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Improving language models by retrieving from trillions of tokens

In recent years, significant performance gains in autoregressive language modeling have been achieved by increasing the number of parameters in Transformer models. This has led to a tremendous increase in training energy cost and resulted in a generation of dense “Large Language Models” (LLMs) with 100+ billion parameters. Simultaneously, large datasets containing trillions of words…

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