  Recent large language models (LLMs) can generate and revise text with
human-level performance, and have been widely commercialized in systems like
ChatGPT. These models come with clear limitations: they can produce inaccurate
information, reinforce existing biases, and be easily misused. Yet, many
scientists have been using them to assist their scholarly writing. How
wide-spread is LLM usage in the academic literature currently? To answer this
question, we use an unbiased, large-scale approach, free from any assumptions
on academic LLM usage. We study vocabulary changes in 14 million PubMed
abstracts from 2010-2024, and show how the appearance of LLMs led to an abrupt
increase in the frequency of certain style words. Our analysis based on excess
words usage suggests that at least 10% of 2024 abstracts were processed with
LLMs. This lower bound differed across disciplines, countries, and journals,
and was as high as 30% for some PubMed sub-corpora. We show that the appearance
of LLM-based writing assistants has had an unprecedented impact in the
scientific literature, surpassing the effect of major world events such as the
Covid pandemic.
