7 papers
Targeted Recovery of Weight-Space Mechanisms From Neural Networks
Antoine Vigouroux, Lee Sharkey
Parameter decomposition (PD) decomposes neural networks into interpretable computational components that faithfully reflect the original network's operations. However, scaling PD t…
Stochastic Parameter Decomposition
Lucius Bushnaq, Dan Braun, Lee Sharkey
A key step in reverse engineering neural networks is to decompose them into simpler parts that can be studied in relative isolation. Linear parameter decomposition -- a framework t…
Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video
Sonia Joseph, Praneet Suresh, Lorenz Hufe +7
Robust tooling and publicly available pre-trained models have helped drive recent advances in mechanistic interpretability for language models. However, similar progress in vision…
AI Behind Closed Doors: a Primer on The Governance of Internal Deployment
Charlotte Stix, Matteo Pistillo, Girish Sastry +6
The most advanced future AI systems will first be deployed inside the frontier AI companies developing them. According to these companies and independent experts, AI systems may re…
Identifying Sparsely Active Circuits Through Local Loss Landscape Decomposition
Brianna Chrisman, Lucius Bushnaq, Lee Sharkey
Much of mechanistic interpretability has focused on understanding the activation spaces of large neural networks. However, activation space-based approaches reveal little about the…
Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition
Dan Braun, Lucius Bushnaq, Stefan Heimersheim +2
Mechanistic interpretability aims to understand the internal mechanisms learned by neural networks. Despite recent progress toward this goal, it remains unclear how best to decompo…