Recent advances in Large Language Models (LLMs) have expanded their role in human interaction, yet curiosity -- a central driver of inquiry -- remains underexplored in these systems, particularly across cultural contexts. In this work, we investigate cultural variation in curiosity using Yahoo! Answers, a real-world multi-country dataset spanning diverse topics. We introduce CUEST (CUriosity Evaluation across SocieTies), an evaluation framework that measures human-model alignment in curiosity through linguistic (style), topic preference (content) analysis and grounding insights in social science constructs. Across open- and closed-source models, we find that LLMs flatten cross-cultural diversity, aligning more closely with how curiosity is expressed in Western countries. We then explore fine-tuning strategies to induce curiosity in LLMs, narrowing the human-model alignment gap by up to 50%. Finally, we demonstrate the practical value of curiosity for LLM adaptability across cultures, showing its importance for future NLP research.

翻译：近年来，大型语言模型（LLMs）在人类交互中的作用日益扩大，然而好奇心——作为探索的核心驱动力——在这些系统中，尤其是跨文化语境下，仍未得到充分研究。本研究利用雅虎问答这一涵盖多国、多主题的真实世界数据集，探究好奇心的文化差异。我们提出了CUEST（跨社会好奇心评估框架），该框架通过语言风格分析、主题偏好内容分析，并将洞察置于社会科学理论建构中进行验证，以衡量人类与模型在好奇心方面的一致性。在开源和闭源模型中，我们发现LLMs削弱了跨文化多样性，其表现更接近西方国家好奇心的表达方式。随后，我们探索了通过微调策略在LLMs中激发好奇心，将人类与模型的一致性差距缩小了高达50%。最后，我们证明了好奇心对于LLMs跨文化适应性的实用价值，并指出其对未来自然语言处理研究的重要性。