LLaVA-Docent：利用多模态大语言模型进行指令微调以支持艺术鉴赏教育
LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education
AI总结：
本研究针对艺术鉴赏教育缺乏AI辅助的问题，开发了基于MLLM的私人导师模型LLaVA-Docent，通过GPT-4生成的虚拟对话数据集训练，验证了其在个性化艺术鉴赏教学中的有效性与应用潜力。
摘要：
尽管已有各类AI系统被开发用于支持多领域的学习，但针对艺术鉴赏教育的AI辅助尚未得到广泛探索。对大多数学生而言，艺术鉴赏往往被视为陌生且有挑战性的活动，而搭载生成式AI的对话伙伴可提供定制化问题、鼓励受众深度欣赏艺术品，从而让艺术鉴赏变得更易接触。本研究探索了多模态大语言模型（MLLMs）在艺术鉴赏教育中的应用，重点开发了LLaVA-Docent——一款旨在充当艺术鉴赏私人导师的模型。我们的方法采用设计与开发研究范式，聚焦于对应用的设计与开发进行迭代优化，最终打造出一款功能完备的搭载MLLM的聊天机器人，以及一套面向艺术鉴赏教育的数据设计框架。为此，我们构建了一个由GPT-4生成的虚拟对话数据集，该数据集对训练我们的MLLM模型LLaVA-Docent起到了关键作用。研究通过将LLaVA-Docent与其他备选设置进行基准测试，评估了其性能，揭示了该模型独特的优势与不足。研究结果凸显了基于MLLM的个性化艺术鉴赏聊天机器人的有效性，并证明其适用于一种教授与体验艺术鉴赏的全新方式。
英文摘要：
Despite the development of various AI systems to support learning in various domains, AI assistance for art appreciation education has not been extensively explored. Art appreciation, often perceived as an unfamiliar and challenging endeavor for most students, can be more accessible with a generative AI enabled conversation partner that provides tailored questions and encourages the audience to deeply appreciate artwork. This study explores the application of multimodal large language models (MLLMs) in art appreciation education, with a focus on developing LLaVA-Docent, a model designed to serve as a personal tutor for art appreciation. Our approach involved design and development research, focusing on iterative enhancement to design and develop the application to produce a functional MLLM-enabled chatbot along with a data design framework for art appreciation education. To that end, we established a virtual dialogue dataset that was generated by GPT-4, which was instrumental in training our MLLM, LLaVA-Docent. The performance of LLaVA-Docent was evaluated by benchmarking it against alternative settings and revealed its distinct strengths and weaknesses. Our findings highlight the efficacy of the MMLM-based personalized art appreciation chatbot and demonstrate its applicability for a novel approach in which art appreciation is taught and experienced.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
