Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

Recent years have seen an explosive increase in research on large language models
(LLMs), and accompanying public engagement on the topic. While starting as a niche area …

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

Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

Large language models (LLMs) have gained considerable attention for their excellent
natural language processing capabilities. Nonetheless, these LLMs present many …

Knowledge hallucination have raised widespread concerns for the security and reliability of
deployed LLMs. Previous efforts in detecting hallucinations have been employed at logit …

Large language models (LLMs) have demonstrated remarkable capabilities across a wide
range of tasks in various domains. Despite their impressive performance, they can be …

This paper presents a method for estimating the hallucination rate for in-context learning
(ICL) with generative AI. In ICL, a conditional generative model (CGM) is prompted with a …

Large Language Models (LLMs) have demonstrated remarkable capability in a variety of
NLP tasks. However, LLMs are also prone to generate nonfactual content. Uncertainty …