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. Extensive experiments conducted on multiple cross-cultural benchmark datasets demonstrate that CALM consistently outperforms state-of-the-art methods.

翻译：语言模型的文化意识是指理解并适应多样化文化背景的能力。然而，现有方法大多将文化视为静态的背景知识，忽视了其动态演变的本质。这一局限降低了模型在需要真正文化敏感性的下游任务中的可靠性。本文提出CALM，一种旨在赋予语言模型文化自我意识的新颖框架。CALM通过对比学习将任务语义与显式文化概念及潜在文化信号解耦，并将其塑造成结构化的文化簇。这些文化簇随后通过交叉注意力进行对齐，以建立相关文化特征间的细粒度交互，并经由专家混合机制沿特定文化维度进行自适应整合。生成的统一表示与模型原有知识融合，构建出基于文化的内部身份状态，该状态通过自我提示的反思学习进一步增强，从而实现持续适应与自我修正。在多个跨文化基准数据集上进行的大量实验表明，CALM始终优于现有最先进方法。