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Retrieval-augmented language models such as Fusion-in-Decoder are powerful,setting the state of the art on a variety of knowledge-intensive tasks.However, they are also expensive, due to the need to encode a large number ofretrieved passages. Some work avoids this cost by pre-encoding a text corpusinto a memory and retrieving dense representations directly. However,pre-encoding memory incurs a severe quality penalty as the memoryrepresentations are not conditioned on the current input. We propose LUMEN, ahybrid between these two extremes, pre-computing the majority of the retrievalrepresentation and completing the encoding on the fly using a live encoder thatis conditioned on the question and fine-tuned for the task. We show that LUMENsignificantly outperforms pure memory on multiple question-answering taskswhile being much cheaper than FiD, and outperforms both for any given computebudget. Moreover, the advantage of LUMEN over FiD increases with model size.

Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute