Intuitively, this seems like a good approach -- pare down the datasets to isolate the essential, if not rudimentary, elements. It feels like a good direction w

Intuitively, this seems like a good approach -- pare down the datasets to isolate the essential, if not rudimentary, elements. It feels like a good direction would be to "grow" these models from a linguistic kernel, then supply them with a more organized / reproducible "fact bank" / memory store. We try to mimic this right now with prompt engineering, but in the end it's always just word soup. Having layers to it, like a language synthesis layer, logic layer, memory layer -- where the logic is a constraint on the language, and the memory provides the working data -- it could lead to a model that hallucinates far less.