Audio note: this article contains 31 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description.
Lewis Smith*, Sen Rajamanoharan*, Arthur Conmy, Callum McDougall, Janos Kramar, Tom Lieberum, Rohin Shah, Neel Nanda
* = equal contribution
The following piece is a list of snippets about research from the GDM mechanistic interpretability team, which we didn’t consider a good fit for turning into a paper, but which we thought the community might benefit from seeing in this less formal form. These are largely things that we found in the process of a project investigating whether sparse autoencoders were useful for downstream tasks, notably out-of-distribution probing.
Outline:
(01:08) TL;DR
(02:38) Introduction
(02:41) Motivation
(06:09) Our Task
(08:35) Conclusions and Strategic Updates
(13:59) Comparing different ways to train Chat SAEs
(18:30) Using SAEs for OOD Probing
(20:21) Technical Setup
(20:24) Datasets
(24:16) Probing
(26:48) Results
(30:36) Related Work and Discussion
(34:01) Is it surprising that SAEs didn't work?
(39:54) Dataset debugging with SAEs
(42:02) Autointerp and high frequency latents
(44:16) Removing High Frequency Latents from JumpReLU SAEs
(45:04) Method
(45:07) Motivation
(47:29) Modifying the sparsity penalty
(48:48) How we evaluated interpretability
(50:36) Results
(51:18) Reconstruction loss at fixed sparsity
(52:10) Frequency histograms
(52:52) Latent interpretability
(54:23) Conclusions
(56:43) Appendix
The original text contained 7 footnotes which were omitted from this narration.
---
First published:
March 26th, 2025
Source:
https://www.lesswrong.com/posts/4uXCAJNuPKtKBsi28/sae-progress-update-2-draft
---
Narrated by TYPE III AUDIO.
---
Lewis Smith*, Sen Rajamanoharan*, Arthur Conmy, Callum McDougall, Janos Kramar, Tom Lieberum, Rohin Shah, Neel Nanda
* = equal contribution
The following piece is a list of snippets about research from the GDM mechanistic interpretability team, which we didn’t consider a good fit for turning into a paper, but which we thought the community might benefit from seeing in this less formal form. These are largely things that we found in the process of a project investigating whether sparse autoencoders were useful for downstream tasks, notably out-of-distribution probing.
TL;DR
- To validate whether SAEs were a worthwhile technique, we explored whether they were useful on the downstream task of OOD generalisation when detecting harmful intent in user prompts
- [...]
Outline:
(01:08) TL;DR
(02:38) Introduction
(02:41) Motivation
(06:09) Our Task
(08:35) Conclusions and Strategic Updates
(13:59) Comparing different ways to train Chat SAEs
(18:30) Using SAEs for OOD Probing
(20:21) Technical Setup
(20:24) Datasets
(24:16) Probing
(26:48) Results
(30:36) Related Work and Discussion
(34:01) Is it surprising that SAEs didn't work?
(39:54) Dataset debugging with SAEs
(42:02) Autointerp and high frequency latents
(44:16) Removing High Frequency Latents from JumpReLU SAEs
(45:04) Method
(45:07) Motivation
(47:29) Modifying the sparsity penalty
(48:48) How we evaluated interpretability
(50:36) Results
(51:18) Reconstruction loss at fixed sparsity
(52:10) Frequency histograms
(52:52) Latent interpretability
(54:23) Conclusions
(56:43) Appendix
The original text contained 7 footnotes which were omitted from this narration.
---
First published:
March 26th, 2025
Source:
https://www.lesswrong.com/posts/4uXCAJNuPKtKBsi28/sae-progress-update-2-draft
---
Narrated by TYPE III AUDIO.
---
Images from the article:
![Latent firing frequency histograms for Gated, JumpReLU and TopK SAEs. Unlike Gated SAEs, which use a L1 penalty that penalizes large latent activations, JumpReLU (middle) and TopK (bottom) SAEs exhibit high-frequency latents: latents that fire on 10% or more of tokens (i.e. that lie to the right of the dotted vertical line).]()
![Reconstruction loss vs L0 for the various SAE architectures and loss functions used in our experiment. The quadratic-frequency penalty (QF loss) has slightly worse reconstruction loss at any given sparsity than standard JumpReLU SAEs (L0 loss), but still compare favourably versus Gated and TopK SAEs.]()
![Latent firing frequency histograms for JumpReLU SAEs trained with a standard L0 loss (top) or quadratic-f</truncato-artificial-root>]()
Mark as Played
Popular Podcasts
Spooky Podcasts from iHeartRadio
Whether you’re a scaredy-cat or a brave bat, this collection of episodes from iHeartPodcasts will put you in the Halloween spirit. Binge stories, frights, and more that may keep you up at night!
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.