alt Hacker NewsYes, and I believe my point still stands. We thoroughly understand the principle by which a loaded die can be intentionally biased despite not being able to predict the outcome of any given throw due to the system in question being a chaotic one.
In contrast, we do not understand LLMs in the same way (nor biological brains). Claiming that anything of that nature is simply biased towards coherent output seems entirely reductive to me - the question is how such coherence arises in the first place. There is no meaning encoded or computation performed by the particular pathway a die travels through the chaotic landscape.
Sure an argument can be made that it's "just" a next token predictor thus how is it really any different from a markov model? Yet the output is not even remotely the same.
> In contrast, we do not understand LLMs in the same way
From my point of view, (not a ML researcher), it’s due to the magic of numbers. The same thing happens with computer vision and neural networks. There’s a bunch of magic weights that get created which has no meaning by themselves, but computing them does help with detecting objects.
So if you take words, derives them into tokens, use the attention techniques to extract the “coherency” aspect, it’s no wonder you can replicate “coherency”. Add reinforcement learning to that to increase towards certain aspects like correct code syntax and you have heavily loaded the dice again.
We have used maths to model chemistry, biology, and physics, as well as economics and sociologic phenomena. Then we use maths (more specifically logic and set theory) to usher in the age of information and computing. Now you want us to act surprised that maths, through ML, can model language.
Maybe further down the line, we can have a simpler set of formulas for language coherency, but for now we have to make to with using the whole internet and a bazillion watts of power to guess the weights for the generic ML model.