Clarifying Features Through Interpretability in Parameter Spaces

What's the next step forward in interpretability? In this episode, I chat with Lee Sharkey about his proposal for detecting computational mechanisms within neural networks: Attribution-based Parameter Decomposition, or APD for short.

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Transcript: https://axrp.net/episode/2025/06/03/episode-41-lee-sharkey-attribution-based-parameter-decomposition.html

Topics we discuss, and timestamps:

0:00:41 APD basics

0:07:57 Faithfulness

0:11:10 Minimality

0:28:44 Simplicity

0:34:50 Concrete-ish examples of APD

0:52:00 Which parts of APD are canonical

0:58:10 Hyperparameter selection

1:06:40 APD in toy models of superposition

1:14:40 APD and compressed computation

1:25:43 Mechanisms vs representations

1:34:41 Future applications of APD?

1:44:19 How costly is APD?

1:49:14 More on minimality training

1:51:49 Follow-up work

2:05:24 APD on giant chain-of-thought models?

2:11:27 APD and "features"

2:14:11 Following Lee's work

Lee links (Leenks):

X/Twitter: https://twitter.com/leedsharkey

Alignment Forum: https://www.alignmentforum.org/users/lee_sharkey

Research we discuss:

Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition: https://arxiv.org/abs/2501.14926

Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html

Towards a unified and verified understanding of group-operation networks: https://arxiv.org/abs/2410.07476

Feature geometry is outside the superposition hypothesis: https://www.alignmentforum.org/posts/MFBTjb2qf3ziWmzz6/sae-feature-geometry-is-outside-the-superposition-hypothesis

Episode art by Hamish Doodles: hamishdoodles.com