@inproceedings{adilazuarda-etal-2025-surveys,
    title = "From Surveys to Narratives: Rethinking Cultural Value Adaptation in {LLM}s",
    author = "Adilazuarda, Farid  and
      Liu, Chen Cecilia  and
      Gurevych, Iryna  and
      Aji, Alham Fikri",
    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.912/",
    pages = "18063--18090",
    ISBN = "979-8-89176-332-6",
    abstract = "Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and data limitations. Previous work aligns LLMs with different cultures using survey data, primarily from the World Values Survey (WVS). However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for tasks like offensiveness classification. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To address these issues, we propose augmenting WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. Our experiments across multiple cultures show that this approach captures more enhances differentiated cultural values and improves downstream classification performances."
}