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Research questionHow can multiple-choice music audio-language models estimate uncertainty well enough to abstain without costly ensembles or retraining?Multiple-choice evaluation forces a model to choose even when its musical understanding is weak, so a lucky guess can resemble genuine competence. A single predictive distribution provides limited evidence about answer reliability, while independently trained ensembles are expensive.
AI
Audio & Speech
Evaluation & Benchmarks
Machine Learning
Multimodal Models
Small / On-device Models
Sound
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Knowing When Not to Answer: Pseudo-Ensembles for Abstention in Music Audio-Language ModelsThe evidence concerns TinyMU evaluated on MuChoMusic. It examines uncertainty from multiple input perturbations, including answer-order changes, corrupted audio, and swapped option labels, using extra forward passes without retraining; the reported results do not establish performance beyond this task and evaluation setting.research paper · Sep 3, 2026
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