秘密守护者：LLM对个人特质语言标记的影响
Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits
AI总结：
本文评估GPT3.5、Llama 2和Gemini辅助写作对六类个人特质语言标记的影响，发现LLM仅轻微降低预测力，但部分词汇标记可靠性下降。
摘要：
先前研究已经确立了个体语言使用与其个人特质之间的关联；我们的语言模式会揭示有关人格、情绪状态和信念的信息。然而，随着大型语言模型（LLM）在日常写作中越来越多地被用作写作助手，一个关键问题随之出现：当LLM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LLM对人口统计学和心理特质语言标记的影响，具体考察了三个LLM——GPT3.5、Llama 2和Gemini——涉及六种不同特质：性别、年龄、政治立场、人格、共情和道德。我们的研究结果表明，尽管使用LLM会略微降低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，而且使用LLM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，当写作过程中使用LLM时，一些在理论上已确立的、基于词汇的语言标记会失去其作为预测因子的可靠性。我们的研究结果对LLM时代的个人特质语言标记研究具有重要意义。
英文摘要：
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.
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。
