Question answering (QA) systems provide a way of querying the information available in
various formats including, but not limited to, unstructured and structured data in natural …

Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …

The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …

It remains an open question whether incorporating external knowledge benefits
commonsense reasoning while maintaining the flexibility of pretrained sequence models. To …

Answering complex questions about textual narratives requires reasoning over both stated
context and the world knowledge that underlies it. However, pretrained language models …

One of the most challenging question types in VQA is when answering the question requires
outside knowledge not present in the image. In this work we study open-domain knowledge …

Recently, large-scale pre-trained language models have demonstrated impressive
performance on several commonsense-reasoning benchmark datasets. However, building …

Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …

In recent years, with the rapid development of Internet technology and applications, the
scale of Internet data has exploded, which contains a significant amount of valuable …

Existing work on augmenting question answering (QA) models with external knowledge (eg,
knowledge graphs) either struggle to model multi-hop relations efficiently, or lack …