Do LLMs “think” in a similar way to humans? Or is it totally different?
Maybe it’s a good idea to listen to someone who publishes papers on this very subject, and is a professor of both philosophy and psychiatry and directs an Institute for Cognitive Science. That person is Dr. Chandra Sripada and his insights are fascinating.
Sean Carroll (interviewer, scientist and science communicator) says this interview made him lean towards the answer being “yes, they think like humans” whereas previously he favored the opposite view.
These are completely orthogonal things.
We train in bursts (“epochs”) because empirically it leads to more rapid convergence on an acceptable error minimum.
The fact that a scoring function is needed is almost a tautology. You have to have some differentiable function to say what direction is lower error. Typically you segment your training corpus, and use the scoring function to get the error for one batch at a time to a certain threshold before proceeding to the next batch. Then go to the first batch and push the error bar even lower, and so on.
Kinda like tightening nuts on a car wheel. You don’t fully tighten the first one first; you have to wiggle the system into its final configuration.
We’re comparing this to the observable behaviors of humans, though. Humanity engages in unsupervised learning without a scoring function (let alone a differentiable one) and, as a result, can not only train on the fly, but can also find its own ways to self-evaluate its learning. Humans also have hormones which change how the brain works even further.
This is also all disregarding the ability for the human mind to become faster and more accurate on recall when it has more knowledge to search in a field. LLMs can’t recall very effectively, instead becoming less effective the more information it needs to search.