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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.00435 |
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| _version_ | 1866914778981597184 |
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| author | Zhang, Wei Kam-Kwai, Wong Xu, Biying Ren, Yiwen Li, Yuhuai Zhu, Minfeng Feng, Yingchaojie Chen, Wei |
| author_facet | Zhang, Wei Kam-Kwai, Wong Xu, Biying Ren, Yiwen Li, Yuhuai Zhu, Minfeng Feng, Yingchaojie Chen, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00435 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CultiVerse: Towards Cross-Cultural Understanding for Paintings with Large Language Model Zhang, Wei Kam-Kwai, Wong Xu, Biying Ren, Yiwen Li, Yuhuai Zhu, Minfeng Feng, Yingchaojie Chen, Wei Human-Computer Interaction 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. |
| title | CultiVerse: Towards Cross-Cultural Understanding for Paintings with Large Language Model |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.00435 |