A Sensitivity Analysis of Multi-Event Audio Grounding in Audio LLMs

Audio LLMs have shown a strong ability to understand audio samples, yet their reliability in complex acoustic scenes remains under-explored.Unlike prior work limited to small scale or less controlled query construction, we present a large-scale evaluation of event grounding and false alarms as auditory scene complexity increases.Using 71K AudioCapsV2 clips, we extract normalized (source, attribute) events and build two query types: present-event queries for ground-truth detection and absentevent queries to probe hallucinations, using similarity-filtered negative sampling in an audio-aligned text embedding space.We evaluate four SOTA Audio LLMs with 12 prompt variants over 500K yes/no queries per model.Across models, increasing event count consistently lowers true-positive rate and raises false-positive rate, while prompts induce a strong trade-off between the two.Our confidence analysis shows that models become more uncertain on multi-event audio, revealing room for improvement.