The integration of new technology with cultural studies enhances our understanding of cultural heritage but often struggles to connect with diverse audiences. It is challenging to align personal interpretations with the intended meanings across different cultures. Our study investigates the important factors in appreciating art from a cross-cultural perspective. We explore the application of Large Language Models (LLMs) to bridge the cultural and language barriers in understanding Traditional Chinese Paintings (TCPs). We present CultiVerse, a visual analytics system that utilizes LLMs within a mixed-initiative framework, enhancing interpretative appreciation of TCP in a cross-cultural dialogue. CultiVerse addresses the challenge of translating the nuanced symbolism in art, which involves interpreting complex cultural contexts, aligning cross-cultural symbols, and validating cultural acceptance. CultiVerse integrates an interactive interface with the analytical capability of LLMs to explore a curated TCP dataset, facilitating the analysis of multifaceted symbolic meanings and the exploration of cross-cultural serendipitous discoveries. Empirical evaluations affirm that CultiVerse significantly improves cross-cultural understanding, offering deeper insights and engaging art appreciation.

翻译：新技术与文化研究的融合增强了我们对文化遗产的理解，但往往难以与不同受众建立联系。将个人解读与不同文化背景中的原意对齐颇具挑战性。本研究从跨文化视角探讨艺术鉴赏的重要因素，探索利用大语言模型（LLMs）突破理解中国传统绘画（TCPs）的文化与语言障碍。我们提出CultiVerse——一个基于混合主动框架的可视化分析系统，该系统利用LLMs在跨文化对话中增强对TCP的阐释性鉴赏。CultiVerse解决了艺术中微妙象征意义转译的难题，这涉及解读复杂文化语境、对齐跨文化符号以及验证文化接受度。该系统将交互式界面与LLM的分析能力相结合，用于探索经过精心策划的TCP数据集，从而促进多层面象征意义的分析及跨文化偶然发现的探索。实证评估证实，CultiVerse能显著提升跨文化理解，为艺术鉴赏提供更深刻的洞察与沉浸式体验。