arXiv (OAI Expanded)
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Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment
The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill this gap, we investigate whether minimal subsets can reliably evaluate LAMs while reducing costs and data redundancy. Analyzing 10 subset selection
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