio showed the
least LLM modification (up to 6.3%). Moreover, at an aggregate level, our
analysis reveals that higher levels of LLM-modification are associated with
papers whose first authors post preprints more frequently, papers in more
crowded research areas, and papers of shorter lengths. Our findings suggests
that LLMs are being broadly used in scientific writings.