matching humans on these kind of quantities and you know way of grinding perplexities is gonna use the same random baseline so it ends up just matching the vocabulary size for like a standard model so random guessing would be a plexi of 50k and one way of thinking about perplexity is as like a branching factor of language so flexi to the N is like the space of possible generations of length then how many your model might assign so you have a perplexity of 10 for a language model and you generate you know to two like