The interesting part here isn't the convolution itself, it's how convolutional layers turn out to like "filters" or "detectors" for individual features. This is explained very well in the distill.pub article linked by GP.

We know the architecture of LLMs because we created it, but we don't yet have the same level of understanding about them, or the same quality of analytical tools for reasoning about them.