How might LLMs store facts | Chapter 7, Deep Learning

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: 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: Anthropic posts about superposition referenced near the end: Some added resources for those interested in learning more about mechanistic interpretability, offered by Neel Nanda Mechanistic interpretability paper reading list Getting started in mechanistic interpretability An interactive demo of sparse autoencoders (made by Neuronpedia) #main Coding tutorials for mechanistic interpretability (made by ARENA) 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: #manim All code for specific videos is visible here: The music is by Vincent Rubinetti. ------------------ 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: Twitter: Instagram: Reddit: Facebook: Patreon: Website: