Demographic Background Prompting Does Not Affect Linguistic Features on LLM-Generated News Texts
Year: 2024
Type: conference-paper
Abstract: We explored if implicit demographic information in prompts for large language models (LLMs) influences the linguistic features of generated text. Two LLMs were prompted to write news articles based on a title and summary, with prompts including demographic details like age, income, or nationality. The models were instructed not to explicitly reference these details. A total of 28,080 articles were generated by varying the demographics and topics. We calculated various linguistic metrics (e.g., sentence length, type-token ratio) and performed ANOVA, treating linguistic metrics as dependent variables and demographic categories as independent variables. Results indicate that demographic attributes do not significantly impact the linguistic metrics.
Institution: Universidade da Coruña
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4407415413