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

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

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

With the invention of deep learning concepts, Machine Translation (MT) migrated towards
Neural Machine Translation (NMT) architectures, eventually from Statistical Machine …

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

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

Inspired by the notion that``{\it to copy is easier than to memorize}``, in this work, we
introduce GNN-LM, which extends the vanilla neural language model (LM) by allowing to …

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 …

Inspired by recent advances in retrieval augmented methods in NLP~\citep {
khandelwal2019generalization, khandelwal2020nearest, meng2021gnn}, in this paper, we …