ComposerX: Multi-Agent Symbolic Music Composition with LLMs
arxiv(2024)
摘要
Music composition represents the creative side of humanity, and itself is a
complex task that requires abilities to understand and generate information
with long dependency and harmony constraints. While demonstrating impressive
capabilities in STEM subjects, current LLMs easily fail in this task,
generating ill-written music even when equipped with modern techniques like
In-Context-Learning and Chain-of-Thoughts. To further explore and enhance LLMs'
potential in music composition by leveraging their reasoning ability and the
large knowledge base in music history and theory, we propose ComposerX, an
agent-based symbolic music generation framework. We find that applying a
multi-agent approach significantly improves the music composition quality of
GPT-4. The results demonstrate that ComposerX is capable of producing coherent
polyphonic music compositions with captivating melodies, while adhering to user
instructions.
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