Summary of Magnifier Prompt: Tackling Multimodal Hallucination Via Extremely Simple Instructions, by Yuhan Fu et al.
Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions
by Yuhan Fu, Ruobing Xie, Jiazhen Liu, Bangxiang Lan, Xingwu Sun, Zhanhui Kang, Xirong Li
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
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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 A novel method called MagPrompt is proposed to tackle hallucinations in multimodal large language models (MLLMs). This simple approach uses two key principles: prioritizing the image when there’s a conflict between the image and model’s inner knowledge, and focusing more on the image. MagPrompt is training-free and can be applied to various models like GPT-4o and Gemini-pro. It outperforms complex methods like VCD in many datasets, providing valuable insights into multimodal hallucination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hallucinations in language models make it hard for them to work well in real-life situations. A new way called MagPrompt helps fix this problem by giving simple instructions to the model. The idea is to focus more on what’s shown and prioritize what’s seen over what the model thinks it knows. This works for many different models, even ones you can’t see how they were trained. It’s as good or even better than some other methods that are more complicated. |
Keywords
» Artificial intelligence » Gemini » Gpt » Hallucination
