  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.
