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 methods with 18 audio models across 40 tasks covering major LAM evaluation dimensions, we show that subsets of just 50 examples (0.3% of data) can achieve over 0.93 Pearson correlation with full benchmark scores. To understand how well these scores align with what practitioners ultimately care about, user satisfaction, we collect 776 human preference ratings from realistic voice assistant conv...
| Authors | Woody Haosheng Gan, William Held, Diyi Yang |
|---|---|
| Primary category | cs.CL |
| Published | 20 April 2026 |
| Last updated at source | 13 September 2026 |
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