[EricMausler]

I am still learning transformers, but I believe part of the issue may be that the weights do not necessarily correlate to things like "orangeness"

Instead of a transformer for each color, you have like 5 to 100 weights that represent some arbitrary combination of colors. Literally the arbitrariness is defined by the dataset and the number of weights allocated.

They may even represent more than just color.

So I am not sure if a weight is actually a "dial" like you are describing it, where you can turn up or down different qualities. I think the relationship between weights and features is relatively chaotic.

Like you may increase orangeness but decrease "cone shapedness" or accidentally make it identify deer as trees or something, all by just changing 1 value on 1 weight

[space_boy]

It is possible that the parameters, like weights in a machine learning model, interact to yield outcomes in a manner analogous to the interactions between genes in biological systems, which produce traits. These interactions involve complex interdependencies, so there really aren't 1 to 1 dials.