The idea that an AI lab would pay a small army of human artists to create training data for $animal on $transport just to cheat on my stupid benchmark delights me.
Would it not be better to have 100 such tests "Pelican on bicycle", "Tiger on stilts"..., and generate them all for every new model but only release a new one each time. That way you could show progression across all models, attempts at benchmaxxing would be more obvious.
Given the crazy money and vying for supremacy among AI companies right now it does seem naive to belive that no attempt at better pelicans on bicycles is being made. You can argue "but I will know because of the quality of ocelots on skateboards" but without a back catalog of ocelots on skateboards to publish its one datapoint and leaves the AI companies with too much plausible deniability.
The pelicans-on-bicycles is a bit of fun for you (and us!) but it has become a measure of the quality of models so its serious business for them.
There is an assymetry of incentives and high risk you are being their useful idiot. Sorry to be blunt.
Or indeed do the Markov chain conceptual slip. Pelican on bicycle, badger on stool, tiger on acid. Pelican on bicycle is definitely cooked, though: people know it and it's talked about in language.
For every combination of animal and vehicle? Very unlikely.
The beauty of this benchmark is that it takes all of two seconds to come up with your own unique one. A seahorse on a unicycle. A platypus flying a glider. A man’o’war piloting a Portuguese man of war. Whatever you want.
No, not every combination. The question is about the specific combination of a pelican on a bicycle. It might be easy to come up with another test, but we're looking at the results from a particular one here.
None of this works if the testers are collaborating with the trainers. The tests ostensibly need to be arms-length from the training. If the trainers ever start over-fitting to the test, the tester would come up with some new test secretly.