Summary of Bilinear Mlps Enable Weight-based Mechanistic Interpretability, by Michael T. Pearce et al.
Bilinear MLPs enable weight-based mechanistic interpretability
by Michael T. Pearce, Thomas Dooms, Alice Rigg, Jose M. Oramas, Lee Sharkey
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 abstract presents a study on mechanistic understanding of how Multilayer Perceptrons (MLPs) perform computations in deep neural networks. The authors analyze bilinear MLPs, a type of Gated Linear Unit (GLU) without element-wise nonlinearity, which achieves competitive performance. By expressing bilinear MLPs as linear operations using third-order tensors, the weights can be analyzed to reveal interpretable low-rank structure across tasks like image classification and language modeling. This understanding is used to craft adversarial examples, uncover overfitting, and identify small language model circuits directly from the weights. The study demonstrates that bilinear layers serve as an interpretable drop-in replacement for current activation functions and that weight-based interpretability is viable for understanding deep-learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out how a type of computer program called Multilayer Perceptrons (MLPs) works. MLPs are used in artificial intelligence, but we don’t really understand how they make decisions. The authors looked at a special kind of MLP that doesn’t have some complicated parts, and they found that it can still do its job well. By looking at the weights (numbers) inside this type of MLP, they discovered that it’s possible to understand how the program makes decisions. This is important because it means we can create better artificial intelligence in the future. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Language model » Overfitting
