kNN MT

k-Nearest Neighbor Machine Translation (kNN-MT) enhances neural machine translation (NMT) by retrieving and incorporating similar past translations during decoding, improving translation quality, especially in low-resource or domain adaptation scenarios. Current research focuses on optimizing kNN-MT's efficiency, addressing issues like high computational cost and noisy retrievals through techniques such as predictive filtering of retrieval operations, datastore compression via clustering, and refined knowledge injection methods. These advancements aim to make kNN-MT a more practical and robust approach for various machine translation tasks, bridging the gap between theoretical performance and real-world applications.