The use of Large Language Models (LLMs) like ChatGPT and DeepSeek for translation and language polishing is a welcome development, reducing the longstanding publishing barrier to non-English speakers. Assessing the uptake of this facility is useful to give insights into changing nature of scientific writing. Although the prevalence of LLM-associated terms has been tracked across science in abstracts and for full text biomedical research, their science-wide prevalence in full texts is unknown. In response, this article investigates an expanded set of 80 potentially LLM-associated terms during 2021-2025 in a science-wide full text collection from the publisher MDPI (1.25 million articles), partly focusing on the 73 journals that published at least 500 articles in 2021. The results demonstrate the increasing prevalence of LLM-associated terms science-wide in full texts to 2024, with some terms declining from 2024 to 2025 and others continuing to increase. LLMs seem to avoid some terms (e.g., thus, moreover) and a few terms have stronger associations with abstracts than full texts (e.g., enhanced) or the opposite (e.g., leveraged). The term family "underscore" had the biggest increase: up to 29-fold. There are substantial differences between journals in the apparent use of LLMs for writing, from lower uptake in the life sciences to higher uptake in social sciences, electronic engineering and environmental science. Fields in which there is currently low uptake may need improved or specialist support, such as for reliably translating complex formulae, before the full benefits of automatic translation can be realised.

翻译：大型语言模型（LLMs）如ChatGPT和DeepSeek在翻译和语言润色中的应用是一项备受期待的进展，减少了非英语母语者长期面临的出版障碍。评估这一工具的使用情况有助于深入了解科学写作性质的变化。尽管LLM相关术语在摘要和生物医学全文本中的普及程度已被追踪，但其在全科学领域全文中的普及程度尚不清楚。为此，本文调查了出版商MDPI（125万篇文章）在2021-2025年间科学全文集中80个可能LLM相关术语的使用情况，并部分聚焦于2021年发表至少500篇文章的73种期刊。结果表明，截至2024年，LLM相关术语在全科学全文中的普及程度持续上升，但部分术语从2024到2025年出现下降，而其他术语继续增加。LLMs似乎避免使用某些术语（例如“thus”“moreover”），而少数术语在摘要中的关联性更强（如“enhanced”），或反之（如“leveraged”）。术语家族“underscore”增幅最大，高达29倍。不同期刊在写作中使用LLMs的明显程度存在显著差异，从生命科学中的较低采纳率到社会科学、电子工程和环境科学中的较高采纳率。当前采纳率较低的领域可能需要改进或专业支持，例如可靠翻译复杂公式的能力，才能实现自动翻译的全部益处。