@inproceedings{pawar-etal-2025-presumed,
    title = "Presumed Cultural Identity: How Names Shape {LLM} Responses",
    author = "Pawar, Siddhesh Milind  and
      Arora, Arnav  and
      Kaffee, Lucie-Aim{\'e}e  and
      Augenstein, Isabelle",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1207/",
    doi = "10.18653/v1/2025.findings-emnlp.1207",
    pages = "22147--22172",
    ISBN = "979-8-89176-335-7",
    abstract = "Names are deeply tied to human identity - they can serve as markers of individuality, cultural heritage, and personal history. When interacting with LLMs, user names can enter chatbot conversations through direct user input (requested by chatbots), as part of task contexts such as CV reviews, or as built-in memory features that store user information for personalisation. In this work, we study name-based cultural bias by analyzing the adaptations that LLMs make when names are mentioned in the prompt. Our analyses demonstrate that LLMs exhibit significant cultural identity assumptions across multiple cultures based on users' presumed backgrounds based on their names. We also show how using names as an indicator of identity can lead to misattribution and flattening of cultural identities. Our work has implications for designing more nuanced personalisation systems that avoid reinforcing stereotypes while maintaining meaningful customisation."
}