Summary of Llava-docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education, by Unggi Lee et al.
LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education
by Unggi Lee, Minji Jeon, Yunseo Lee, Gyuri Byun, Yoorim Son, Jaeyoon Shin, Hongkyu Ko, Hyeoncheol Kim
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A generative AI model, LLaVA-Docent, is developed as a personal tutor for art appreciation education. This multimodal large language model (MLLM) is designed to provide tailored questions and encourage students to appreciate artwork. The study explores the application of MLLMs in art appreciation education through design and development research. A virtual dialogue dataset was generated using GPT-4, which trained LLaVA-Docent. The model’s performance was evaluated by benchmarking it against alternative settings, revealing its strengths and weaknesses. The findings demonstrate the efficacy of the MMLM-based personalized art appreciation chatbot as a novel approach to teaching and experiencing art appreciation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Art appreciation can be made more accessible with a generative AI chatbot that asks questions and helps people understand artwork. This study creates a special kind of computer model, called LLaVA-Docent, to serve as a personal tutor for art appreciation. The model is trained using a large dataset generated by GPT-4. The researchers tested the model and found it can be very helpful in teaching and learning about art. |
Keywords
» Artificial intelligence » Gpt » Large language model
