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 …

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 …

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

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 …

Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

Incorporating factual knowledge in knowledge graph is regarded as a promising approach
for mitigating the hallucination of large language models (LLMs). Existing methods usually …

In the era of large language models (LLMs), hallucination (ie, the tendency to generate
factually incorrect content) poses great challenge to trustworthy and reliable deployment of …

Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the
development of Large Language Models (LLMs). While much of the current research in this …