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Just to make clear because it seems to come up a lot in some responses - I absolutely don't think (and never have) that chatgpt is intelligent, 'understands' what I'm saying to it or what it's saying to me (let alone is accurate!). Older chat bots were very prone to getting in weird loops, or sudden context/topic switches. Chatgpt doesn't do this very often, and I was wondering what the mechanism for keeping it's answers plausibly connected to the topic under discussion and avoiding grammatical cul-de-sacs.
I know it's just a model, I want to understand the difference between it's predictions and the predictions on my Android keyboard. Is it simply considering the entire previous text as it makes its predictions vs just the last few words? Why doesn't it occasionally respond with a hundred thousand word response? Many of the texts it's trained on are longer than it's usual responses. There seems to be some limits and guidance given either through its training data or its response training that guide it beyond "based on the texts I have seen, what is the most likely word." and I was curious if there was a summary what the blend of corpus based prediction and respinse feedback, etc. has been used.
Software engineer here, but not llm expert. I want to address one of the questions you had there.
An llm like chatgpt does some rudimentary level of pattern matching when it analyzes training data. So this is why it won't generate a giant blurb of text unless you ask it to.
Let's say for example one of its training inputs is a transcription of a conversation. That will be tagged "conversation" by a person. Then it will see that tag when analyzing hundreds of input texts that are conversations. Finally, the training algorithm writes down that "conversation" have responses of 1-2 sentences with x% likelyhood because that's what the transcripts did. Now if another of the training sets is "best selling novels" it'll store that "best selling novels have" responses" that are very long.
Chatgpt will probably insert a couple of tokens before your question to help it figure out what it's supposed to respond: "respond to the user as if you are in a casual conversation"
This will make the model more likely to output small answers rather than giving you a giant wall of text. However it is still possible for the model to respond with a giant wall of text if you ask something that would contradict the original instructions. (hence why jailbreaking models is possible)