It's not about whether the word is important (as you understand language), but whether the word frequently appears near all those other words.

Nobody is out there asking the Internet whether their non-allergy is dangerous. But the question next door to that one has hundreds of answers, so that's what this thing is paying attention to. If the question is asked a thousand times with the same answer, the addition of one more word can't be that important, right?

This behavior reveals a much more damning problem with how LLMs work. We already knew they didn't understand context, such as the context you and I have that peanut allergies are common and dangerous. That context informs us that most questions about the subject will be about the dangers of having a peanut allergy. Machine models like this can't analyze a sentence on the basis of abstract knowledge, because they don't understand anything. That's what understanding means. We knew that was a weakness already.

But what this reveals is that the LLM can't even parse language successfully. Even with just the context of the language itself, and lacking the context of what the sentence means, it should know that "not" matters in this sentence. But it answers as if it doesn't know that.