Summary of Unveiling Transformer Perception by Exploring Input Manifolds, By Alessandro Benfenati and Alfio Ferrara and Alessio Marta and Davide Riva and Elisabetta Rocchetti
Unveiling Transformer Perception by Exploring Input Manifolds
by Alessandro Benfenati, Alfio Ferrara, Alessio Marta, Davide Riva, Elisabetta Rocchetti
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed approach is a general method for exploring equivalence classes in the input space of Transformer models. By describing internal layers as sequential deformations of the input manifold, this mathematical theory enables eigendecomposition to reconstruct equivalence classes and navigate across them. This tool can facilitate local and task-agnostic explainability in Computer Vision and Natural Language Processing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to understand how Transformer models see the world. By using math, we can figure out what’s going on inside these powerful models. We can even use this method to help us understand why they make certain decisions. This is important because it will allow us to use Transformers in more creative and flexible ways. |
Keywords
» Artificial intelligence » Natural language processing » Transformer
