> Here is what this argument amounts to:

> "Humans are, frankly, not very interseting. You change the things you tell them and you get different responses? What a surprise!"

Maybe try making an argument that doesn't involve twisting what I say. I notice you haven't responded to my other post where I clarified what my core point is.

If you wrote a paper saying that people respond to different stimuli with different responses, I would in fact say that paper is not interesting. Just as the paper about AIs creating different outputs from different inputs is not interesting.

That isn't a statement about humans, just as my other statement wasn't a statement about AI. It's a statement about the paper.

In fact, I do think ChatGPT and other LLMs are very interesting. They're just not beyond human understanding.

Again: LLMs are very interesting. The paper you posted, isn't interesting.

> I'm not sure what your point is. I wish you'd just read the paper sigh.

I did read the paper, and as long as you accuse me of not reading the paper, my only response is going to be that I did read the paper. It would be a better use of everyone's time if you refrained from ad hominem attacks.

> Quote from the paper:

> > Emergent few-shot prompted tasks are also unpredictable in the sense that these tasks are not explicitly included in pre-training, and we likely do not know the full scope of few-shot prompted tasks that language models can perform.

1. As mentioned before, they're using a pretty specific meaning of "emergent".

2. The word "explicitly" is doing a lot of work there. If you've got a massive training dataset and you don't explicitly include arithmetic, but you include, for example, Wikipedia, which contains a metric fuckton of arithmetic, then maybe it's not so surprising that your model learns something about arithmetic.

As with "emergent abilities", this is poor communication--and as with "emergent abilities", I think it's intentional. They're writing a paper that's likely to attract attention, because that's how you get funding for future research. But since they know that it wouldn't the paper also has to pass academic scrutiny, they include weasel-words like "explicitly" so that what they're saying is technically true. To the non-academic eye, this looks like they're claiming the abilities are surprising, but to one practiced in reading this sort of fluff, it's clear that the abilities aren't surprising. They're only "surprising in the sense that..." which is a much narrower claim. In fact, the claim they're making amounts to, "Emergent few-shot prompted tasks are also unpredictable in the sense that you can't predict them if you don't bother to look at what's in your training data".

Likewise with the second half of the sentence. Obviously we don't know all the few-shot prompted tasks the models can perform: it's trivial to prove that's an infinite set. But that doesn't mean that, for given few-shot prompted tasks and a look through the training data, you can't predict whether the model can perform it.

The paper is full of these not-quite interesting claims. Perhaps more directly in line with your point, the paper says: "We have seen that a range of abilities—in the few-shot prompting setup or otherwise—have thus far only
been observed when evaluated on a sufficiently large language model. Hence, their emergence cannot be
predicted by simply extrapolating performance on smaller-scale models." That's again obviously true: obviously, if you look only at the size of the models and don't look at the training data, you won't be able to predict what's cached in the model. If you don't look at the inputs, and you'll be surprised by the outputs!

I don't blame the authors for doing this by the way--it's just part of the game of getting your research published. If they didn't do this, we might not have read the paper because it might never have gotten published. Don't hate the player, hate the game.