@inproceedings{saffari-etal-2025-introduce,
    title = "Can {I} Introduce My Boyfriend to My Grandmother? Evaluating Large Language Models Capabilities on {I}ranian Social Norm Classification",
    author = "Saffari, Hamidreza  and
      Shafiei, Mohammadamin  and
      Rooein, Donya  and
      Pierri, Francesco  and
      Nozza, Debora",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.337/",
    pages = "6060--6074",
    ISBN = "979-8-89176-195-7",
    abstract = "Creating globally inclusive AI systems demands datasets reflecting diverse social norms. Iran, with its unique cultural blend, offers an ideal case study, with Farsi adding linguistic complexity. In this work, we introduce the Iranian Social Norms (ISN) dataset, a novel collection of 1,699 Iranian social norms, including environments, demographic features, and scope annotation, alongside English translations. Our evaluation of 6 Large Language Models (LLMs) in classifying Iranian social norms, using a variety of prompts, uncovered critical insights into the impact of geographic and linguistic context. Results revealed a substantial performance gap in LLMs' comprehension of Iranian norms. Notably, while the geographic context in English prompts enhanced the performance, this effect was absent in Farsi, pointing to nuanced linguistic challenges. Particularly, performance was significantly worse for Iran-specific norms, emphasizing the importance of culturally tailored datasets. As the first Farsi dataset for social norm classification, ISN will facilitate crucial cross-cultural analyses, shedding light on how values differ across contexts and cultures."
}