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arXiv · 2603.03855

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

Taehan Lee·Jaehan Jung·Hyukjun Lee
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Abstract

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 absent-event 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.

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Taehan Lee, Jaehan Jung, Hyukjun Lee. 2026-03-04. A Sensitivity Analysis of Multi-Event Audio Grounding in Audio LLMs. https://arxiv.org/abs/2603.03855

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