| I think you are taking a very major assumption that LLMs are deterministic infact they are exactly opposite. They are probabilistic systems. - They do not transpile Rough English to Deterministic English. Infact they do not do any transpiling at all. - LLMs learn the probabilities of words in the dataset for all contexts from that dataset (context is a ordered set of words). This is called training. - Once training is done, LLMs can generate text given a prompt and the probabilities that it has learned. The analogy of LLMs being auto-complete on steroids is very apt. - Whether the text generated by LLMs is factual or not is purely coincidental. I would highly recommend watching and working though the code (if possible) of NanoGPT by Andrej Karpathy. https://www.youtube.com/watch?v=kCc8FmEb1nY A lot of things which seem like magic about LLMs will get demystified. Now one can argue that LLMs are showing human like reasoning/intelligence/sentience as an emergent behavior. This is hard to argue against because all these terms are extremely hard to define. IMO, the only emergent behavior that LLMs are showing is the output they generate looks like it might have been generated by a human which should not be surprising given that LLMs like ChatGPT has trained on a large amount of human written text available on internet. |