arXiv:2607.00247v1 Announce Type: cross 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 bin
The proliferation of large audio-language models is making hallucination a critical challenge, driving research into mitigation techniques like contrastive decoding.
Improved reliability and accuracy in LALMs directly impacts their adoption across various applications, reducing the risk of generative AI producing misleading or incorrect information.
This research introduces a more sophisticated and adaptive approach to contrastive decoding, potentially leading to more robust audio AI systems that better integrate acoustic evidence with language priors.
- · AI developers
- · Audio AI applications
- · End-users of AI assistants
- · Developers relying on blunt hallucination mitigation methods
LALMs become more trustworthy in generating or interpreting audio, accelerating their deployment in sensitive contexts.
The improved reliability may lead to a broader integration of LALMs into critical infrastructure and enterprise solutions.
Increased trust in audio AI could shift human-computer interaction paradigms, with more reliance on voice interfaces for complex tasks.
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Read at arXiv cs.AI