From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs

ACL ARR 2025 February Submission4446 Authors

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.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: language/cultural bias analysis

Contribution Types: NLP engineering experiment, Data analysis

Languages Studied: arabic, bengali, chinese, english, deutsch, greek, korean, portuguese, spanish, turkish

Submission Number: 4446