用AI生成的模糊诗句激发艺术创作，比完美诗歌更有启发性。
Between Predictability and Randomness: Seeking Artistic Inspiration from AI Generative Models
- 用LSTM-VAE生成带有未完成感的诗句，激发创作者联想。
- 相比LLM的规整诗作，VAE诗句更易引发自由联想与创作冲动。
- 适合想突破套路、寻找灵感的创作者和艺术研究者。
艺术灵感常源于具有开放解读空间的语言。本文探讨了使用AI生成的诗句作为创意刺激的可能性。通过分析两种生成方法——基于长短期记忆变分自编码器（LSTM-VAE）生成的诗句片段，以及大型语言模型（LLMs）生成的完整诗作——发现LSTM-VAE的诗句凭借意象共鸣与有意的不确定性，产生更强的感染力。相比之下，LLMs虽产出技术成熟的诗歌，但多遵循传统模式；而LSTM-VAE的诗句则以语义开放、非常规组合及无法闭合的碎片化特征，更能激发艺术家的参与感。通过一段原创诗歌创作实践，其中情节在与LSTM-VAE生成诗句互动中自然浮现而非预先设定，验证了这些特性可作为真实艺术表达的有效起点。
原文摘要 · Abstract (English)
Artistic inspiration often emerges from language that is open to interpretation. This paper explores the use of AI-generated poetic lines as stimuli for creativity. Through analysis of two generative AI approaches--lines generated by Long Short-Term Memory Variational Autoencoders (LSTM-VAE) and complete poems by Large Language Models (LLMs)--I demonstrate that LSTM-VAE lines achieve their evocative impact through a combination of resonant imagery and productive indeterminacy. While LLMs produce technically accomplished poetry with conventional patterns, LSTM-VAE lines can engage the artist through semantic openness, unconventional combinations, and fragments that resist closure. Through the composition of an original poem, where narrative emerged organically through engagement with LSTM-VAE generated lines rather than following a predetermined structure, I demonstrate how these characteristics can serve as evocative starting points for authentic artistic expression.
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