Seems like there would be low hanging fruit in heavier pre processing then? Something deterministic like a reading level score. Or even a tiny model trained for the task to pick out good data?
An example: More than ten years ago a friend of mine was fascinated by the german edition of the book "A Cultural History of Physics" by Károly Simonyi. He scanned the book (600+ pages) and created a PDF (nearly) same layout.
Against my advice he used Adobe tools for it instead of creating an epub or something like DocBook.
The PDF looks great, but the text inside is impossible to use as training data for a small LLM. The lines from the two columns are mixed and a lot of spaces are randomly placed (makes it particularly difficult because mathematical formulas often appear in the text itself).
After many attempts (with RegEx and LLMs), I gave up and rendered each page and had a large LLM extract the text.
I have less concrete examples but my understanding is that dataset curation is for sure the way many improvements are gained at any model size. Unless you are building a frontier model, you can use a better model to help curate or generate that dataset for sure. TinyStories was generated with GPT-4 for example.
It's not sexy, it's not a breakthrough, but it does help.
At the big labs that makes sense. Bit more puzzled by why it isn’t used in the toy projects. Certainly more complexity but seems like it would make a big difference
Sure, it's a lot of data to sift through, and the time and cost to do so can be substantial. But if you are already planning on funneling all of that through a 1T LLM? You might as well pass the fragments through a small classifier before you do that.