

Well, you could maybe sort of train it not to generate āall men are catsā, but then that might also prevent it from making the more correct generalization āall cats are mortalā or even completely valid generalizations like combing āall men are mortalā and āSocrates is manā to get āSocrates is mortalā.
Just wanted to say that that ātalā comes after āmorā when āsoc-rate-sā is in the near context and in agreement with the attention mechanism is a very different type of logic than what this phrasing implies. This is also in combination with the peculiarities of word embeddings (the technique by which the tokens are translated to numeric vectors) like how it has a hard time making something useful out of numbers, it uh gets uh complicated.
The monofacts thing seems very post hoc and way too abstracted in comparison, and also the amount of text that can be categorized as strictly true or false isnāt that big all things considered.
Still if the point was to formalize the very no-duh observation that a neural net isnāt supposed to output itās dataset verbatim at all times hence hallucinations, then fine, I guess. Their proposed sort of solution (controlled miscalibration) even amounts to forcing the model to generalize less by memorizing more, which used to be the opposite of why you would choose to use this type of topography.










Thatās mostly because outright admitting that the point of prediction markets was to make having the prediction gene profitable so they could get on with breeding a rationailst kwisatz haderach to fight the robot god on more equal terms wouldnāt fly with the lower level thetans and other exoterics.