I may be missing something as I don't claim to be as smart as LeCun, but to me that probabilistic argument is not even wrong. The "probability e that a produce

I may be missing something as I don't claim to be as smart as LeCun, but to me that probabilistic argument is not even wrong. The "probability e that a produced token takes us outside of the set of correct answers" is likely to vary so wildly due to a plethora of factors like filler words vs. keywords, hard vs. easy parts of the question, previous tokens generated (I know abuse of statistical independence assumptions is common and often tolerated, but here it's doing a lot of heavy lifting), parts of the answer that you can express in many ways vs. concrete parts that you must get exactly right, etc. that I don't think simplifying it as he does makes any sense. Yes, I know, abstraction is useful, models are always simplifications, that probability doesn't need to be even close to a constant for the gist of the argument to stand. But everything can be bad in excess and in this case, the simplification is extreme. One can very easily imagine a long answer where the bulk of that probability is concentrated into a single word, while the rest have near-zero probability. Under such circumstances, I don't think his model is meaningful at all. Connecting with your comment, if someone made that kind of claim about humans, I suppose most people would find it ridiculous (or at the very least, meaningless/irrelevant). With LLMs we find it more palatable, because we are primed to think about LLMs in terms of "probability of generating the next token". But I don't think it really makes much more sense for LLMs than for humans, as the problems with the argument are more in how language works than in how the words are generated.