Recent work has improved language models (LMs) remarkably by equipping them with a
non-parametric memory component. However, most existing approaches only introduce …

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

Abstract Multi-Label Text Classification (MLTC) is a fundamental and challenging task in
natural language processing. Previous studies mainly focus on learning text representation …

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) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

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 …

Machine translation models struggle when translating out-of-domain text, which makes
domain adaptation a topic of critical importance. However, most domain adaptation methods …

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 …