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There are limitations in learning language from text alone. Therefore, recentfocus has been on developing multimodal models. However, few benchmarks existthat can measure what language models learn about language from multimodaltraining. We hypothesize that training on a visual modality should improve onthe visual commonsense knowledge in language models. Therefore, we introducetwo evaluation tasks for measuring visual commonsense knowledge in languagemodels and use them to evaluate different multimodal models and unimodalbaselines. Primarily, we find that the visual commonsense knowledge is notsignificantly different between the multimodal models and unimodal baselinemodels trained on visual text data.

What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge