Zero-Shot Classification of Art With Large Language Models
Art has become an important new investment vehicle. Thus, interest is growing in art price prediction as a tool for assessing the returns and risks of art investments. Both traditional statistical methods and machine learning methods have been used to predict art prices. However, both methods incur...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10851281/ |
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| author | Tatsuya Tojima Mitsuo Yoshida |
| author_facet | Tatsuya Tojima Mitsuo Yoshida |
| author_sort | Tatsuya Tojima |
| collection | DOAJ |
| description | Art has become an important new investment vehicle. Thus, interest is growing in art price prediction as a tool for assessing the returns and risks of art investments. Both traditional statistical methods and machine learning methods have been used to predict art prices. However, both methods incur substantial human costs for data preprocessing for the construction of prediction models, necessitating a reduction in the workload. In this study, we propose the zero-shot classification method to perform automatic annotation in data processing for art price prediction by leveraging large language models (LLMs). The proposed method can perform annotation without new training data. Thus, it minimizes human costs. Our experiments demonstrated that the 4-bit quantized Llama-3 70B model, which can run on a local server, achieved the most accurate (over 0.9) automatic annotation of different art forms using LLMs, performing slightly better than the GPT-4o model from OpenAI. These results are practical for data preprocessing and comparable with the results of previous machine learning methods. |
| format | Article |
| id | doaj-art-852eb467ddf348859f1ed95a7c62b2fe |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-852eb467ddf348859f1ed95a7c62b2fe2025-01-31T00:01:33ZengIEEEIEEE Access2169-35362025-01-0113174261743910.1109/ACCESS.2025.353299510851281Zero-Shot Classification of Art With Large Language ModelsTatsuya Tojima0https://orcid.org/0009-0005-8048-9647Mitsuo Yoshida1https://orcid.org/0000-0002-0735-1116Degree Programs in Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, JapanInstitute of Business Sciences, University of Tsukuba, Bunkyo, Tokyo, JapanArt has become an important new investment vehicle. Thus, interest is growing in art price prediction as a tool for assessing the returns and risks of art investments. Both traditional statistical methods and machine learning methods have been used to predict art prices. However, both methods incur substantial human costs for data preprocessing for the construction of prediction models, necessitating a reduction in the workload. In this study, we propose the zero-shot classification method to perform automatic annotation in data processing for art price prediction by leveraging large language models (LLMs). The proposed method can perform annotation without new training data. Thus, it minimizes human costs. Our experiments demonstrated that the 4-bit quantized Llama-3 70B model, which can run on a local server, achieved the most accurate (over 0.9) automatic annotation of different art forms using LLMs, performing slightly better than the GPT-4o model from OpenAI. These results are practical for data preprocessing and comparable with the results of previous machine learning methods.https://ieeexplore.ieee.org/document/10851281/Artauction priceChatGPTclassificationdata preprocessingGemma |
| spellingShingle | Tatsuya Tojima Mitsuo Yoshida Zero-Shot Classification of Art With Large Language Models IEEE Access Art auction price ChatGPT classification data preprocessing Gemma |
| title | Zero-Shot Classification of Art With Large Language Models |
| title_full | Zero-Shot Classification of Art With Large Language Models |
| title_fullStr | Zero-Shot Classification of Art With Large Language Models |
| title_full_unstemmed | Zero-Shot Classification of Art With Large Language Models |
| title_short | Zero-Shot Classification of Art With Large Language Models |
| title_sort | zero shot classification of art with large language models |
| topic | Art auction price ChatGPT classification data preprocessing Gemma |
| url | https://ieeexplore.ieee.org/document/10851281/ |
| work_keys_str_mv | AT tatsuyatojima zeroshotclassificationofartwithlargelanguagemodels AT mitsuoyoshida zeroshotclassificationofartwithlargelanguagemodels |