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 humanwritten 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 DE-PLOT, which translates the image of a plot or chart to a linearized table. The output of DE-PLOT can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DEPLOT, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DEPLOT endto-end on this task. DEPLOT can then be used off-the-shelf together with LLMs in a plugand-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