  In this paper, we study the generation quality of interpolation-based
retrieval-augmented language models (LMs). These methods, best exemplified by
the KNN-LM, interpolate the LM's predicted distribution of the next word with a
distribution formed from the most relevant retrievals for a given prefix. While
the KNN-LM and related methods yield impressive decreases in perplexity, we
discover that they do not exhibit corresponding improvements in open-ended
generation quality, as measured by both automatic evaluation metrics (e.g.,
MAUVE) and human evaluations. Digging deeper, we find that interpolating with a
retrieval distribution actually increases perplexity compared to a baseline
Transformer LM for the majority of tokens in the WikiText-103 test set, even
though the overall perplexity is lower due to a smaller number of tokens for
which perplexity dramatically decreases after interpolation. However, when
decoding a long sequence at inference time, significant improvements on this
smaller subset of tokens are washed out by slightly worse predictions on most
tokens. Furthermore, we discover that the entropy of the retrieval distribution
increases faster than that of the base LM as the generated sequence becomes
longer, which indicates that retrieval is less reliable when using
model-generated text as queries (i.e., is subject to exposure bias). We hope
that our analysis spurs future work on improved decoding algorithms and
interpolation strategies for retrieval-augmented language models.
