This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Large language models (LLMs) are making writing styles more similar without changing the overall meaning, according to an analysis of more than 880,000 texts across different writing types published in Nature Human Behaviour. The findings indicate that using LLMs could make it more difficult to identify important clues about aspects of a person's identity, personality and mental health from their language.
People express similar ideas in different ways, and these differences can provide information about them, such as their social background. Researchers use language patterns to study individuals and societies, but LLMs could reduce this variation by favoring common language patterns. Zhivar Sourati and colleagues analyzed more than 880,000 texts, including Reddit stories, news articles, academic papers, essays, social media posts and political speeches.
They also asked GPT-3.5, Llama 3 70B and Gemini Pro to rewrite thousands of human-written texts. LLM rewriting was found to reduce variation in writing complexity by 21% to 50%. In 87% of cases, the original and rewritten texts had meaning-similarity scores above 0.95, indicating that the LLMs largely maintained the original meaning.
After rewriting, computer models used to analyze the LLM texts were an average of six percentage points less accurate at identifying authors' personal characteristics from the writing. The LLMs weakened some language patterns linked to these traits, including associations between pronoun use and extraversion, friend-related words and loyalty, and future-focused words and age. However, other associations remained, including those between negative-emotion words and neuroticism, religion-related words and purity, and social words and gender.
The findings suggest that LLM-assisted writing could make language-based assessments less reliable in areas including psychology, mental health care, recruitment and personalized services. Further research is needed to understand why LLMs preserve some personal language markers but weaken others. Zhivar Sourati et al, The shrinking landscape of linguistic diversity in the age of large language models, Nature Human Behaviour (2026).
DOI: 10.1038/s41562-026-02550-0 Journal information: Nature Human Behaviour BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries. Full profile → Bachelor's in mathematical biology, Master's in creative writing.
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