  Retrieval augmented methods have shown promising results in various
classification tasks. However, existing methods focus on retrieving extra
context to enrich the input, which is noise sensitive and non-expandable. In
this paper, following this line, we propose a $k$-nearest-neighbor (KNN) -based
method for retrieval augmented classifications, which interpolates the
predicted label distribution with retrieved instances' label distributions.
Different from the standard KNN process, we propose a decoupling mechanism as
we find that shared representation for classification and retrieval hurts
performance and leads to training instability. We evaluate our method on a wide
range of classification datasets. Experimental results demonstrate the
effectiveness and robustness of our proposed method. We also conduct extra
experiments to analyze the contributions of different components in our
model.\footnote{\url{https://github.com/xnliang98/knn-cls-w-decoupling}}
