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Text language models (LMs) acquire broad linguistic and factual capabilities through large-scale pretraining, while comparable capabilities remain difficult to learn from speech alone. Recent work has shown that knowledge distillation from pretrained text LMs offers a promising way to transfer their linguistic and fact...
A method for distilling sequential computation by replacing spans of input tokens with collapsed representations, computed on the fly by a lightweight merge module, allowing pretrained models to operate on compressed inputs without architectural changes or re-training.
The speech-text model closes much of the scaling gap in transcript-based semantic coherence, suggesting that text provides an efficient semantic training signal for spoken language modeling.
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