Summary of Explaining Multi-modal Large Language Models by Analyzing Their Vision Perception, By Loris Giulivi et al.
Explaining Multi-modal Large Language Models by Analyzing their Vision Perception
by Loris Giulivi, Giacomo Boracchi
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach to enhance the interpretability of Multi-modal Large Language Models (MLLMs) is proposed. The method focuses on the image embedding component by combining an open-world localization model with a MLLM, allowing for simultaneous text and object localization outputs from the same vision embedding. This leads to improved interpretability, enabling the design of novel saliency maps, hallucination identification, and bias assessment through semantic adversarial perturbations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves the understanding and generation capabilities of MLLMs by making them more interpretable. It’s a great step forward in using these models for important tasks. |
Keywords
» Artificial intelligence » Embedding » Hallucination » Multi modal
