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

2025-08-02

AI Summary

This paper identifies a systemic risk of cognitive homogenization imposed by large language models (LLMs) on human cognitive diversity: LLMs—through training data biases and deployment mechanisms—reinforce dominant linguistic styles and reasoning paradigms while marginalizing non-mainstream expressions, culturally situated cognition, and alternative logical strategies, thereby undermining the foundations of collective intelligence and creativity. Methodologically, the study integrates interdisciplinary evidence from linguistics, cognitive science, and computer science to construct a “data–model–use–cognition” impact chain framework, systematically identifying three convergent pathways through which LLMs drive cognitive alignment: representational bias, interactive domestication, and institutional embedding. The contributions include (1) proposing AI design principles explicitly aimed at safeguarding cognitive diversity, and (2) exposing the structural tension between technological standardization and cultural plurality—offering both a theoretical anchor and actionable intervention points for responsible LLM development.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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. 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.

Problem

• LLMs risk standardizing human language and reasoning diversity
• LLMs marginalize alternative voices and reasoning strategies
• Unchecked homogenization flattens cognitive landscapes for adaptability

Innovation

• Analyzing LLM impact on cognitive diversity
• Identifying standardization risks in language models
• Examining training data bias effects