Before all this Machine Learning madness, while I was studying unsupervised learning I had the following thought:

To cluster is to create an ideia, a concept which encompasses a set of data points. A cluster might be bigger than the set of points with which was created and intersect with other clusters. This is still what I visualize when I see image models interpolating between "concepts".

Believing that all an LLM does is the likelihood of adjacent words is probably an oversimplification. My verdict about this topic is: I don't know and I'm totally confused about what interpolation and extrapolation mean in higher dimensions