Comment by vore

I mean, that is the fundamental architecture of the model and how it's explicitly designed: given an input embedding and output embedding, the model is trained over a massive corpus of text to optimize the probability for generating the next token in the sequence. Any other interpretation is secondary to that, no matter how impressive the results are.