> Being terse and allowing for human interpretation is kind of the entire point of something being written like this. It's also to a certain extent why LLMs wo

> Being terse and allowing for human interpretation is kind of the entire point of something being written like this. It's also to a certain extent why LLMs work. When IBM Watson was playing Jeopardy, one of the game prompts was: > It was the anatomical oddity of U.S. gymnast George Eyser, who won a gold medal on the parallel bars in 1904 The man was missing a leg and used a prosthetic. Watson's output was, "What is leg?" At first it was regarded as correct. If a human said that you could conclude that they knew the answer. But then the judges decided not to give Watson the point because its output didn't provide enough specificity to prove that it understood the context. If you ask an LLM what kinds of things taste sweet it can give you examples like cotton candy or strawberries, but it has never actually tasted anything. All it knows is that the training data contains the association between those tokens. But the human reading the output knows what strawberries are, which is what allows the output to be meaningful.