Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success on domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval. However, the reasons underlying its success have not been thoroughly investigated. In this paper, we provide a comprehensive analysis of $k$NN-MT through theoretical and empirical studies. Initially, we offer a theoretical interpretation of the working mechanism of $k$NN-MT as an efficient technique to implicitly execute gradient descent on the output projection layer of NMT, indicating that it is a specific case of model fine-tuning. Subsequently, we conduct multi-domain experiments and word-level analysis to examine the differences in performance between $k$NN-MT and entire-model fine-tuning. Our findings suggest that: (1) Incorporating $k$NN-MT with adapters yields comparable translation performance to fine-tuning on in-domain test sets, while achieving better performance on out-of-domain test sets; (2) Fine-tuning significantly outperforms $k$NN-MT on the recall of low-frequency domain-specific words, but this gap could be bridged by optimizing the context representations with additional adapter layers.

本文分析了$k$NN-MT的理论和实证研究，以及针对多域实验和单词级别的分析，发现在特定情况下，结合$k$NN-MT和适配器的方法能够实现与在域测试集上微调相当的翻译性能，并在域外测试集上取得更好的性能,同时，优化上下文表示可以弥补低频特定领域词汇召回方面$k$NN-MT与微调的差距。

最近邻机器翻译是输出投影层上的元优化器