Between Predictability and Randomness: Seeking Artistic Inspiration from AI Generative Models

TL;DR

This paper investigates how AI-generated poetic lines, especially from LSTM-VAE models, can serve as open-ended stimuli to inspire authentic artistic creativity, contrasting with more conventional LLM-produced poetry.

Contribution

It introduces the use of LSTM-VAE generated lines as evocative artistic stimuli, highlighting their semantic openness and potential to foster organic narrative development.

Findings

LSTM-VAE lines evoke resonance through imagery and indeterminacy.

LLMs produce technically proficient but conventional poetry.

Engagement with LSTM-VAE lines can lead to organic narrative creation.

Abstract

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.