Large-scale generative models show an impressive ability to perform a wide range of
Natural Language Processing (NLP) tasks using in-context learning, where a few examples …

Recently, retrieval-augmented text generation attracted increasing attention of the
computational linguistics community. Compared with conventional generation models …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Large language models (LLMs) demonstrate remarkable machine translation (MT) abilities
via prompting, even though they were not explicitly trained for this task. However, even given …

k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-
parametric solution for domain adaptation in neural machine translation (NMT). It aims to …

Pre-trained language models (PLMs) have exhibited remarkable few-shot learning
capabilities when provided a few examples in a natural language prompt as demonstrations …

Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in
various tasks with instruction tuning. However, these models can sometimes struggle with …

Semi-parametric models, which augment generation with retrieval, have led to impressive
results in language modeling and machine translation, due to their ability to retrieve fine …

k-Nearest-Neighbor Machine Translation (kNN-MT) becomes an important research
direction of NMT in recent years. Its main idea is to retrieve useful key-value pairs from an …

$ k $ NN-MT is a straightforward yet powerful approach for fast domain adaptation, which
directly plugs pre-trained neural machine translation (NMT) models with domain-specific …