Adaptive selection among a library of audio perturbations in contrastive decoding produces task-dependent accuracy gains, including +4.3% on an existence task via a hidden-state selector.
Can large audio-language models truly hear? Tackling hallucinations with multi-task assessment and stepwise audio reasoning.arXiv preprint arXiv:2410.16130
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A literature survey that organizes spoken language models by architecture, training, and evaluation choices and identifies key challenges and future directions.
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Adaptive Perturbation Selection for Contrastive Audio Decoding
Adaptive selection among a library of audio perturbations in contrastive decoding produces task-dependent accuracy gains, including +4.3% on an existence task via a hidden-state selector.
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On The Landscape of Spoken Language Models: A Comprehensive Survey
A literature survey that organizes spoken language models by architecture, training, and evaluation choices and identifies key challenges and future directions.