u/shmeebzin r/ExperiencedDevs • Recommends
On “I have no idea how AI Works”
3 blue 1 brown has a great intro to LLMs video series that helped me understand at least at a high level how they work. Chapter 7 goes into how tokens can store facts.
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#transformer#multilayerperceptron#mechanisticinterpretability
How might LLMs store facts | Deep Learning Chapter 7
3Blue1Brown
8.7M subscribers • 45 videos on Hallway
- Recommended by 2 people: 2 in engineering communities.
- Linked in the description3b1b/manimManimCommunity/manim3b1b/videos
Intermediate. Assumes working knowledge.
2.3M views2 years ago2 recommendations
AI and machine learningComputer scienceTransformers and LLMsNeural networks and deep learningMath for machine learning
An explanation of how multilayer perceptrons inside a transformer model store and retrieve factual associations.
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-atte… Anthropic posts about superposition referenced near the end: transformer-circuits.pub/2022/toy_model/index.html 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-opin… 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) arena3-chapter1-transformer-interp.streamlit.app/ Звуковая дорожка на русском языке: Влад Бурмистров. Sections: - Where facts in LLMs live - Quick refresher on transformers - Assumptions for our toy example - Inside a multilayer perceptron - Counting parameters - Superposition - 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.comWhy it's here
2 people recommend thisWhat people say. Recommended as an excellent introduction to how large language models and high-dimensional vectors store factual information.
u/ProdigySimin r/typescript • Recommends
On “How to Implement a Cosine Similarity Function in TypeScript for Vector Comparison”
There's a good 3Blue1Brown video that explains how the high-dimensionality vectors store information, which might be a good starting point.
I think they do share a good amount of math in common with 3D Graphics vectors, but are operating on thousands of dimensions instead of 3, so you don't get to visualize it easily.