Summary of Attention Meets Post-hoc Interpretability: a Mathematical Perspective, by Gianluigi Lopardo et al.
Attention Meets Post-hoc Interpretability: A Mathematical Perspective
by Gianluigi Lopardo, Frederic Precioso, Damien Garreau
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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 As a machine learning educator writing for a technical audience not specialized in the subfield, I generate a medium-difficulty summary. This paper mathematically studies simple attention-based architectures to understand the differences between post-hoc and attention-based explanations. The results show that post-hoc methods can capture more useful insights than examining attention weights alone. This research provides meaningful insights into the internal behavior of transformer models and has implications for their applications in various domains, including natural language processing and computer vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how a machine learning model makes predictions. You could look at the “attention” mechanism inside the model, which helps it focus on important parts of your data. But does this attention really explain why the model made those predictions? This paper explores this question by studying a simple attention-based architecture and comparing two ways to get explanations: looking at the attention itself, or using a separate method to understand what’s going on inside the model. The results show that these two approaches give different answers, but one way can actually help you learn more from your data. |
Keywords
* Artificial intelligence * Attention * Machine learning * Natural language processing * Transformer
