Summary of Evaluating Synthetic Activations Composed Of Sae Latents in Gpt-2, by Giorgi Giglemiani et al.
Evaluating Synthetic Activations composed of SAE Latents in GPT-2
by Giorgi Giglemiani, Nora Petrova, Chatrik Singh Mangat, Jett Janiak, Stefan Heimersheim
First submitted to arxiv on: 23 Sep 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 study explores the sensitivity of machine learning models to perturbations in early layers, aiming to compare real and synthetic activations generated from Sparse Auto-Encoders (SAEs). By controlling for sparsity and cosine similarity of SAE latents, researchers found that synthetic activations closely resemble real ones. This suggests that SAE latents possess internal structure and geometric/statistical properties beyond a simple “bag of latents.” The study observes less pronounced activation plateaus in synthetic compared to real activations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning models to understand how they work. Researchers want to know if the things that make the model’s output look like it does come from what’s called “Sparse Auto-Encoders” (SAEs). They did some experiments and found that SAEs are more complicated than just mixing together simple pieces. This means that when we use these models, they’re not just doing a simple job, but rather doing something more complex. |
Keywords
» Artificial intelligence » Cosine similarity » Machine learning
