  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.
