Have LLM-associated terms increased in article full texts in all fields?

TL;DR

This study analyzes the increasing use of LLM-associated terms in full texts across various scientific fields from 2021 to 2025, revealing trends, field differences, and implications for scientific writing and translation support.

Contribution

It provides the first comprehensive analysis of science-wide LLM-associated term prevalence in full texts over multiple years across diverse disciplines.

Findings

LLM-associated terms increased in full texts until 2024, then some declined.

Significant variation in LLM term usage across scientific fields.

The term 'underscore' increased up to 29-fold, indicating rapid adoption.

Abstract

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.