Why Musical Artificial Intelligence is difficult to train even with larger LLMS

This research paper explores the challenges of training artificial intelligence models for music generation, even with the advent of large language models (LLMs). While LLMs have revolutionized natural language processing and speech synthesis, their success has not fully translated into the musical domain. This paper investigates the technical, cultural, and data-related obstacles that hinder AI's ability to effectively understand and generate music, especially in diverse global styles. Particular attention is given to the symbolic vs. audio-based learning gap, the scarcity of well-annotated and diverse training datasets, and the limitations of current neural network architectures in capturing musical creativity and emotional nuance. The paper concludes with proposed directions for more inclusive and effective AI-driven music systems.