Mechanistic interpretability focuses on understanding how a model processes information internally to produce its outputs. This involves analyzing the internals of the model, such as the behavior of neurons, attention heads, and weight patterns, to identify interpretable mechanisms or algorithms that explain the model’s decisions.
https://en.wikipedia.org/wiki/Mechanistic_interpretability
https://www.anthropic.com/research/team/interpretability
https://transformer-circuits.pub/2021/framework/index.html
https://transformer-circuits.pub/
https://transformerlensorg.github.io/TransformerLens/
https://distill.pub/2020/circuits/zoom-in/
https://github.com/gauravfs-14/awesome-mechanistic-interpretability
https://github.com/TransformerLensOrg/TransformerLens
https://github.com/callummcdougall/sae_vis
Bridging the Black Box: A Survey on Mechanistic Interpretability in AI