CALM: Culturally Self-Aware Language Models

Lingzhi Shen (University of Southampton) · Xiaohao Cai (University of Southampton) · Yunfei Long (Queen Mary, University of London) · Imran Razzak (Mohamed bin Zayed University of Artificial Intelligence) · Guanming Chen (University of Southampton) · Shoaib Jameel (University of Southampton)

calm frameworkcontinual adaptation contrastive learning cross-attentioncultural awarenesscultural clusterscultural conceptscultural sensitivitydynamic contextsinternal identity statelanguage models mixture-of-expertsself-prompted learningtask semanticsunified representation

Cultural awareness in language models is the capacity to understand and adapt to diverse cultural contexts. However, most existing approaches treat culture as static background knowledge, overlooking its dynamic and evolving nature. This limitation reduces their reliability in downstream tasks that demand genuine cultural sensitivity. In this work, we introduce CALM, a novel framework designed to endow language models with cultural self-awareness. CALM disentangles task semantics from explicit cultural concepts and latent cultural signals, shaping them into structured cultural clusters through contrastive learning. These clusters are then aligned via cross-attention to establish fine-grained interactions among related cultural features and are adaptively integrated through a Mixture-of-Experts mechanism along culture-specific dimensions. The resulting unified representation is fused with the model's original knowledge to construct a culturally grounded internal identity state, which is further enhanced through self-prompted reflective learning, enabling continual adaptation and self-correction. Experiments on the benchmark datasets demonstrate that CALM outperforms state-of-the-art methods.