Retrieval-based language models (R-LM) model the probability of natural language text by
combining a standard language model (LM) with examples retrieved from an external …

Considerable effort has been dedicated to mitigating toxicity, but existing methods often
require drastic modifications to model parameters or the use of computationally intensive …

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 …

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 …

Math word problem (MWP) solving is an important task in question answering which
requires human-like reasoning ability. Analogical reasoning has long been used in …

$ 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 …

Transfer learning has been shown to be an effective technique for enhancing the
performance of low-resource neural machine translation (NMT). This is typically achieved …

$ k $-Nearest neighbor machine translation ($ k $ NN-MT) has attracted increasing attention
due to its ability to non-parametrically adapt to new translation domains. By using an …