I like the token window bit. I don't really like the probability bit, because it kinda alludes that OpenAI just built a huge probability map of all N-grams (N=8000) and called it a day. Which incidentally would also imply that a lot of N-grams just don't exist in the training data, causing the model to completely halt when someone says something unexpected. But that's not the case - instead we convert words into a lo…
What should I google to understand how a word is encoded as a vector and then vector turned back into word(s)?