Large language models (LLMs) show promise in offering emotional support and generating empathetic responses for individuals in distress, but their ability to deliver culturally sensitive support remains underexplored due to a lack of resources. In this work, we introduce CultureCare, the first dataset designed for this task, spanning four cultures and including 1729 distress messages, 1523 cultural signals, and 1041 support strategies with fine-grained emotional and cultural annotations. Leveraging CultureCare, we (i) develop and test four adaptation strategies for guiding three state-of-the-art LLMs toward culturally sensitive responses; (ii) conduct comprehensive evaluations using LLM-as-a-Judge, in-culture human annotators, and clinical psychologists; (iii) show that adapted LLMs outperform anonymous online peer responses, and that simple cultural role-play is insufficient for cultural sensitivity; and (iv) explore the application of LLMs in clinical training, where experts highlight their potential in fostering cultural competence in novice therapists.

翻译：大型语言模型（LLM）在向处于困境中的个体提供情感支持与生成共情回应方面展现出潜力，但由于资源匮乏，其提供文化敏感性支持的能力尚未得到充分探索。本研究推出了首个针对此任务设计的CultureCare数据集，涵盖四种文化背景，包含1729条困境消息、1523个文化信号及1041条支持策略，并附有细粒度的情感与文化标注。基于CultureCare数据集，我们（i）开发并测试了四种适配策略，用于引导三种前沿LLM生成具有文化敏感性的回应；（ii）通过LLM即评判者、文化内部人工标注者及临床心理学家的多维度评估体系进行全面评测；（iii）证明适配后的LLM表现优于匿名在线同伴回应，且简单的文化角色扮演不足以实现文化敏感性；（iv）探索了LLM在临床培训中的应用，专家指出其在培养新手治疗师文化胜任力方面具有潜力。