Improving language models by retrieving from trillions of tokens
This addresses the challenge of improving language model efficiency and knowledge access for AI applications, though it builds incrementally on existing retrieval-augmented methods.
The authors tackled the problem of scaling language model performance by enhancing auto-regressive models with retrieval from a 2 trillion token database, achieving comparable performance to GPT-3 and Jurassic-1 on the Pile while using 25× fewer parameters.
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$\times$ 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.