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Explicitly modeling copy operations unlocks substantial improvements in LLM efficiency and performance, even for models already adept at in-context learning.
CO-LMLM achieves lower perplexity than models trained on 40 times more data, revolutionizing how we leverage knowledge bases in language generation.
Separating state storage from next-token prediction in Transformers leads to a 2-3 percentage point improvement in downstream task performance.
LLMs suffer from a severe gradient bottleneck in the output layer, suppressing 95-99% of the gradient norm and crippling training.