  Non-parametric neural language models (NLMs) learn predictive distributions
of text utilizing an external datastore, which allows them to learn through
explicitly memorizing the training datapoints. While effective, these models
often require retrieval from a large datastore at test time, significantly
increasing the inference overhead and thus limiting the deployment of
non-parametric NLMs in practical applications. In this paper, we take the
recently proposed $k$-nearest neighbors language model (Khandelwal et al.,
2020) as an example, exploring methods to improve its efficiency along various
dimensions. Experiments on the standard WikiText-103 benchmark and
domain-adaptation datasets show that our methods are able to achieve up to a 6x
speed-up in inference speed while retaining comparable performance. The
empirical analysis we present may provide guidelines for future research
seeking to develop or deploy more efficient non-parametric NLMs.
