K Nearest Neighbor Machine Translation

K-Nearest Neighbor Machine Translation (kNN-MT) enhances neural machine translation (NMT) by retrieving similar past translations from a large datastore during decoding, improving translation quality, especially in low-resource or domain adaptation scenarios. Current research focuses on improving kNN-MT's efficiency through techniques like dynamic retrieval, which selectively skips unnecessary searches, and clustering, which organizes the datastore for faster access. These advancements aim to address the computational cost associated with large datastores, making kNN-MT more practical for real-world applications while maintaining or improving translation accuracy.