You have probably seen examples of LLMs doing the "mirror test", i.e. identifying themselves in screenshots and referring to the screenshot from the first person. That is a genuinely novel question as an "LLM mirror test" wasn't a concept that existed before about a year ago.

Elephant mirror tests existed, so it doesn’t seem all that novel when the word “elephant” could just be substituted for the word “LLM”?

The question isn't about universal novelty, but whether the prompt/context is novel enough such that the LLM answering competently demonstrates understanding. The claim of parroting is that the dataset contains a near exact duplicate of any prompt and so the LLM demonstrating what appears to be competence is really just memorization. But if an LLM can generalize from an elephant mirror test to an LLM mirror test in an entirely new context (showing pictures and being asked to describe it), that demonstrates sufficient generalization to "understand" the concept of a mirror test.

How do you know it’s the one generalizing?

Likely there has been at least one text that already does that for say dolphin mirror tests or chimpanzee mirror teats.

It's not exactly difficult to come up with a question that's so unusual the chance of it being in the training set is effectively zero.

Can you provide some examples of these genuinely unique questions?

I'm not sure what you mean by "genuinely." But in the coding context LLMs answer novel questions all the time. My codebase uses components and follows patterns that an LLM will have seen before, but the actual codebase is unique. Yet, the LLM can provide detailed explanations about how it works, what bugs or vulnerabilities it might have, modify it, or add features to it.

It must not have existed prior in any text database whatsoever.

It certainly wasn't. The codebase is thousands of lines of bespoke code that I just wrote.

Which pretty much every line in it was written similarly somewhere else before, including an explanation and is somehow included in the massive data set it was trained on.

So far i have asked the AI some novel questions and it came up with novel answers full of hallucinated nonsense, since it copied some similarly named setting or library function and replaced a part of it's name with something i was looking for.

And this training data somehow includes an explanation of how these individual lines (with variable names unique to my application) work together in my unique combination to produce a very specific result? I don't buy it.

And...

> pretty much

Is it "pretty much" or "all"? The claim that the LLM simply has simply memorized all of its responses seems to require "all."

And as any programmer will tell you: they immediately devolve into "hallucinating" answers, not trying to actually reason about the world. Because that's what they do: they create statistically plausible answers even if those answers are complete nonsense.