Safety, alignment & interpretability· 2024
Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
On the reading lists
- #315 in the Top 500 (2026 edition)
- #37 in Safety, alignment & interpretability (2026 edition)
Why it matters
Scaled interpretable feature extraction to a production language model and demonstrated causal feature steering.
Condensed from the expert lists' notes.
The record
- First released
- 2024 (from oracle lists)
- Citations
- 898 citations, as counted by
Semantic Scholar (non-commercial licence) - Named by
- both expert lists
- Score
- 0.434 · computed rank 315 · consensus 0.46, must-read 0.5, citations percentile 0.702 (2020s · safety), awards 0, existing lists 0. Method
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and
Semantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.