Abstract Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has
been a new rising research hotspot, which uses powerful Large Language Models (LLMs) …

Recent development of Large Vision-Language Models (LVLMs) has attracted growing
attention within the AI landscape for its practical implementation potential. However,`` …

The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs)
are plagued by the critical issue of hallucination. The reliable detection of such …

Multimodal large language models (MLLMs) enhance the capabilities of standard large
language models by integrating and processing data from multiple modalities, including text …

One of the key shortcomings in current text-to-image (T2I) models is their inability to
consistently generate images which faithfully follow the spatial relationships specified in the …

Despite recent successes, LVLMs or Large Vision Language Models are prone to
hallucinating details like objects and their properties or relations, limiting their real-world …

Large Vision-Language Models (LVLMs) exhibit remarkable capabilities but struggle
with''hallucinations''-inconsistencies between images and their descriptions. Previous …

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

Large vision language models (LVLMs) often suffer from object hallucination, producing
objects not present in the given images. While current benchmarks for object hallucination …