CultiVerse: Towards Cross-Cultural Understanding for Paintings with Large Language Model

TL;DR

CultiVerse leverages Large Language Models within an interactive visual system to enhance cross-cultural understanding and appreciation of Traditional Chinese Paintings, addressing cultural and linguistic barriers.

Contribution

This paper introduces CultiVerse, a novel visual analytics system that uses LLMs to interpret and bridge cultural differences in art appreciation, specifically for Chinese paintings.

Findings

CultiVerse significantly improves cross-cultural understanding.

The system facilitates deeper insights into cultural symbolism.

Empirical evaluations show increased engagement and interpretative depth.

Abstract

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.