monosemanticity

This paper introduces 'Rosetta Neurons'—universal neurons across diverse neural networks—and shows they scale as a sublinear power law, becoming more selective and monosemantic with scale, enabling data filtering that nearly matches oracle performance.

This paper demonstrates that sparse autoencoders can extract interpretable features from Claude 3 Sonnet, a production-scale language model, addressing scalability concerns for dictionary learning. The features are multilingual, multimodal, and include safety-relevant concepts like deception and sycophancy, with causal influence on model outputs.