  Joint vision-language models have shown great performance over a diverse set
of tasks. However, little is known about their limitations, as the high
dimensional space learned by these models makes it difficult to identify
semantic errors. Recent work has addressed this problem by designing highly
controlled probing task benchmarks. Our paper introduces a more scalable
solution that relies on already annotated benchmarks. Our method consists of
extracting a large set of diverse features from a vision-language benchmark and
measuring their correlation with the output of the target model. We confirm
previous findings that CLIP behaves like a bag of words model and performs
better with nouns and verbs; we also uncover novel insights such as CLIP
getting confused by concrete words. Our framework is available at
https://github.com/MichiganNLP/Scalable-VLM-Probing and can be used with other
multimodal models and benchmarks.
