arXiv:2606.02642v1 Announce Type: cross Abstract: Despite the success of audio-visual large-language models (LLMs), they can produce plausible but ungrounded outputs, termed hallucination. Existing benchmarks focus on environmental sounds (e.g., dog barking) to indicate event occurrence. In contrast, human speech carries fundamentally different, rich semantics and temporal structures, yet it remains unexplored whether current models can accurately align speech content with corresponding visual signals. In this work, we show that speech content can induce hallucinations in audio-visual LLMs. To
The rapid advancement of audio-visual large language models necessitates rigorous testing for reliability as they integrate into more applications.
Hallucinations in speech-vision models pose significant risks for AI applications requiring high fidelity and factual grounding, impacting trust and safety.
The focus expands from general environmental sound recognition to the complex task of aligning and understanding human speech content within audio-visual AI.
- · AI safety researchers
- · Model developers focusing on grounding
- · Companies with proprietary data for robust training
- · Audio-visual LLMs with poor grounding
- · Applications reliant on unverified multi-modal outputs
New benchmarks will drive research into mitigating speech-vision hallucinations in audio-visual LLMs.
Improved model reliability will open doors for more sensitive and integrated AI applications in areas like education, healthcare, and human-computer interaction.
Public confidence in multi-modal AI and its practical utility will increase, accelerating adoption in critical sectors.
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Read at arXiv cs.LG