Summary of Mambalrp: Explaining Selective State Space Sequence Models, by Farnoush Rezaei Jafari et al.
MambaLRP: Explaining Selective State Space Sequence Models
by Farnoush Rezaei Jafari, Grégoire Montavon, Klaus-Robert Müller, Oliver Eberle
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 paper introduces Mamba models, which have gained popularity in sequence modeling due to their efficient processing of long sequences in linear time. To ensure reliable use in real-world scenarios, the authors propose bringing explainability to the Mamba architecture using Layer-wise Relevance Propagation (LRP). They identify components causing unfaithful explanations and develop MambaLRP, a novel algorithm within the LRP framework that ensures stable relevance propagation. The proposed method achieves state-of-the-art explanation performance across various models and datasets. It also enables deeper inspection of Mamba architectures, uncovering biases and evaluating their significance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making complex AI models more understandable. Right now, these “Mamba” models are good at predicting things like language patterns, but it’s hard to figure out why they’re making certain predictions. The authors want to change that by adding a special tool called Layer-wise Relevance Propagation (LRP) to the Mamba model. This tool helps us see which parts of the model are most important for making predictions. The new algorithm is really good at explaining things and can even help us discover biases in the model. |
