Adaptive Perturbation Selection for Contrastive Audio Decoding

• URL: http://arxiv.org/abs/2607.00247v1
• Date: Tue, 30 Jun 2026 22:55:22 GMT
• Title: Adaptive Perturbation Selection for Contrastive Audio Decoding
• Abstract summary: Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors.<n>While contrastive decoding (CD) offers training-free mitigation, existing methods rely on blunt perturbations like masking or noise.<n>We explore this design space by evaluating a diverse library of targeted audio perturbations and adaptively selecting the optimal negative branch for each task.
• Score: 17.576102240052503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors. While contrastive decoding (CD) offers training-free mitigation, existing methods rely on blunt perturbations like masking or noise, leaving structured audio transformations unexplored. We explore this design space by evaluating a diverse library of targeted audio perturbations and adaptively selecting the optimal negative branch for each task and example. First, we improve upon earlier prompt engineering by showing that a simple binary yes/no constraint reduces the model's tendency to falsely confirm absent audio features. Second, evaluating our library across temporal, spectral, frequency, and amplitude domains reveals that optimal transformations are highly task-dependent; for instance, reversing the audio array disrupts temporal coherence, raising accuracy on the temporal order task from 74.7% to 81.4%. Finally, we trained a light-weight perturbation selector on model hidden states to dynamically route negative branches, yielding an additional +4.3% gain on the existence task.

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