A Survey of Hallucination in Large Visual Language Models
This is an incremental work that provides a comprehensive review for researchers and practitioners in AI to understand and address hallucination issues in LVLMs.
This survey addresses the problem of hallucinations in Large Visual Language Models (LVLMs), which limit their practical effectiveness, by reviewing existing mitigation and correction methods and available evaluation benchmarks.
The Large Visual Language Models (LVLMs) enhances user interaction and enriches user experience by integrating visual modality on the basis of the Large Language Models (LLMs). It has demonstrated their powerful information processing and generation capabilities. However, the existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields. Although lots of work has been devoted to the issue of hallucination mitigation and correction, there are few reviews to summary this issue. In this survey, we first introduce the background of LVLMs and hallucinations. Then, the structure of LVLMs and main causes of hallucination generation are introduced. Further, we summary recent works on hallucination correction and mitigation. In addition, the available hallucination evaluation benchmarks for LVLMs are presented from judgmental and generative perspectives. Finally, we suggest some future research directions to enhance the dependability and utility of LVLMs.