That's the definition that matters. And as it's impossible to describe in words what it's like to experience qualia, and LLMs are trained on words, they cannot know what it's like. Even if images and sounds are included in their input training data, those are just arrays or sequences of numbers, not the experience of seeing and hearing.

We don't understand what those are or how they come about, so how does that help us objectively reason about whether a particular black box does or does not have them, especially when it behaves as though it does, beyond arbitrary I-know-it-when-I-see-it "things I don't like don't have the property" style declarations?

That doesn't really solve the problem. We can't conclusively say the models don't have qualia. Hell we don't know if a perfectly accurate atom-for-atom simulation of a human brain, would produce qualia.