9 citations · 12 across the 14 of their papers we have counts for
8 papers · 1 filter
Interpreting Language Model Parameters
Lucius Bushnaq, Dan Braun, Oliver Clive-Griffin +5
Mechanistic interpretability aims to uncover how neural networks use their parameters to implement their impressive neural algorithms. Although previous work has uncovered substant…
Functional Degeneracy in Neural Networks: Measurement and Pruning
Maria Matveev, Pascal Esser, Ayush Bharadwaj +2
A central question in modern machine learning is how much a trained model can be compressed without changing its behavior, to reduce the memory, compute and energy required to depl…
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…
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…
Open Problems in Mechanistic Interpretability
Lee Sharkey, Bilal Chughtai, Joshua Batson +26
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goa…
The Local Interaction Basis: Identifying Computationally-Relevant and Sparsely Interacting Features in Neural Networks
Lucius Bushnaq, Stefan Heimersheim, Nicholas Goldowsky-Dill +7
Mechanistic interpretability aims to understand the behavior of neural networks by reverse-engineering their internal computations. However, current methods struggle to find clear…
Using Degeneracy in the Loss Landscape for Mechanistic Interpretability
Lucius Bushnaq, Jake Mendel, Stefan Heimersheim +5
Mechanistic Interpretability aims to reverse engineer the algorithms implemented by neural networks by studying their weights and activations. An obstacle to reverse engineering ne…