Art appreciation based on graph retrieval augmented generation and few-shot learning
With the continuous advancement of quality education in our country, the influence of aesthetic education in subject education is becoming increasingly important. Appreciation of artworks is one of the important contents of aesthetic education, which can cultivate students' artistic ability and...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2025-01-01
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| Series: | 大数据 |
| Subjects: | |
| Online Access: | http://www.j-bigdataresearch.com.cn/zh/article/111999100/ |
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| _version_ | 1849433492224475136 |
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| author | LIU Tianyang KOU Sijia JIN Xu WANG Wenjing LU Xuesong |
| author_facet | LIU Tianyang KOU Sijia JIN Xu WANG Wenjing LU Xuesong |
| author_sort | LIU Tianyang |
| collection | DOAJ |
| description | With the continuous advancement of quality education in our country, the influence of aesthetic education in subject education is becoming increasingly important. Appreciation of artworks is one of the important contents of aesthetic education, which can cultivate students' artistic ability and literacy. However, the lack of excellent art teachers and the imbalance of the development level of art education in various regions has led to many students being unable to receive high-quality art appreciation education. In this case, using multimodal large language models to tutor students in art appreciation has become a potential alternative. Using a multimodal large language model, this paper proposes a method based on graph retrieval augmented generation and few-shot learning to guide the model to generate art appreciation content that meets the needs of high school education. Experimental results show that compared with the comparison methods, this method can effectively improve the quality of the appreciation content generated by the multimodal large language model. |
| format | Article |
| id | doaj-art-4ffba66255de40b98d3e77f9fcaed3bb |
| institution | Kabale University |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-4ffba66255de40b98d3e77f9fcaed3bb2025-08-20T03:27:01ZzhoChina InfoCom Media Group大数据2096-02712025-01-01116111999100Art appreciation based on graph retrieval augmented generation and few-shot learningLIU TianyangKOU SijiaJIN XuWANG WenjingLU XuesongWith the continuous advancement of quality education in our country, the influence of aesthetic education in subject education is becoming increasingly important. Appreciation of artworks is one of the important contents of aesthetic education, which can cultivate students' artistic ability and literacy. However, the lack of excellent art teachers and the imbalance of the development level of art education in various regions has led to many students being unable to receive high-quality art appreciation education. In this case, using multimodal large language models to tutor students in art appreciation has become a potential alternative. Using a multimodal large language model, this paper proposes a method based on graph retrieval augmented generation and few-shot learning to guide the model to generate art appreciation content that meets the needs of high school education. Experimental results show that compared with the comparison methods, this method can effectively improve the quality of the appreciation content generated by the multimodal large language model.http://www.j-bigdataresearch.com.cn/zh/article/111999100/graph retrieval augmented generationart educationmultimodal large language model |
| spellingShingle | LIU Tianyang KOU Sijia JIN Xu WANG Wenjing LU Xuesong Art appreciation based on graph retrieval augmented generation and few-shot learning 大数据 graph retrieval augmented generation art education multimodal large language model |
| title | Art appreciation based on graph retrieval augmented generation and few-shot learning |
| title_full | Art appreciation based on graph retrieval augmented generation and few-shot learning |
| title_fullStr | Art appreciation based on graph retrieval augmented generation and few-shot learning |
| title_full_unstemmed | Art appreciation based on graph retrieval augmented generation and few-shot learning |
| title_short | Art appreciation based on graph retrieval augmented generation and few-shot learning |
| title_sort | art appreciation based on graph retrieval augmented generation and few shot learning |
| topic | graph retrieval augmented generation art education multimodal large language model |
| url | http://www.j-bigdataresearch.com.cn/zh/article/111999100/ |
| work_keys_str_mv | AT liutianyang artappreciationbasedongraphretrievalaugmentedgenerationandfewshotlearning AT kousijia artappreciationbasedongraphretrievalaugmentedgenerationandfewshotlearning AT jinxu artappreciationbasedongraphretrievalaugmentedgenerationandfewshotlearning AT wangwenjing artappreciationbasedongraphretrievalaugmentedgenerationandfewshotlearning AT luxuesong artappreciationbasedongraphretrievalaugmentedgenerationandfewshotlearning |