HalluAudio is the first large-scale benchmark spanning speech, environmental sound, and music that uses human-verified QA pairs, adversarial prompts, and mixed-audio tests to measure hallucinations in large audio-language models.
Chun-Yi Kuan and Hung-yi Lee
3 Pith papers cite this work. Polarity classification is still indexing.
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A learned per-example router over a 105-perturbation audio library improves contrastive decoding for audio-LLM hallucination, with task-dependent best distortions (e.g., reverse audio for temporal order).
CAAD internalizes contrastive audio-aware decoding into student SLM weights via synchronized teacher-forcing, delivering an 8% relative gain over standard knowledge distillation on Dynamic-SUPERB while reducing linguistic bias on MCR-BENCH.
citing papers explorer
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HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models
HalluAudio is the first large-scale benchmark spanning speech, environmental sound, and music that uses human-verified QA pairs, adversarial prompts, and mixed-audio tests to measure hallucinations in large audio-language models.
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Adaptive Perturbation Selection for Contrastive Audio Decoding
A learned per-example router over a 105-perturbation audio library improves contrastive decoding for audio-LLM hallucination, with task-dependent best distortions (e.g., reverse audio for temporal order).
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CAAD: Contrastive Audio-Aware Distillation for Efficient Speech Language Models
CAAD internalizes contrastive audio-aware decoding into student SLM weights via synchronized teacher-forcing, delivering an 8% relative gain over standard knowledge distillation on Dynamic-SUPERB while reducing linguistic bias on MCR-BENCH.