‘A Woman is More Culturally Knowledgeable than A Man?’: The Effect of Personas on Cultural Norm Interpretation in LLMs

Mahammed Kamruzzaman, Hieu Minh Nguyen, Nazmul Hassan, Gene Louis Kim

Abstract:

As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is by assigning a persona---a role that describes the expected behavior of the LLM (e.g., a man, a woman, an engineer). This study examines whether an LLM’s interpretation of social norms varies based on assigned personas and whether these variations stem from embedded biases within the models. In our research, we tested 34 distinct personas
from 12 categories (e.g., age, gender, beauty) across four different LLMs. We find that LLMs' cultural norm interpretation varies based on the persona used and that the variations within a persona category (e.g., a fat person and a thin person as in physical appearance group) follow a trend where an LLM with the more socially desirable persona (e.g., a thin person) interprets social norms more accurately than with the less socially desirable persona (e.g., a fat person). While persona-based conditioning can enhance model adaptability, it also risks reinforcing stereotypes rather than providing an unbiased representation of cultural norms. We also discuss how different types of social biases due to stereotypical assumptions of LLMs may contribute to the results that we observe.