  One way to personalize and steer generations from large language models (LLM)
is to assign a persona: a role that describes how the user expects the LLM to
behave (e.g., a helpful assistant, a teacher, a woman). This paper investigates
how personas affect diverse aspects of model behavior. We assign to seven LLMs
162 personas from 12 categories spanning variables like gender, sexual
orientation, and occupation. We prompt them to answer questions from five
datasets covering objective (e.g., questions about math and history) and
subjective tasks (e.g., questions about beliefs and values). We also compare
persona's generations to two baseline settings: a control persona setting with
30 paraphrases of "a helpful assistant" to control for models' prompt
sensitivity, and an empty persona setting where no persona is assigned. We find
that for all models and datasets, personas show greater variability than the
control setting and that some measures of persona behavior generalize across
models.
