I'm not sure what you mean? As the length of a sequence increases (from word to n-gram to sentence to paragraph to ...), the probability that it actually ever appeared (in any corpus, whether that's a training set on disk, or every word ever spoken by any human even if not recorded, or anything else) quickly goes to exactly zero. That makes it computationally useless.

If we define perplexity in the usual way in NLP, then that probability approaches zero as the length of the sequence increases, but it does so smoothly and never reaches exactly zero. This makes it useful for sequences of arbitrary length. This latter metric seems so obviously better that it seems ridiculous to me to reject all statistical approaches based on the former. That's with the benefit of hindsight for me; but enough of Chomsky's less famous contemporaries did judge correctly that I get that benefit, that LLMs exist, etc.