大语言模型正在让人类写作越来越千篇一律。
The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models
- 用大模型改写文本时，写作风格趋于统一，复杂度方差下降21%-50%。
- 模型放大主流特征，压制非主流表达，削弱个体差异性。
- 影响心理诊断、招聘评估和文化传承，需警惕语言多样性流失。
语言不仅是交流工具，更承载身份、心理状态与社会背景等丰富信息，对心理学、营销、医疗等领域具有重要价值。在涵盖七个数据集、超88万条文本的三项研究中，我们发现大语言模型（LLMs）作为写作助手的广泛应用，导致语言多样性下降，干扰了语言所蕴含的社会与心理洞察。尽管核心内容得以保留，但写作风格被同质化，跨数据集与模型的写作复杂度方差显著降低21%-50%（p ≤ .05），并强化主导特征，抑制其他表达模式，强调一致性而弱化个性。该趋势在不同模型、提示词及语境下均成立，可能对诊断流程、个性化服务、招聘评估及文化保护带来深远影响。
原文摘要 · Abstract (English)
Language is far more than a communication tool; it encodes a wealth of information about a person's identity, psychological state, and social context, providing valuable insights for diverse fields including psychology, marketing, and healthcare. Across three studies spanning seven datasets in different domains and over 880,000 texts, we show that the widespread adoption of large language models (LLMs) as writing assistants is linked to declines in linguistic diversity, interfering with the societal and psychological insights language provides. While core content is retained when LLMs polish and rewrite texts, LLMs also homogenize writing styles, reducing writing-complexity variance by a statistically significant 21-50% across datasets and models (p <= .05), and amplify patterns associated with dominant characteristics while suppressing others, emphasizing conformity over individuality. These trends hold across different LLMs, prompts, and contexts, with potential implications for diagnostic processes, personalization efforts, hiring assessments, and cultural preservation.
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