DePlot: One-shot visual language reasoning by plot-to-table translation

Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex human-written queries. This paper presents the first few(one)shot solution to visual language reasoning. We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text. The key in this method is a modality conversion module, named as DEPLOT, which translates the image of a plot or chart to a linearized table. The output of DEPLOT can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DE-PLOT, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DEPLOT end-to-end on this task. DE-PLOT can then be used off-the-shelf together with LLMs in a plug-and-play fashion. Compared with a SOTA model finetuned on thousands of data points, DEPLOT+LLM with just one-shot prompting achieves a 29.4% improvement over finetuned SOTA on human-written queries from the task of chart QA. 12 * Work done during Google internship. § Equal contributions.