Comment by kennykartman
13 hours ago
Ah ah I was curious about that! I wonder if (when? if not already) some company is using some version of this in their training set. I'm still impressed by the fact that this benchmark has been out for so long and yet produce this kind of (ugly?) results.
Because no one cares about optimizing for this because it's a stupid benchmark.
It doesn't mean anything. No frontier lab is trying hard to improve the way its model produces SVG format files.
I would also add, the frontier labs are spending all their post-training time on working on the shit that is actually making them money: i.e. writing code and improving tool calling.
The Pelican on a bicycle thing is funny, yes, but it doesn't really translate into more revenue for AI labs so there's a reason it's not radically improving over time.
+1 to "it's a stupid benchmark".
You can always suggest a new one ;)
Why stupid? Vector images are widely used and extremely useful directly and to render raster images at different scales. It’s also highly connected with spacial and geometric reasoning and precision, which would open up a whole new class of problems these models could tackle. Sure, it’s secondary to raster image analysis and generation, but curious why it would be stupid to persue?
I suspect there is actually quite a bit of money on the table here. For those of us running print-on-demand workflows, the current raster-to-vector pipeline is incredibly brittle and expensive to maintain. Reliable native SVG generation would solve a massive architectural headache for physical product creation.
It shows that these are nowhere near anything resembling human intelligence. You wouldn't have to optimize for anything if it would be a general intelligence of sorts.
Here's a pencil and paper. Let's see your SVG pelican.
3 replies →
It would be trivial to detect such gaming, tho. That's the beauty of the test, and that's why they're probably not doing it. If a model draws "perfect" (whatever that means) pelicans on a bike, you start testing for owls riding a lawnmower, or crows riding a unicycle, or x _verb_ on y ...
It could still be special-case RLHF trained, just not up to perfection.
It’d be difficult to use in any automated process, as the judgement for how good one of these renditions is, is very qualitative.
You could try to rasterize the SVG and then use an image2text model to describe it, but I suspect it would just “see through” any flaws in the depiction and describe it as “a pelican on a bicycle” anyway.