3Blue1Brown | How might LLMs store facts | Chapter 7, Deep Learning @3blue1brown | Uploaded 1 month ago | Updated 22 minutes ago
Unpacking the multilayer perceptrons in a transformer, and how they may store facts
Instead of sponsored ad reads, these lessons are funded directly by viewers: 3b1b.co/support
An equally valuable form of support is to share the videos.
AI Alignment forum post from the Deepmind researchers referenced at the video's start:
alignmentforum.org/posts/iGuwZTHWb6DFY3sKB/fact-finding-attempting-to-reverse-engineer-factual-recall
Anthropic posts about superposition referenced near the end:
https://transformer-circuits.pub/2022/toy_model/index.html
https://transformer-circuits.pub/2023/monosemantic-features
Some added resources for those interested in learning more about mechanistic interpretability, offered by Neel Nanda
Mechanistic interpretability paper reading list
alignmentforum.org/posts/NfFST5Mio7BCAQHPA/an-extremely-opinionated-annotated-list-of-my-favourite
Getting started in mechanistic interpretability
neelnanda.io/mechanistic-interpretability/getting-started
An interactive demo of sparse autoencoders (made by Neuronpedia)
neuronpedia.org/gemma-scope#main
Coding tutorials for mechanistic interpretability (made by ARENA)
https://arena3-chapter1-transformer-interp.streamlit.app/
Sections:
0:00 - Where facts in LLMs live
2:15 - Quick refresher on transformers
4:39 - Assumptions for our toy example
6:07 - Inside a multilayer perceptron
15:38 - Counting parameters
17:04 - Superposition
21:37 - Up next
------------------
These animations are largely made using a custom Python library, manim. See the FAQ comments here:
3b1b.co/faq#manim
github.com/3b1b/manim
github.com/ManimCommunity/manim
All code for specific videos is visible here:
github.com/3b1b/videos
The music is by Vincent Rubinetti.
vincentrubinetti.com
vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u
------------------
3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.
Mailing list: 3blue1brown.substack.com
Twitter: twitter.com/3blue1brown
Instagram: instagram.com/3blue1brown
Reddit: reddit.com/r/3blue1brown
Facebook: facebook.com/3blue1brown
Patreon: patreon.com/3blue1brown
Website: 3blue1brown.com
Unpacking the multilayer perceptrons in a transformer, and how they may store facts
Instead of sponsored ad reads, these lessons are funded directly by viewers: 3b1b.co/support
An equally valuable form of support is to share the videos.
AI Alignment forum post from the Deepmind researchers referenced at the video's start:
alignmentforum.org/posts/iGuwZTHWb6DFY3sKB/fact-finding-attempting-to-reverse-engineer-factual-recall
Anthropic posts about superposition referenced near the end:
https://transformer-circuits.pub/2022/toy_model/index.html
https://transformer-circuits.pub/2023/monosemantic-features
Some added resources for those interested in learning more about mechanistic interpretability, offered by Neel Nanda
Mechanistic interpretability paper reading list
alignmentforum.org/posts/NfFST5Mio7BCAQHPA/an-extremely-opinionated-annotated-list-of-my-favourite
Getting started in mechanistic interpretability
neelnanda.io/mechanistic-interpretability/getting-started
An interactive demo of sparse autoencoders (made by Neuronpedia)
neuronpedia.org/gemma-scope#main
Coding tutorials for mechanistic interpretability (made by ARENA)
https://arena3-chapter1-transformer-interp.streamlit.app/
Sections:
0:00 - Where facts in LLMs live
2:15 - Quick refresher on transformers
4:39 - Assumptions for our toy example
6:07 - Inside a multilayer perceptron
15:38 - Counting parameters
17:04 - Superposition
21:37 - Up next
------------------
These animations are largely made using a custom Python library, manim. See the FAQ comments here:
3b1b.co/faq#manim
github.com/3b1b/manim
github.com/ManimCommunity/manim
All code for specific videos is visible here:
github.com/3b1b/videos
The music is by Vincent Rubinetti.
vincentrubinetti.com
vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u
------------------
3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.
Mailing list: 3blue1brown.substack.com
Twitter: twitter.com/3blue1brown
Instagram: instagram.com/3blue1brown
Reddit: reddit.com/r/3blue1brown
Facebook: facebook.com/3blue1brown
Patreon: patreon.com/3blue1brown
Website: 3blue1brown.com
