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}}

翻译：检索增强方法已在多种分类任务中展现出良好效果。然而，现有方法侧重于检索额外上下文以丰富输入，这类方法对噪声敏感且不可扩展。本文沿此研究路线，提出一种基于$k$近邻(KNN)的检索增强分类方法，该方法将预测标签分布与检索实例的标签分布进行插值。与标准KNN过程不同，我们提出一种解耦机制，因为发现用于分类和检索的共享表征会损害性能并导致训练不稳定。我们在多种分类数据集上评估了该方法。实验结果表明了所提方法的有效性和鲁棒性。此外，我们通过额外实验分析了模型中不同组件的贡献。\footnote{\url{https://github.com/xnliang98/knn-cls-w-decoupling}}