Aside:

I've found that 'not'[0] isn't something that LLMs can really understand.

Like, with us humans, we know that if you use a 'not', then all that comes after the negation is modified in that way. This is a really strong signal to humans as we can use logic to construct meaning.

But with all the matrix math that LLMs use, the 'not' gets kinda lost in all the other information.

I think this is because with a modern LLM you're dealing with billions of dimensions, and the 'not' dimension [1] is just one of many. So when you try to do the math on these huge vectors in this space, things like the 'not' get just kinda washed out.

This to me is why using a 'not' in a small little prompt and token sequence is just fine. But as you add in more words/tokens, then the LLM gets confused again. And none of that happens at a clear point, frustrating the user. It seems to act in really strange ways.

[0] Really any kind of negation

[1] yeah, negation is probably not just one single dimension, but likely a composite vector in this bazillion dimensional space, I know.