@inproceedings{kamruzzaman-kim-2025-exploring,
    title = "Exploring Changes in Nation Perception with Nationality-Assigned Personas in {LLM}s",
    author = "Kamruzzaman, Mahammed  and
      Kim, Gene Louis",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.181/",
    pages = "3660--3678",
    ISBN = "979-8-89176-332-6",
    abstract = "Persona assignment has become a common strategy for customizing LLM use to particular tasks and contexts. In this study, we explore how evaluation of different nations changes when LLMs are assigned specific nationality personas. We assign 193 different nationality personas (e.g., an American person) to five LLMs and examine how the LLM evaluations (or *{``}perceptions''*) of countries change. We find that all LLM-persona combinations tend to favor Western European nations, though nation-personas push LLM behaviors to focus more on and treat the nation-persona{'}s own region more favorably. Eastern European, Latin American, and African nations are treated more negatively by different nationality personas. We additionally find that evaluations by nation-persona LLMs of other nations correlate with human survey responses but fail to match the values closely. Our study provides insight into how biases and stereotypes are realized within LLMs when adopting different national personas. Our findings underscore the critical need for developing mechanisms to ensure that LLM outputs promote fairness and avoid over-generalization."
}