Summary of Interpreting Attention Layer Outputs with Sparse Autoencoders, by Connor Kissane et al.
Interpreting Attention Layer Outputs with Sparse Autoencoders
by Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur Conmy, Neel Nanda
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 method decomposes the internal activations of trained transformers into sparse, interpretable features using Sparse Autoencoders (SAEs). This technique has been applied to MLP layers and residual streams in previous work. In this study, SAEs are trained on attention layer outputs, demonstrating a sparse, interpretable decomposition of transformer models with up to 2 billion parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are powerful machine learning models that can be difficult to understand. Researchers have found a way to break down the internal workings of these models into simpler parts using something called Sparse Autoencoders (SAEs). This helps us get a better grasp on how the model is working and why it’s making certain decisions. The method has been tested on different types of transformers and shown to be effective in understanding their inner workings. |
Keywords
» Artificial intelligence » Attention » Machine learning » Transformer
