大语言模型对人类表达与思维的同质化效应
The Homogenizing Effect of Large Language Models on Human Expression and Thought
AI总结：
大语言模型通过标准化语言和推理，加剧了人类认知多样性的同质化效应，影响集体智慧和适应性。
摘要：
认知多样性，体现在语言、视角和推理的差异上，对于创造力和集体智慧至关重要。这种多样性根植于文化、历史和个体经验。然而，随着大语言模型（LLMs）深入融入人们的生活，它们有风险标准化语言和推理。我们综合语言学、心理学、认知科学和计算机科学的证据，展示LLMs如何反映并强化主导风格，同时边缘化替代的声音和推理策略。我们考察其设计和广泛应用如何通过镜像训练数据中的模式以及所有人越来越依赖相同模型的上下文，加剧这种效应。若不受遏制，这种同质化将风险平抑推动集体智慧和适应性的认知景观。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
