Comment by Sharlin

Surprisingly many people seem to believe LLMs cannot form any deeper world models beyond superficial relationships between words, even if figuring out a "hidden" model allows for a big leap in prediction performance – in this case, a hypothesis corresponding to chess rules happens to be give the best bang for the buck for predicting strings that have chess notation structure.

But the model could in principle just have learned a long list of rote heuristics that happen to predict notation strings well, without having made the inferential leap to a much simpler set of rules, and a learner weaker than a LLM could well have got stuck at that stage.

I wonder how well a human (or a group of humans) would fare at the same task and if they could also successfully reconstruct chess even if they had no prior knowledge of chess rules or notation.

(OTOH a GPT3+ level LLM certainly does know that chess notation is related to something called "chess", which is a "game" and has certain "rules", but to what extent is it able to actually utilize that information?)