Summary of Sage: Scalable Ground Truth Evaluations For Large Sparse Autoencoders, by Constantin Venhoff et al.
SAGE: Scalable Ground Truth Evaluations for Large Sparse Autoencoders
by Constantin Venhoff, Anisoara Calinescu, Philip Torr, Christian Schroeder de Witt
First submitted to arxiv on: 9 Oct 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 This paper addresses a key challenge in machine learning interpretability: decomposing model activations into meaningful features using sparse autoencoders (SAEs). However, evaluating SAE quality is hindered by the lack of ground truth features. The authors introduce SAGE: Scalable Autoencoder Ground-truth Evaluation, a framework that scales to large state-of-the-art SAEs and models. SAGE can automatically identify task-specific activations and compute ground truth features at these points, reducing training overhead through a novel reconstruction method. The paper demonstrates the scalability of SAGE by evaluating SAEs on novel tasks using Pythia70M, GPT-2 Small, and Gemma-2-2 datasets. This framework paves the way for generalizable, large-scale evaluations of SAEs in interpretability research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make machines like computers learn from data without just doing random things. Right now, it’s hard to know if these machines are really learning or not because we don’t have a way to compare their answers to the right answers. The authors created a new tool called SAGE that can help us figure this out by giving us the correct answers for certain types of problems. This means we can test how well these machines do on different tasks and see if they’re really learning or not. The authors tested SAGE on several big datasets and showed that it works well, which is an important step towards making machines more useful and helpful. |
Keywords
» Artificial intelligence » Autoencoder » Gpt » Machine learning
