In BLOOM: Creativity and Affinity in Artificial Lyrics and Art
Keywords: Large language models, creative AI, prompt engineering, stable diffusion, lyric generation, Chinese NLP, text generation
TL;DR: Creating lyrics and album art to discover the limits of current evaluation metrics for measuring subjective qualities of art.
Abstract: We apply a large multilingual language model (BLOOM-176B) in open-ended generation of Chinese song lyrics, and evaluate the resulting lyrics for coherence and creativity using human reviewers. We find that current computational metrics for evaluating large language model outputs (MAUVE) have limitations in evaluation of creative writing. We note that the human concept of creativity requires lyrics to be both comprehensible and distinctive --- and that humans assess certain types of machine-generated lyrics to score more highly than real lyrics by popular artists. Inspired by the inherently multimodal nature of album releases, we leverage a Chinese-language stable diffusion model to produce high-quality lyric-guided album art, demonstrating a creative approach for an artist seeking inspiration for an album or single. Finally, we introduce the MojimLyrics dataset, a Chinese-language dataset of popular song lyrics for future research.
Submission Type: archival
Presentation Type: onsite
Presenter: Evan Crothers
Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2301.05402/code)
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