  Recent work on the Retrieval-Enhanced Transformer (RETRO) model has shown
that off-loading memory from trainable weights to a retrieval database can
significantly improve language modeling and match the performance of
non-retrieval models that are an order of magnitude larger in size. It has been
suggested that at least some of this performance gain is due to non-trivial
generalization based on both model weights and retrieval. In this paper, we try
to better understand the relative contributions of these two components. We
find that the performance gains from retrieval largely originate from
overlapping tokens between the database and the test data, suggesting less
non-trivial generalization than previously assumed. More generally, our results
point to the challenges of evaluating the generalization of retrieval-augmented
language models such as RETRO, as even limited token overlap may significantly
decrease test-time loss. We release our code and model at
https://github.com/TobiasNorlund/retro
