Discovery of capability overhangs via wiki writing
Is there any prior writing about finding under-sampled latent space in a model and directing that behavior into documentation writing?
I was fixing cache invalidation and this page was the right thing at the right time to help me understand the solution to the problem: https://grokipedia.com/page/Cache_busting_in_Vite#troubleshooting
AFAIK, that collection of information is a new synthesis of many different bits of documentation, and presented in a way that got me to understanding faster and more completely than reading the disparate threads.
As a mechanism for probing the model, is this not generalizable? Given my "truly novel integration of existing data" assumption, is there a way to successfully sample under-explored latent spaces of the model and get interpretable results once you bump the output into the "wikipedia writer" direction?
If you could "diff" a model to find where the weights changed the most during training/tuning, you could distill down what the model has learned in an interpretable format.
Comments
Like did Grok generate that on its own two months ago? Did you tell it generate it? What happened?
No idea, I googled "cache busting in vite" and it was by far the most comprehensive result.
I am not too surprised, I get good answers about Vite from Google’s AI mode though Microsoft’s Copilot tends to do especially poorly on Vite: like an answer that should be “use vite-ignore” becomes a 10-line Vite plugin inlined into the vite.config.js that doesn’t work.