Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue
models often suffer from factual incorrectness and hallucination of knowledge (Roller et al …

Abstract Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM)
output by providing prior knowledge as context to input. This is beneficial for knowledge …

We present a memory-augmented approach to condition an autoregressive language model
on a knowledge graph. We represent the graph as a collection of relation triples and retrieve …

Though nearest neighbor Machine Translation ($ k $ NN-MT)\citep {khandelwal2020nearest
} has proved to introduce significant performance boosts over standard neural MT systems, it …

Conducting literature reviews for scientific papers is essential for understanding research, its
limitations, and building on existing work. It is a tedious task which makes an automatic …

Large language models can produce fluent dialogue but often hallucinate factual
inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a …

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 …

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

$ 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 …

Retrieval-augmented methods are successful in the standard scenario where the retrieval
space is sufficient; whereas in the few-shot scenario with limited retrieval space, this paper …