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 …

As large language models continue to develop in the field of AI, text generation systems are
susceptible to a worrisome phenomenon known as hallucination. In this study, we …

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 …

Do large language models (LLMs) know the law? LLMs are increasingly being used to
augment legal practice, education, and research, yet their revolutionary potential is …

The fluency and creativity of large pre-trained language models (LLMs) have led to their
widespread use, sometimes even as a replacement for traditional search engines. Yet …

This survey provides an in-depth analysis of knowledge conflicts for large language models
(LLMs), highlighting the complex challenges they encounter when blending contextual and …

Retrieval-augmented generation (RAG) has become a main technique for alleviating
hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may …

Recent language models generate false but plausible-sounding text with surprising
frequency. Such “hallucinations” are an obstacle to the usability of language-based AI …

As language models are adapted by a more sophisticated and diverse set of users, the
importance of guaranteeing that they provide factually correct information supported by …