That is to force it to form models about concepts.
It can’t make models about concepts. It can only make models about what words tend to follow other words. It has no understanding of the underlying concepts.
You can see that by asking them to apply their knowledge to something they haven’t seen before
That can’t happen because they don’t have knowledge, they only have sequences of words.
For example a cat is closer related to a dog than to a tractor.
The only way ML models “understand” that is in terms of words or pixels. When they’re generating text related to cats, the words they’re generating are closer to the words related to dogs than the words related to tractors. When dealing with images, it’s the same basic idea. But, there’s no understanding there. They don’t get that cats and dogs are related.
This is fundamentally different from how human minds work, where a baby learns that cats and dogs are similar before ever having a name for either of them.