.png){kind=icon}
Return
Quantifying large language model usage in scientific papers
.png){kind=icon}
PRE
AI
DOI：10.1038/s41562-025-02273-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Scientific publishing is the primary means of disseminating research findings. There has been speculation about how extensively large language models (LLMs) are being used in academic writing. Here we conduct a systematic analysis across 1,121,912 preprints and published papers from January 2020 to September 2024 on arXiv, bioRxiv and Nature portfolio journals, using a population-level framework based on word frequency shifts to estimate the prevalence of LLM-modified content over time. Our findings suggest a steady increase in LLM usage, with the largest and fastest growth estimated for computer science papers (up to 22%). By comparison, mathematics papers and the Nature portfolio showed lower evidence of LLM modification (up to 9%). LLM modification estimates were higher among papers from first authors who post preprints more frequently, papers in more crowded research areas and papers of shorter lengths. Our findings suggest that LLMs are being broadly used in scientific writing. Liang et al. estimate the prevalence of text modified by large language models in recent scientific papers and preprints, finding widespread use (up to 17.5% of papers in computer science).
Journal
IF:
15.9
Papers:
504
Citations:
1.7W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSaveCan Large Language Models Provide Useful Feedback on Research Papers? A Large-Scale Empirical Analysis.png)
NEJM AI
OAAI
NEJM AIIF0
OAAIShareSave
View more