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Text-only LLMs already contain surprisingly diverse levels of auditory knowledge, and this pre-existing knowledge strongly predicts their performance when adapted for audio-language tasks.
Forget confidence scores: a modality-aware early exit strategy for spoken language models slashes decoding costs without sacrificing accuracy or perceptual quality, revealing that speech tokens require specialized handling compared to text.
