Deep Papers LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic
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Jun 14, 2024 Delve into recent research on LLM interpretability with k-sparse autoencoders from OpenAI and sparse autoencoder scaling laws from Anthropic. Explore the implications for understanding neural activity and extracting interpretable features from language models.
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Why Interpretability Matters
- Mechanistic interpretability maps model internals to human concepts using feature extraction techniques like sparse autoencoders.
- Understanding these features aids alignment, safety, and more efficient model design.
Sparse Autoencoders Reveal Features
- Sparse autoencoders (SAEs) expand activations into a larger sparse feature space to reveal interpretable components.
- Training SAEs at production scale shows these features persist in real large models, not just toy models.
Follow SAE Scaling Laws
- Allocate more compute and increase training steps to improve SAE performance following observed scaling laws.
- Reduce learning rates as compute scales to achieve more efficient training.