This is a linkpost for Apollo Research's new interpretability paper:

"Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition".

We introduce a new method for directly decomposing neural network parameters into mechanistic components.

Motivation

At Apollo, we've spent a lot of time thinking about how the computations of neural networks might be structured, and how those computations might be embedded in networks' parameters. Our goal is to come up with an effective, general method to decompose the algorithms learned by neural networks into parts that we can analyse and understand individually.

For various reasons, we've come to think that decomposing network activations layer by layer into features and connecting those features up into circuits (which we have started calling 'mechanisms'[1]) may not be the way to go. Instead, we think it might be better to directly decompose a network's parameters into components by [...]

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Outline:

(00:27) Motivation

(01:56) What we do

(06:25) Future work

The original text contained 5 footnotes which were omitted from this narration.

The original text contained 1 image which was described by AI.

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First published:
January 25th, 2025

Source:
https://www.lesswrong.com/posts/EPefYWjuHNcNH4C7E/attribution-based-parameter-decomposition

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Narrated by TYPE III AUDIO.

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“Attribution-based parameter decomposition” by Lucius Bushnaq, Dan Braun, StefanHex, jake_mendel, Lee Sharkey

LessWrong (30+ Karma)