Comment by immibis
9 months ago
"Draw a wine glass filled to the brim with wine" worked recently on image generators. They only knew about half-full wine glasses.
If you asked a multimodal system questions about the image it just generated, it would tell you the wine was almost overflowing out of the top of the glass.
But any trick prompt like this is going to start giving expected results once it gets well-known enough.
Late edit: Another one was the farmer/fox/chicken/cabbage/river problem, but you modify the problem in unexpected ways, by stating, for example, that the cabbage will eat the fox, or that the farmer can bring three items per trip. LLMs used to ignore your modifications and answer the original problem.
"But any trick prompt like this is going to start giving expected results once it gets well-known enough."
Which makes it difficult to fairly evaluate whether the models have actually gotten better at the feather/iron problem or if it just got enough samples of trick questions that it learned better, either naturally from the internet, or fed as part of the training data. I am fairly certain the training data has had "trick questions" like this added to it, because, I mean, why wouldn't it?
I have noticed in my playing with image AIs that they do seem more prone to getting dragged into local maxima when a human would know the prompt than the LLMs. Perhaps it's all the additional data in an image that reveals it.
> Another one was the farmer/fox/chicken/cabbage/river problem, but you modify the problem in unexpected ways, by stating, for example, that the cabbage will eat the fox, or that the farmer can bring three items per trip. LLMs used to ignore your modifications and answer the original problem.
This is still the case. Very few non-reasoning models can solve such variations correctly, even SOTA models. Worse yet, not only they confidently give wrong responses, but they often do so even when specifically told to use CoT, and they continue giving wrong answers in a loop even if you specifically point out where they are wrong.
Reasoning models do much better, though. E.g. QwQ-32b can solve it pretty reliably, although it takes a lot of tokens for it to explore the possibilities. But at least it can fairly consistently tell when it's doing something wrong and then backtrack.
One other example that befuddles even the reasoning models is frying-cubes-in-a-pan and equivalents, e.g. this version from Simple Bench:
> Beth places four whole ice cubes in a frying pan at the start of the first minute, then five at the start of the second minute and some more at the start of the third minute, but none in the fourth minute. If the average number of ice cubes per minute placed in the pan while it was frying a crispy egg was five, how many whole ice cubes can be found in the pan at the end of the third minute? Pick the most realistic answer option. A) 5 B) 11 C) 0 D) 20
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