Abstract 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 …

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 …

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

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

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 …

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 …

Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

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 …

Artificial Intelligence (AI) in Education claims to have the potential for building personalised
curricula, as well as bringing opportunities for democratising education and creating a …

Language models can store vast factual knowledge, yet their ability to flexibly use this
knowledge for downstream tasks (eg, via instruction finetuning) remains questionable. This …