Now with llms we need way more that 6 dimensions so we can start thinking of assigning matrices to each 3d point for example. That allows us to increase the dimension from 3 to 3 + whatever the matrix dimension is.
We can visualize the matrices instead of having numbers as having colors for each entry, so they can be a sort of cube with each vowel being a different color.
Now you can start to visually intuit about how these massively high dimensional spaces can be formed of these colored matrices that can react to some input training data.
That's a start of an idea for intuiting things that might seem impossible to have an intuition about. I think visualization is a great way to start.
visualizign 100 dim matrix will not tell you how llm work. so what you even talking about.
Depending on your knowledge of math, I recommend starting with linear algebra, building an understanding of the equations and try to visualize more and more complex systems, then study llms to see how you can apply your linear algebra intuition to your understanding of llms.
VTK is a great toolkit for visualizing complex systems. 3 blue one brown on YouTube has other visuals that might help you.
It takes time but it's possible. Good luck.
> Visualizing large dimensional matrices is a way of starting to develop intuition about llms.
one last time before i disengage. how do you know this and what intuitions have you personally developed.
I've spent a great deal of time studying llms and NN architectures in general. This allows me to intuit things about them and that intuitive understanding for my comes mainly from visualizations.
Are you able to visualize much about llms? I ask because if not, starting by learning to visualize high dimensional complex systems is what I would personally recommend.
Check out this video for a really nice visualization into NN architecture https://youtu.be/QgH9sr7G13Q?si=iTn1FiFsYCZCJKE_
Like a cat who knows how to teleport so rapidly around the room that he becames a blur. All the while taking into account stuff falling around he knocked down.
Linking to a Twitter thread about training a small LLM to be good at Wordle is not an example of what I'm talking about. It might well be a useful task but it doesn't allow us to understand deeply what's happening.