This study investigates how Large Language Models (LLMs) are influencing the language of academic papers by tracking 12 LLM-associated terms across six major scholarly databases (Scopus, Web of Science, PubMed, PubMed Central (PMC), Dimensions, and OpenAlex) from 2015 to 2024. Using over 2.4 million PMC open-access publications (2021–July 2025), we also analysed full texts to assess changes in the frequency and co-occurrence of these terms before and after ChatGPT’s initial public release. Across databases, delve (+ 1,500%), underscore (+ 1,000%), and intricate (+ 700%) had the largest increases between 2022 and 2024. Growth in LLM-term usage was much higher in STEM fields than in social sciences and arts and humanities. In PMC full texts, the proportion of papers using underscore six or more times increased by over 10,000% from 2022 to 2025, followed by intricate (+ 5,400%) and meticulous (+ 2,800%). Nearly half of all 2024 PMC papers using any LLM term also included underscore, compared with only 3%–14% of papers before ChatGPT in 2022. Papers using one LLM term are now much more likely to include other terms. For example, in 2024, underscore strongly correlated with pivotal (0.449) and delve (0.311), compared with very weak associations in 2022 (0.032 and 0.018, respectively). These findings provide the first large-scale evidence based on full-text publications and multiple databases that some LLM-associated terms are now being used much more frequently and together in academic writing. However, the results do not provide direct causal evidence and cannot distinguish between LLM-generated text, LLM-edited text, or broader adoption of LLM-associated writing or publishing styles. The rapid uptake of LLMs to support scholarly publishing is a welcome development reducing the language barrier to academic publishing for non-English speakers.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)