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A Survey of Hallucination in Large Visual Language Models
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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.
Forward citations
Cited by 3 Pith papers
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VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models
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Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key
OPA-DPO aligns expert-corrected hallucination responses with the model's own distribution via SFT before DPO, cutting hallucination rates on AMBER and Object-Hal benchmarks.
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The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs
A structured survey of LLM safety evaluation that proposes a why/what/where/how taxonomy and catalogs metrics, datasets, benchmarks, evaluators, and frameworks.
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