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. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast 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

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