> Show me progress on Winograd schema and I'd be impressed.

The paper evaluated Winograds: https://arxiv.org/pdf/2005.14165.pdf#page=16

i think you are assuming that what is happening under the hood is that a human-inputted sentence is being parsed into a grammar. it is not.

I know that it isn't. That's part of the problem. There is no attempt to generate some sort of structure that can be interpreted semantically and reasoned about by the model. The model just operates on the input superficially and statistically. That's why there has been virtually no progress on trivial tasks such as answering:

"I took the water bottle out of the backpack so that it would be [lighter/handy]"

What is lighter and what is handy? No amount of stochastic language manipulation gets you the answer, you need to understand some rudimentary physics to answer the question, and as a precondition, you need a grammar or ontology.

Have you tried feeding this to GPT and seeing if it continues it in a way that reveals understanding?

It sounds like you're saying "It doesn't work because it can't work", but you haven't actually shown that it doesn't work.

yes, I have. You can paste these into the website of the Allen Institute for AI, yourself here. (https://demo.allennlp.org/reading-comprehension/MjE1MzE1Mg==)

In the example above it guesses wrongly, but again this is not surprising because it can't possibly get the right answer (other than by chance). The solution here cannot be found by correlating syntax, you can only answer the question if you understand the meaning of the sentence. That's what these schemas are constructed for.

The problem for me was how to formulate the sentence in a way so that the natural next word would reveal the thing the network had modelled.

edit: Retracted a test where it seemed to know which to select, because further tries revealed it was random.

edit: I did some more tries, and it does seem to be somewhat random, but the way it continues the sentence does seem to indicate that it has some form of operational model. It's just hard to prompt it in a way that it is "forced" to reveal which of the two it's talking about. Also, it seems to me its coherence range is too short in GPT-2. I would love to try this with GPT-3.

FWIW, I fed this into AIDungeon (running on OpenAI) and got this back: “The bottle is definitely lighter than the pack because you can throw it much further away than you can carry the pack. You continue on into the night and come to an intersection.”