  Retrieval-augmented language models (LMs) have received much attention
recently. However, typically the retriever is not trained jointly as a native
component of the LM, but added post-hoc to an already-pretrained LM, which
limits the ability of the LM and the retriever to adapt to one another. In this
work, we propose the Retrieval-Pretrained Transformer (RPT), an architecture
and training procedure for jointly training a retrieval-augmented LM from
scratch and apply it to the task of modeling long texts. Given a recently
generated text chunk in a long document, the LM computes query representations,
which are then used to retrieve earlier chunks in the document, located
potentially tens of thousands of tokens before. Information from retrieved
chunks is fused into the LM representations to predict the next target chunk.
We train the retriever component with a semantic objective, where the goal is
to retrieve chunks that increase the probability of the next chunk, according
to a reference LM. We evaluate RPT on four long-range language modeling tasks,
spanning books, code, and mathematical writing, and demonstrate that RPT
improves retrieval quality and subsequently perplexity across the board
compared to strong baselines.
