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.

Many recent studies have investigated social biases in LLMs but brand bias has received little attention. This research examines the biases exhibited by LLMs towards different brands, a significant concern given the widespread use of LLMs in affected use cases such as product recommendation and market analysis. Biased models may perpetuate societal inequalities, unfairly favoring established global brands while marginalizing local ones. Using a curated dataset across four brand categories, we probe the behavior of LLMs in this space. We find a consistent pattern of bias in this space—both in terms of disproportionately associating global brands with positive attributes and disproportionately recommending luxury gifts for individuals in high-income countries. We also find LLMs are subject to country-of-origin effects which may boost local brand preference in LLM outputs in specific contexts.