Summary of Hal-eval: a Universal and Fine-grained Hallucination Evaluation Framework For Large Vision Language Models, by Chaoya Jiang et al.
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
by Chaoya Jiang, Hongrui Jia, Wei Ye, Mengfan Dong, Haiyang Xu, Ming Yan, Ji Zhang, Shikun Zhang
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper proposes a refined taxonomy of hallucinations in Large Vision Language Models (LVLMs), specifically addressing complex hallucinations that create an entire narrative around a fictional entity, known as Event Hallucination. The authors utilize advanced LVLMs to generate and filter fine-grained hallucinatory data, with a focus on event hallucinations. This research lays the groundwork for integrating discriminative and generative evaluation methods within a universal evaluation framework. The proposed benchmark assesses LVLMs’ ability to handle a broad spectrum of hallucinations, making it a reliable tool for evaluating their efficacy in tackling hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Vision Language Models can describe images. Right now, these models sometimes make up things that aren’t really there, which is called hallucination. The researchers created a new way to group these hallucinations into categories and then tested advanced language models to see if they could create this kind of data. They’re trying to develop a way to measure how well these models can handle all kinds of hallucinations, so people can trust what they say. |
Keywords
» Artificial intelligence » Hallucination
