Papers › Delving into ChatGPT usage in academic writing through excess vocabulary
Delving into ChatGPT usage in academic writing through excess vocabulary
Dmitry Kobak, Rita González-Márquez, Emőke-Ágnes Horvát, Jan Lause
Large language models (LLMs) like ChatGPT can generate and revise text with human-level performance. These models come with clear limitations: they can produce inaccurate information, reinforce existing biases, and be easily misused. Yet, many scientists use them for their scholarly writing. But how wide-spread is such LLM usage in the academic literature? To answer this question, we present an unbiased, large-scale approach: 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. This excess word analysis suggests that at least 10% of 2024 abstracts were processed with LLMs. This lower bound differed across disciplines, countries, and journals, reaching 30% for some sub-corpora. We show that LLMs have had an unprecedented impact on the scientific literature, surpassing the effect of major world events such as the Covid pandemic.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections