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 …

Hallucination in a foundation model (FM) refers to the generation of content that strays from
factual reality or includes fabricated information. This survey paper provides an extensive …

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 …

Large Language Models (LLMs) have emerged as a groundbreaking technology with their
unparalleled text generation capabilities across various applications. Nevertheless …

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) have emerged as a widely-used tool for information
seeking, but their generated outputs are prone to hallucination. In this work, our aim is to …

The ML community is rapidly exploring techniques for prompting language models (LMs)
and for stacking them into pipelines that solve complex tasks. Unfortunately, existing LM …

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 remarkable ability of large language models (LLMs) to comprehend, interpret, and
generate complex language has rapidly integrated LLM-generated text into various aspects …