Prior research has established associations between individuals' language usage and their personal traits; our linguistic patterns reveal information about our personalities, emotional states, and beliefs. However, with the increasing adoption of Large Language Models (LLMs) as writing assistants in everyday writing, a critical question emerges: are authors' linguistic patterns still predictive of their personal traits when LLMs are involved in the writing process? We investigate the impact of LLMs on the linguistic markers of demographic and psychological traits, specifically examining three LLMs - GPT3.5, Llama 2, and Gemini - across six different traits: gender, age, political affiliation, personality, empathy, and morality. Our findings indicate that although the use of LLMs slightly reduces the predictive power of linguistic patterns over authors' personal traits, the significant changes are infrequent, and the use of LLMs does not fully diminish the predictive power of authors' linguistic patterns over their personal traits. We also note that some theoretically established lexical-based linguistic markers lose their reliability as predictors when LLMs are used in the writing process. Our findings have important implications for the study of linguistic markers of personal traits in the age of LLMs.

翻译：先前研究已确立个体语言使用与其个人特质之间的关联；我们的语言模式能够揭示关于个性、情绪状态和信念的信息。然而，随着大语言模型（LLMs）在日常写作中作为写作助手的日益普及，一个关键问题浮现：当LLMs参与写作过程时，作者的语言模式是否仍能预测其个人特质？我们研究了LLMs对人口统计和心理特征语言标记的影响，具体考察了三种LLMs——GPT3.5、Llama 2和Gemini——在六种不同特质（性别、年龄、政治倾向、人格、共情和道德）上的表现。研究结果表明，尽管使用LLMs略微降低了语言模式对作者个人特质的预测能力，但显著变化并不常见，且LLMs的使用并未完全削弱作者语言模式对其个人特质的预测能力。我们还注意到，当LLMs用于写作过程时，一些理论确立的基于词汇的语言标记作为预测因子的可靠性会降低。我们的发现对LLMs时代个人特质语言标记的研究具有重要意义。