dlvr.it
The homogenizing effect of large language models on human expression and thought
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. We synthesize evidence across linguistics, psychology, cognitive science,
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.