探索大型语言模型在艺术创作中的潜力:创意编程的合作与反思

ArXiv Pub Date : 2024-02-15 DOI:10.48550/arXiv.2402.09750
Anqi Wang, Zhizhuo Yin, Yulu Hu, Yuanyuan Mao, Pan Hui
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引用次数: 0

摘要

最近,大型语言模型(LLM)的潜力已被广泛用于辅助编程。然而,目前的研究并没有探索 LLM 在艺术家与人工智能合作的创意编码中的艺术家潜力。我们的工作探究了艺术家在创作过程中对这种合作的反思类型。我们比较了两种常见的合作方式:调用整个程序和多个子任务。我们的研究结果表明,在两种不同的方法中,艺术家们的反思受到了不同的刺激。通过实验数据和定性访谈等两种方法,我们的研究结果还显示了在两种协作中,反思类型与用户表现、用户满意度和主观体验之间的相关性。从这个意义上说,我们的工作揭示了 LLM 在创意编码方面的艺术潜力。同时,我们还从艺术家的视角为人类与人工智能的合作提供了一个批判性视角,并为人工智能辅助创意任务的未来工作提出了设计建议。
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Exploring the Potential of Large Language Models in Artistic Creation: Collaboration and Reflection on Creative Programming
Recently, the potential of large language models (LLMs) has been widely used in assisting programming. However, current research does not explore the artist potential of LLMs in creative coding within artist and AI collaboration. Our work probes the reflection type of artists in the creation process with such collaboration. We compare two common collaboration approaches: invoking the entire program and multiple subtasks. Our findings exhibit artists' different stimulated reflections in two different methods. Our finding also shows the correlation of reflection type with user performance, user satisfaction, and subjective experience in two collaborations through conducting two methods, including experimental data and qualitative interviews. In this sense, our work reveals the artistic potential of LLM in creative coding. Meanwhile, we provide a critical lens of human-AI collaboration from the artists' perspective and expound design suggestions for future work of AI-assisted creative tasks.
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