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In this paper, we propose a table and image generation task to verify how theknowledge about entities acquired from natural language is retained in Vision &Language (V&L) models. This task consists of two parts: the first is togenerate a table containing knowledge about an entity and its related image,and the second is to generate an image from an entity with a caption and atable containing related knowledge of the entity. In both tasks, the model mustknow the entities used to perform the generation properly. We created theWikipedia Table and Image Generation (WikiTIG) dataset from about 200,000infoboxes in English Wikipedia articles to perform the proposed tasks. Weevaluated the performance on the tasks with respect to the above researchquestion using the V&L model OFA, which has achieved state-of-the-art resultsin multiple tasks. Experimental results show that OFA forgets part of itsentity knowledge by pre-training as a complement to improve the performance ofimage related tasks.

Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models