This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Generation of plausible yet incorrect factual information, termed hallucination, is an
unsolved issue in large language models. We study the ability of language models to …

Large language models (LLMs) can store a vast amount of world knowledge, often
extractable via question-answering (eg," What is Abraham Lincoln's birthday?"). However …

As Large Language Models (LLMs) continue to advance in their ability to write human-like
text, a key challenge remains around their tendency to hallucinate generating content that …

We investigate the internal behavior of Transformer-based Large Language Models (LLMs)
when they generate factually incorrect text. We propose modeling factual queries as …

Language models can store vast amounts of factual knowledge, but their ability to use this
knowledge for logical reasoning remains questionable. This paper explores a language …

Language Models (LMs) memorize a vast amount of factual knowledge, exhibiting strong
performance across diverse tasks and domains. However, it has been observed that the …