Seonglae ChoCausal abstraction
Circuit Discovery Methods
Circuit Discovery Usage
Zoom In: An Introduction to Circuits
By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.
https://distill.pub/2020/circuits/zoom-in/
curve circuit (2020)
Curve Circuits
Reverse engineering the curve detection algorithm from InceptionV1 and reimplementing it from scratch.
https://distill.pub/2020/circuits/curve-circuits/
Stanford video, Mechanistic interpretability that aims for causal grounding.
Causal Mechanistic Interpretability (Stanford lecture 1) - Atticus Geiger
How can we use the language of causality to understand and edit the internal mechanisms of AI models?
Atticus Geiger (Goodfire) gives a guest lecture on applying frameworks and tools from causal modeling to understand LLMs and other neural networks in Surya Ganguli's Stanford course APPPHYS 293.
00:00 - Intro
01:51 - Activation steering (e.g. Golden Gate Claude)
10:23 - Causal mediation analysis (understanding the contribution of an intermediate component)
21:42 - Causal abstraction methods (explaining a complex causal system with a simple one)
26:11 - Interchange interventions
40:46 - Distributed Alignment Search
54:54 - Lookback mechanisms: a case study in designing counterfactuals
Read more about our research: https://www.goodfire.ai/research
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https://www.youtube.com/watch?v=78Xa8VkH7-g&pp=0gcJCU0KAYcqIYzv
