Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment

This research explores efficient methods for evaluating Large Audio Models (LAMs) using minimal data subsets, achieving high correlation with full benchmarks. It also shows that regression models trained on these subsets can better predict human preferences for user satisfaction than full benchmarks.