Summary of Explaining Latent Representations Of Generative Models with Large Multimodal Models, by Mengdan Zhu et al.
Explaining latent representations of generative models with large multimodal models
by Mengdan Zhu, Zhenke Liu, Bo Pan, Abhinav Angirekula, Liang Zhao
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed framework aims to provide interpretable representations of data generative latent factors in large multimodal models. By leveraging a large multimodal model that aligns images with text, the framework generates explanations for each latent variable. The uncertainty of these generated explanations is quantified and evaluated across multiple large multimodal models. Visualizations are used to qualitatively analyze the disentanglement effects of different generative models on explanations. This work demonstrates the capabilities and limitations of state-of-the-art large multimodal models in providing explainable AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a way to understand how artificial intelligence (AI) works by generating explanations for its decisions. They used a big model that connects images with text, allowing it to provide reasons for its answers. The team tested different versions of this model and showed how they work differently. By looking at the results, we can see what each part of the AI is doing and why it’s making certain choices. |
