CALM: Culturally Self-Aware Language Models
Lingzhi Shen, Xiaohao Cai, Yunfei Long, Imran Razzak, Guanming Chen, Shoaib Jameel
Cultural awareness in language models refers to the ability to understand norms, values, and perspectives embedded in diverse cultural contexts. However, existing approaches often treat culture as static background knowledge, failing to capture the evolving nature of cultural context, which limits their reliability in dynamic downstream tasks that require cultural sensitivity. In this work, we introduce CALM, a novel framework designed to endow language models with cultural self-awareness. CALM simultaneously extracts explicit cultural concepts and latent cultural signals beyond task semantics, and structures them through contrastive learning to induce culturally coherent representations. These features are then aligned via cross-attention and routed through a dimension-specific Mixture-of-Experts mechanism, resulting in a unified representation that is fused with the model’s internal knowledge to form a culturally grounded identity profile. To enable continual cultural adaptation, CALM incorporates self-prompted reflective learning, allowing the model to adaptively self-correct its cultural understanding across contexts. Experiments on the benchmark datasets demonstrate that CALM outperforms state-of-the-art methods.