MRMAD: A Multi-Round Multi-Audio Benchmark for Evaluating Acoustic Degradation Perception in Large Audio-Language Models
This work introduces MRMAD, a Multi-Round Multi-Audio Degradation benchmark for evaluating audio degradation perception and understanding in LALMs, and finds that current models often recognize coarse content while failing to diagnose, compare, or reason about degradations reliably.
Yi-Ze Li, Ning-Yuan Yang, Sile Yin et al.
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