RETRO

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• DeepMind says its new language model can beat others 25 times its size | Will Douglas Heaven - MIT Technology Review ... RETRO (for “Retrieval-Enhanced Transformer”), uses an external memory to look up passages of text on the fly, avoiding some of the costs of training a vast neural network

We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (Retro) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25× fewer parameters. After fine-tuning, Retro performance translates to downstream knowledge-intensive tasks such as question answering. Retro combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train Retro from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale. - RETRO | S. Borgeaud, A. Mensch, J. Hoffmann, & L. Sifre DeepMind

Are Bigger Language Models Better? | DeepMind Gopher and RETRO Retrieval-Enhanced Transformer (RETRO) is autoregressive language model from DeepMind’s Improving Language Models by Retrieving from Trillions of Tokens (2021) Jordan Harrod

DeepMind's RETRO Transformer Model Retrieval-Enhanced Language Model cross-attends trillions of tokens for SoTA on Wikitext103 and The Pile with 25x fewer parameters. Vaclav Kosar