I'm getting impatient with criticisms of ML models that are already covered in the papers introducing the models. OP is basically trying to get it to do what the GPT3 paper calls zero-shot inference. In the paper, it's pretty bad at zero shot inference across the board. And given what it does and how it was trained, that's unsurprising. And the point they're trying to make (that it can fail spectacularly) is also covered in the paper.

It can do cool shit. It sucks at a lot of stuff. It's impressive and limited, but the hype train seems to only allow "it's nearly human level" or "it's awful." To everybody who is arguing about its capabilities without having read the paper yet, please read it. Then we can discuss stuff that hasn't already been covered more rigorously in the original paper. I don't know Davis, but I respect Marcus, and it seems like he's pushing back on the hype more than the actual model. Just not in a way that you couldn't glean from the paper itself (it almost always sucks on zero-shot), making it pretty disingenuous. Further, from the paper [0]:

> it does little better than chance when evaluated one-shot or even few-shot on some “comparison” tasks, such as determining if two words are used the same way in a sentence, or if one sentence implies another (WIC and ANLI respectively), as well as on a subset of reading comprehension tasks.

Maybe that's the curse of doing a thing that has broad implications. You can't fit the implications in a 10 page paper, so you write a 75 page paper. The blogosphere reads the first 10 pages (if even that), and because there's so much more to it that that introduction, they go on to argue about the rest of the implications without reading it. I'm sure Marcus and Davis have read it, but this criticism wouldn't be on the front page if the rest of everyone interested in this article had read the paper too.

[0] Language Models are Few-Shot Learners https://arxiv.org/abs/2005.14165