Retrieval-augmented generation models have shown state-of-the-art performance across
many knowledge-intensive NLP tasks such as open question answering and fact …

Recent research shows that pre-trained language models (PLMs) suffer from" prompt bias"
in factual knowledge extraction, ie, prompts tend to introduce biases toward specific labels …

Previous literatures show that pre-trained masked language models (MLMs) such as BERT
can achieve competitive factual knowledge extraction performance on some datasets …

In order to facilitate natural language understanding, the key is to engage commonsense or
background knowledge. However, how to engage commonsense effectively in question …

We propose a new paradigm to help Large Language Models (LLMs) generate more
accurate factual knowledge without retrieving from an external corpus, called RECITation …

We proposed an end-to-end system design towards utilizing Retrieval Augmented
Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for …

Despite significant interest in developing general purpose fact checking models, it is
challenging to construct a large-scale fact verification dataset with realistic real-world claims …

Fact-checking of textual sources needs to effectively extract relevant information from large
knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this …

In this work, we study the impact of QA fine-tuning data on downstream factuality. We show
that fine-tuning on lesser-known facts that are poorly stored during pretraining yields …

On-the-fly retrieval of relevant knowledge has proven an essential element of reliable
systems for tasks such as open-domain question answering and fact verification. However …