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-related terms are now being used much more frequently and together. The rapid uptake of LLMs to support scholarly publishing is a welcome development reducing the language barrier to academic publishing for non-English speakers.

翻译：本研究追踪了2015年至2024年间，六个主要学术数据库（Scopus、Web of Science、PubMed、PubMed Central (PMC)、Dimensions和OpenAlex）中12个与大型语言模型（LLM）相关的术语，以探究LLM如何影响学术论文的语言。我们利用超过240万篇PMC开放获取出版物（2021年至2025年7月），分析了ChatGPT首次公开发布前后这些术语出现频率与共现模式的变化。在各数据库中，从2022年到2024年，增长幅度最大的术语依次为delve（+1,500%）、underscore（+1,000%）和intricate（+700%）。LLM相关术语使用量的增长在STEM领域远高于社会科学、艺术与人文学科。在PMC全文数据中，使用underscore六次及以上的论文比例，从2022年到2025年增长了超过10,000%，紧随其后的是intricate（+5,400%）和meticulous（+2,800%）。在2024年PMC收录的论文中，凡使用了任一LLM术语的论文，近半数也包含underscore；相比之下，2022年ChatGPT出现前，这一比例仅为3%至14%。当下，使用一个LLM术语的论文更可能同时使用其他LLM术语。例如，2024年，underscore与pivotal（系数0.449）和delve（系数0.311）呈现强相关；而在2022年，这两组关联非常微弱（系数分别为0.032和0.018）。这些发现首次基于全文出版物与多个数据库提供了大规模证据，表明某些LLM相关术语目前正被更频繁地、且倾向于共同使用。快速采纳LLM以支持学术出版是一个可喜的发展，它有助于为非英语母语者降低学术出版的语言障碍。