From 3Blue1Brown.
Unpacking the multilayer perceptrons in a transformer, and how they may store facts
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AI Alignment forum post from the Deepmind researchers referenced at the video’s start:
https://www.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
https://www.alignmentforum.org/posts/NfFST5Mio7BCAQHPA/an-extremely-opinionated-annotated-list-of-my-favourite
Getting started in mechanistic interpretability
https://www.neelnanda.io/mechanistic-interpretability/getting-started
An interactive demo of sparse autoencoders (made by Neuronpedia)
https://www.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
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These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/
All code for specific videos is visible here:
https://github.com/3b1b/videos/
The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
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