Cultural Learning-Based Culture Adaptation of Language Models

Chen Cecilia Liu, Anna Korhonen, Iryna Gurevych

Abstract:

Adapting large language models (LLMs) to diverse cultural values is a challenging task, as
existing LLMs often reflect the values of specific groups by default, and potentially causing harm to others. In this paper, we present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural
learning. The framework leverages simulated social interactions to generate conversations
in which LLMs engage in role-playing within culturally adapted social scenarios, capturing
implicit cultural norms for model fine-tuning. CLCA improves cultural value alignment across various model architectures measured using
World Value Survey data, demonstrating the effectiveness of our proposed approach. Our results provide early evidence that understanding intent and social interactions can enhance cultural value adaptation in LLMs, highlighting the promise of training approaches based on cultural learning.