CALM：具有文化自感知能力的语言模型
CALM: Culturally Self-Aware Language Models
AI总结：
CALM通过对比学习和专家混合机制，赋予语言模型文化自感知能力，提升其在跨文化任务中的表现。
摘要：
语言模型中的文化意识是指理解并适应多样化文化背景的能力。然而，大多数现有方法将文化视为静态背景知识，忽略了其动态和演变的本质。这一局限性降低了其在需要真实文化敏感性的下游任务中的可靠性。在本工作中，我们引入了CALM，一种新的框架，旨在赋予语言模型文化自感知能力。CALM将任务语义与显式文化概念和潜在文化信号解耦，并通过对比学习将它们塑造成结构化的文化聚类。这些聚类通过交叉注意力对齐，建立相关文化特征之间的细粒度交互，并通过专家混合机制沿文化特定维度进行适应性整合。最终的统一表示与模型的原始知识融合，构建出文化扎根的内部身份状态，进一步通过自我提示的反思学习进行增强，实现持续适应和自我修正。在多个跨文化基准数据集上进行的广泛实验表明，CALM在多个跨文化基准数据集上进行的广泛实验表明，CALM始终优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
