Large Language Models (LLMs) demonstrate impressive capabilities across various fields,
yet their increasing use raises critical security concerns. This article reviews recent literature …

This survey presents a comprehensive analysis of the phenomenon of hallucination in
multimodal large language models (MLLMs), also known as Large Vision-Language Models …

Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing
Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can …

The rapid advancement of foundation models (FMs) across language, image, audio, and
video domains has shown remarkable capabilities in diverse tasks. However, the …

Large Multimodal Models (LMMs) often suffer from multimodal hallucinations, wherein they
may create content that is not present in the visual inputs. In this paper, we explore a new …

Vision-Language Models (VLMs) have demonstrated their broad effectiveness thanks to
extensive training in aligning visual instructions to responses. However, such training of …

The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for
effective knowledge editing. Current methods, including intrinsic knowledge editing and …

Pretrained large models, particularly large language models, have garnered increasing
attention, as they have demonstrated remarkable abilities through contextual learning …

Multimodal Large Language Models (MLLMs) have exhibited impressive capabilities in
visual understanding and reasoning, providing sightly reasonable answers, such as image …

In recent years, generative artificial intelligence models, represented by Large Language
Models (LLMs) and Diffusion Models (DMs), have revolutionized content production …