[4S3-OS-43] OS-43

[4S3-OS-43-05] Creative Music Generation via In-Context Learning of Value Criteria

〇Futa Hidaka¹, Naomi Imasato¹, Kazuki Miyazawa¹, Takato Horii^1,2 (1. Osaka University, 2. Univ. of Tokyo)

Keywords:Music generation, In-context learning, Creativity

Creativity in people and their creations involves two key concepts: novelty and value. A creation is creative when it meets both. With LLM-based generative models, we can produce diverse, novel music via language manipulation. However, a musical work’s value is determined externally. To address the value aspect of creativity, the generative model must learn value criteria through iterative music generation and evaluation. We propose a system using LLM-based in context learning to learn and apply such criteria in repeated loops. The system forms hypotheses about potentially high-rated music, generates accordingly, and refines its approach via feedback. Experiments under multiple value criteria showed it can produce high-value music by inferring and hypothesizing those criteria. However, inferred values may not match actual criteria, reflecting dependence on the LLM’s training. Experimental results suggest that, by trial and error, a system can interpret external standards and generate valuable music.