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Retrieval augmentation addresses many critical problems in large languagemodels such as hallucination, staleness, and privacy leaks. However, runningretrieval-augmented language models (LMs) is slow and difficult to scale due toprocessing large amounts of retrieved text. We introduce binary tokenrepresentations (BTR), which use 1-bit vectors to precompute every token inpassages, significantly reducing computation during inference. Despite thepotential loss of accuracy, our new calibration techniques and trainingobjectives restore performance. Combined with offline and runtime compression,this only requires 127GB of disk space for encoding 3 billion tokens inWikipedia. Our experiments show that on five knowledge-intensive NLP tasks, BTRaccelerates state-of-the-art inference by up to 4x and reduces storage by over100x while maintaining over 95% task performance.

BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models