Publications
Open Problems in Mechanistic Interpretability
This review discusses the current frontier of mechanistic interpretability, which aims to understand the computational mechanisms underlying neural networks. While the field has made progress, many open problems remain, including the need for improved methods, better applications to specific goals, and engagement with socio-technical challenges.
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
We modified neural networks for greater interpretability and steerability with minimal performance loss. Each layer applies a quantization bottleneck, converting dense activation vectors into a discrete list of learned codes that are either on or off.
News
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
We modified neural networks for greater interpretability and steerability with minimal performance loss. Each layer applies a quantization bottleneck, converting dense activation vectors into a discrete list of learned codes that are either on or off.