Measuring Vision-Language STEM Skills of Neural Models
Jianhao Shen Ye Yuan Srbuhi Mirzoyan Ming Zhang Chenguang Wang
We introduce a new challenge to test the STEM skills of neural models. The
problems in the real world often require solutions, combining knowledge from
STEM (science, technology, engineering, and math). Unlike existing datasets,
our dataset requires the understanding of multimodal vision-language
information of STEM. Our dataset features one of the largest and most
comprehensive datasets for the challenge. It includes 448 skills and 1,073,146
questions spanning all STEM subjects. Compared to existing datasets that often
focus on examining expert-level ability, our dataset includes fundamental
skills and questions designed based on the K-12 curriculum. We also add
state-of-the-art foundation models such as CLIP and GPT-3.5-Turbo to our
benchmark. Results show that the recent model advances only help master a very
limited number of lower grade-level skills (2.5% in the third grade) in our
dataset. In fact, these models are still well below (averaging 54.7%) the
performance of elementary students, not to mention near expert-level
performance. To understand and increase the performance on our dataset, we
teach the models on a training split of our dataset. Even though we observe
improved performance, the model performance remains relatively low compared to
average elementary students. To solve STEM problems, we will need novel
algorithmic innovations from the community.