大语言模型时代语言多样性的不断缩小
The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models
AI总结：
该研究通过三项针对超88万篇文本的研究，发现广泛使用大语言模型作为写作助手会降低语言多样性，使写作风格同质化、复杂性方差下降21%-50%，对多领域存在潜在影响。
摘要：
语言远不止是一种交流工具；它编码了关于一个人的身份、心理状态和社会背景的丰富信息，为心理学、市场营销和医疗保健等不同领域提供了有价值的见解。在三项研究中，涵盖了七个不同领域的数据集以及超过880,000篇文本，我们发现，将大语言模型（LLMs）广泛用作写作助手与语言多样性的下降相关，干扰了语言所提供的社会和心理见解。尽管LLMs在润色和重写文本时保留了核心内容，但它们也会使写作风格同质化，在不同数据集和模型中将写作复杂性方差降低了具有统计显著性的21%至50%（p ≤.05），并放大与主导特征相关的模式，同时抑制其他模式，强调一致性而非个性。这些趋势在不同的LLMs、提示词和情境中均存在，对诊断流程、个性化工作、招聘评估和文化保护具有潜在影响。
英文摘要：
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.
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。
