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).
Binghuai Lin, Liyuan Wang, Xiaoli Feng, and Jinsong Zhang
3 Pith papers cite this work. Polarity classification is still indexing.
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Speech language models fail at reasoning about sentence stress but improve after fine-tuning on a new 17k-example synthetic dataset that varies stress to alter meaning.
AUDITA is a challenging audio QA benchmark where humans score 32% accuracy on average while state-of-the-art models score below 9%, using IRT to reveal systematic model deficiencies.
citing papers explorer
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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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StressTest: Can YOUR Speech LM Handle the Stress?
Speech language models fail at reasoning about sentence stress but improve after fine-tuning on a new 17k-example synthetic dataset that varies stress to alter meaning.
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AUDITA: A New Dataset to Audit Humans vs. AI Skill at Audio QA
AUDITA is a challenging audio QA benchmark where humans score 32% accuracy on average while state-of-the-art models score below 9%, using IRT to reveal systematic model deficiencies.