That's an interesting analogy, because I've always thought of the hidden states (and weights and biases) of an LLMs as a compressed version of the training data.
The Newtonian model makes provably less accurate predictions than Einsteinian (yes, I'm using a different example), so while still useful in many contexts where accuracy is less important, the number of parameters it requires doesn't much matter when looking for the one true GUT.
My understanding, again as a filthy computationalist, is that an accurate model of the real bonafide underlying architecture of the universe will be the simplest possible way to accurately predict anything. With the word "accurately" doing all the lifting.
As always: https://www.sas.upenn.edu/~dbalmer/eportfolio/Nature%20of%20...
I'm sure there are decreasingly accurate, but still useful, models all the way up the computational complexity hierarchy. Lossy compression is, precisely, using one of them.
(discussed here: https://news.ycombinator.com/item?id=34724477 )
Reiterating again, we can lose a lot of data (have incomplete data) and have a perfectly visible jpeg (or MP3, same thing).
