  Retrieval-augmented language models (LMs) use non-parametric memory to
substantially outperform their non-retrieval counterparts on perplexity-based
evaluations, but it is an open question whether they achieve similar gains in
few- and zero-shot end-task accuracy. We extensively study one such model, the
k-nearest neighbor LM (kNN-LM), showing that the gains marginally transfer. The
main challenge is to achieve coverage of the verbalizer tokens that define the
different end-task class labels. To address this challenge, we also introduce
kNN-Prompt, a simple and effective kNN-LM with automatically expanded fuzzy
verbalizers (e.g. to expand terrible to also include silly and other
task-specific synonyms for sentiment classification). Across nine diverse
end-tasks, using kNN-Prompt with GPT-2 large yields significant performance
boosts over strong zero-shot baselines (13.4% absolute improvement over the
base LM on average). We also show that other advantages of non-parametric
augmentation hold for end tasks; kNN-Prompt is effective for domain adaptation
with no further training, and gains increase with the size of the retrieval
model.
