Yeah, it was probably patched. It could reason novel problems only of you ask it to pay attention to some particular detail a.k.a. handholding..

Same would happen with the the sheep and the wolf and the cabbage puzzle. If you l formulated similarly, there is a wolf and a cabbage without mentioning the sheep, it would summon up the sheep into existence at a random step. It was patched shortly after.

I’m not sure ‘patched’ is the right word here. Are you suggesting they edited the LLM weights to fix cabbage transportation and car wash question answering?

They might have further trained the model with these edgecases in the dataset

Whatever it was, that’s not real thinking, we can possibly patch all knowledge and even if we did, it would become crystallize somehow.

Absolutely not my area of expertise but giving it a few examples of what should be the expected answer in a fine-tuning step seems like a reasonable thing and I would expect it would "fix" it as in less likely to fall into the trap.

At the same time, I wouldn't be surprised if some of these would be "patched" via simply prompt rewrite, e.g. for the strawberry one they might just recognize the question and add some clarifying sentence to your prompt (or the system prompt) before letting it go to the inference step?

But I'm just thinking out loud, don't take it too seriously.

Used patched for lack of a better word. Not sure how they fix the edge cases for these types of fixes/patches or whatever they’re specifically called

What if it’s raining though? Car wash wouldn’t be open though it would waste gas