Not only is the inverse not generally true (as others have pointed out), their examples requires several mental leaps. "Who is Tom Cruise's mother? [A: Mary Lee Pfeiffer]" and the reverse "Who is Mary Lee Pfeiffer's son?" The word "mother" has no relationship to "son" in terms of the model, and so while the model might be able to infer a proximity relationship between "Tom Cruise" and "Mary Lee Pfeiffer" just because…
Pretraining could be reasonably expected to make it learn that mother/father and son/daughter are inverse relationships and Tom is usually a male name.
All of this context works because we build up an extensive model of the world through the course of our lifetimes. LLM models don't do that, they pattern match based on stats.
Somebody would have to decide each of these things is important and create training data sets for each of them. But we implicitly understand so much context about the world that it's practically impossible to document everything we know in the form that a model can actually learn from.