The Homogenizing Effect of Large Language Models on Human Expression and Thought
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arXiv
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| Format: | Preprint |
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2025
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| author | Sourati, Zhivar Ziabari, Alireza S. Dehghani, Morteza |
| author_facet | Sourati, Zhivar Ziabari, Alireza S. Dehghani, Morteza |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_01491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Homogenizing Effect of Large Language Models on Human Expression and Thought Sourati, Zhivar Ziabari, Alireza S. Dehghani, Morteza Computation and Language 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. |
| title | The Homogenizing Effect of Large Language Models on Human Expression and Thought |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.01491 |