Summary of Analyzing (in)abilities Of Saes Via Formal Languages, by Abhinav Menon et al.
Analyzing (In)Abilities of SAEs via Formal Languages
by Abhinav Menon, Manish Shrivastava, David Krueger, Ekdeep Singh Lubana
First submitted to arxiv on: 15 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 focuses on using autoencoders to extract interpretable and disentangled features from neural network representations in the text domain. The authors train sparse autoencoders (SAEs) on synthetic testbeds of formal languages, finding that latents often emerge in the learned features. However, they also find that performance is highly sensitive to inductive biases in the training pipeline. To address this, the authors propose an approach that promotes learning of causally relevant features in their formal language setting. They use Dyck-2, Expr, and English PCFG as models trained on formal languages, and train SAEs under various hyperparameter settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a special kind of AI model called an autoencoder to understand how neural networks work in the text domain. The researchers train these models on fake test cases that look like real language, and they find that some patterns emerge. However, they also discover that the performance of these models depends heavily on how they are trained. To fix this, the authors suggest a new approach that helps the models learn more useful patterns in the text. They use three different types of text to test their ideas. |
Keywords
» Artificial intelligence » Autoencoder » Hyperparameter » Neural network
