arXiv:2603.00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind. In this paper, we bridge this critical gap by establishing a comprehensive ecosystem for music reward modeling under Compositional Multimodal Instruction (CMI), where the generated music may be conditioned on text descriptions, lyrics, and audio prompts. We first introduce CMI-Pref-Pseudo, a large-scale preference dataset comprising 110k pseudo-labeled samples, and CMI-Pref, a
The rapid advancement of multimodal AI models, particularly in generation, necessitates more robust and nuanced evaluation frameworks to guide future development and deployment.
Sophisticated evaluation of AI-generated content is crucial for ensuring quality, safety, and alignment, impacting the adoption and trust in next-generation AI applications across industries.
The introduction of CMI-RewardBench provides a more comprehensive and standardized approach to evaluating music reward models under complex multimodal conditions, refining the development feedback loop.
- · AI researchers in multimodal generation
- · Music technology companies
- · Developers of AI evaluation platforms
- · Creators utilizing AI music tools
- · Companies relying on outdated evaluation metrics
- · Simple rule-based music generation models
Improved reward models will lead to more nuanced and high-quality AI-generated music and multimodal content.
Higher quality multimodal AI outputs could reduce the barrier to entry for creative content generation, potentially expanding creator economies.
The methodology could be extended to other creative domains, accelerating the development of robust evaluation for a wider array of generative AI applications.
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Read at arXiv cs.AI