Dec 8, 2021 · Abstract page for arXiv paper 2112.04426: Improving language models by retrieving from trillions of tokens.
May 4, 2021 · We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with pre-.
Abstract. We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding ...
The Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25% fewer parameters, ...
Dec 8, 2021 · We show that language modeling improves continuously as we increase the size of the retrieval database, at least up to 2 trillion tokens – 175 ...
We show that retrieving based on a pre-trained frozen. Bert model (§2.3) works at scale, removing the need for training and updating a retriever network. 4 We ...
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Paper. Title: Improving Language Models by Retrieving from Trillions of Tokens; Authors: Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza ...
Jul 8, 2023 · Retrieval-Enhanced Transformer (RETRO) combines a frozen BERT retriever, a differentiable encoder and a chunked cross-attention decoder.
Jan 4, 2024 · Scaling the training data to trillions of tokens improves the performance of language models in machine translation and downstream tasks.
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Apr 28, 2024 · In 2021, Deepmind published Improving language models by retrieving from trillions of tokens and introduced a Retrieval-Enhanced Transformer ...