How might LLMs store facts | Chapter 7, Deep Learning
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/iGuwZTHWb6DFY
Anthropic posts about superposition referenced near the end:
https://transformer-circuits.p....ub/2022/toy_model/in
https://transformer-circuits.p....ub/2023/monosemantic
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/NfFST5Mio7BCA
Getting started in mechanistic interpretability
https://www.neelnanda.io/mecha....nistic-interpretabil
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-transf....ormer-interp.streaml
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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https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/
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The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcam....p.com/album/the-musi
https://open.spotify.com/album..../1dVyjwS8FBqXhRunaG5
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