The Homogenizing Effect of Large Language Models on Human Expression and Thought
This addresses a foundational issue for society by highlighting how LLMs may homogenize human expression and thought, with incremental insights from a review of existing evidence.
The paper tackles the problem of large language models (LLMs) standardizing human language and reasoning, synthesizing evidence to show they reflect and reinforce dominant styles while marginalizing alternatives, which risks flattening cognitive diversity essential for creativity and adaptability.
Cognitive diversity, reflected in variations of language, perspective, and reasoning, is essential to creativity and collective intelligence. This diversity is rich and grounded in culture, history, and individual experience. Yet as large language models (LLMs) become deeply embedded in people's lives, they risk standardizing language and reasoning. This Review synthesizes evidence across linguistics, cognitive, and computer science to show how LLMs reflect and reinforce dominant styles while marginalizing alternative voices and reasoning strategies. We examine how their design and widespread use contribute to this effect by mirroring patterns in their training data and amplifying convergence as all people increasingly rely on the same models across contexts. Unchecked, this homogenization risks flattening the cognitive landscapes that drive collective intelligence and adaptability.