@inproceedings{banerjee-etal-2025-navigating,
    title = "Navigating the Cultural Kaleidoscope: A Hitchhiker{'}s Guide to Sensitivity in Large Language Models",
    author = "Banerjee, Somnath  and
      Layek, Sayan  and
      Shrawgi, Hari  and
      Mandal, Rajarshi  and
      Halder, Avik  and
      Kumar, Shanu  and
      Basu, Sagnik  and
      Agrawal, Parag  and
      Hazra, Rima  and
      Mukherjee, Animesh",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.388/",
    pages = "7580--7617",
    ISBN = "979-8-89176-189-6",
    abstract = "Cultural harm stems in LLMs whereby these models fail to align with specific cultural norms, resulting in misrepresentations or violations of cultural values. This work addresses the challenges of ensuring cultural sensitivity in LLMs, especially in small-parameter models that often lack the extensive training data needed to capture global cultural nuances. We present two key contributions: (1) A cultural harm test dataset, created to assess model outputs across different cultural contexts through scenarios that expose potential cultural insensitivities, and (2) A culturally aligned preference dataset, aimed at restoring cultural sensitivity through fine-tuning based on feedback from diverse annotators. These datasets facilitate the evaluation and enhancement of LLMs, ensuring their ethical and safe deployment across different cultural landscapes. Our results show that integrating culturally aligned feedback leads to a marked improvement in model behavior, significantly reducing the likelihood of generating culturally insensitive or harmful content."
}