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.
TL;DR---
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:
Source:
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
View all episodes
By LessWrong
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.
TL;DR---
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:
Source:
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
26,353 Listeners
2,453 Listeners
8,507 Listeners
4,165 Listeners
95 Listeners
1,611 Listeners
10,018 Listeners
95 Listeners
516 Listeners
5,526 Listeners
15,849 Listeners
554 Listeners
130 Listeners
92 Listeners
472 Listeners