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Free pause tokens boost language model performance without increasing context length or latency, achieving significant gains with minimal training overhead.
Stacking language models into a single nested architecture can cut training costs by 36% while maintaining competitive performance.
CO-LMLM achieves lower perplexity than models trained on 40 times more data, revolutionizing how we leverage knowledge bases in language generation.
