The Homogenizing Effect of Large Language Models on Human Expression and Thought

TL;DR

Large language models tend to homogenize human expression and thought by reinforcing dominant styles, which may threaten cognitive diversity and collective intelligence.

Contribution

This paper synthesizes interdisciplinary evidence to highlight how LLMs reinforce linguistic and reasoning homogenization, a novel perspective on their societal impact.

Findings

LLMs reflect and reinforce dominant language styles.

Widespread use of LLMs amplifies convergence in thought.

Homogenization risks reducing cognitive diversity.

Abstract

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.