https://chatgpt.com/share/683f3e7d-0dfc-8005-b6c9-99e3d39ff4...
https://chatgpt.com/share/683f3e49-9c58-8005-99a6-c3a919838b...
This seems like something a VLM should handle very easily, but instead I got pure nonsense.
Not if its training data doesn't include braille as first class but has lots of braille signage with bad description (e.g., because people assumed the accompanying English matches the braille.)
This could very well be the kind of mundane AI bias problem that the x-risk and tell-me-how-to-make-WMD concerns have shifted concerns about problems in AI away from.
Also I think the authors used the API, and maybe there are differences between the API and chatgpt.com behavior...
The system prompt may still make a difference though.
o3 Chat is also similarly wrong, saying {4}.
I can replicate the flag examples from Figure 15 in the paper, if not the Adidas one from Figure 9: https://chatgpt.com/share/683f7c3a-b318-8011-9759-c495db2556... it even confirms its wrong answer when asked to check again.
For example: "The animal in the image is a chicken, and it appears to have four legs. However, chickens normally have only two legs. The presence of four legs suggests that the image may have been digitally altered or artificially generated."
I don't have a good explanation for why I got different results.