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I’m curious how this would work considering knowledge can be fuzzy.

Like if I’m out camping and I sit on a log or a rock those things are not what people usually think of as chairs but they can serve as chairs in that situation.

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You can get models to actually show that 'logs' could function as 'chairs.' You're forgetting that we humans also learn this as well, but we learn this in a much simpler manner than LLMs though so someone has to explicitly let models know what assumptions they can make. You get the LLM to write Prolog programs and learn associations in this manner. As the model gets better at logically modelling the problems - the solutions to prompted problems like this should get better.

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