Scaling and Evaluating Sparse Autoencoders
by Anthropic · free · Last verified 2026-03-17
This paper from Anthropic scales sparse autoencoders (SAEs) to GPT-4-level models and provides rigorous evaluation methods for measuring dictionary quality, showing that SAE features are interpretable, monosemantic, and causally relevant to model behavior. The work establishes SAEs as a core tool for mechanistic interpretability at scale.
https://arxiv.org/abs/2406.04093 ↗C+
C+—Average
Adoption: BQuality: A+Freshness: ACitations: BEngagement: F
Specifications
- License
- Open Access
- Pricing
- free
- Capabilities
- feature-extraction, dictionary-learning, sparse-representation, monosemanticity
- Integrations
- Use Cases
- ai-safety, model-interpretability, feature-analysis, mechanistic-research
- API Available
- No
- Tags
- interpretability, sparse-autoencoders, mechanistic-interpretability, features, anthropic
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
57.7Adoption
60
Quality
91
Freshness
88
Citations
62
Engagement
0
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