An Empirical Analysis of the Writing Styles of Persona-Assigned LLMs

There are recent efforts to "personalize" large language models (LLMs) by assigning them specific personas. This paper explores the writing styles of such persona-assigned LLMs across different socio-demographic groups based on age, profession, location, and political affiliations, using three widely-used LLMs. Leveraging an existing style embedding model that produces detailed style attributes and latent Dirichlet allocation (LDA) for broad style analysis, we measure style differences using Kullback-Leibler divergence to compare LLM-generated and human-written texts. We find significant style differences among personas. This analysis emphasizes the need to consider socio-demographic factors in language modeling to accurately capture diverse writing styles used for communications. The findings also reveal the strengths and limitations of personalized LLMs, their potential uses, and the importance of addressing biases in their design. The code and data are available at: https://github.com/ ra-MANUJ-an/writing-style-persona