Summary of A Survey Of Hallucination in Large Visual Language Models, by Wei Lan et al.
A Survey of Hallucination in Large Visual Language Models
by Wei Lan, Wenyi Chen, Qingfeng Chen, Shirui Pan, Huiyu Zhou, Yi Pan
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Large Visual Language Models (LVLMs) integrate visual modality with Large Language Models (LLMs), enhancing user interaction and enriching user experience. LVLMs have demonstrated powerful information processing and generation capabilities, but hallucinations limit their potential and practical effectiveness. This survey reviews recent works on hallucination correction and mitigation, introducing the background of LVLMs and hallucinations, main causes of hallucination generation, and available evaluation benchmarks from judgmental and generative perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LVLMs combine visual and language processing to improve user interaction and experience. They can process and generate information well, but sometimes make things up (hallucinate). This makes it hard to trust them in real-life applications. This survey looks at how researchers are trying to fix this problem by correcting or mitigating hallucinations. It also explains why LVLMs work the way they do and what measures are used to test their reliability. |
Keywords
» Artificial intelligence » Hallucination
