  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 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 DePlot, we standardize the plot-to-table task by establishing
unified task formats and metrics, and train DePlot end-to-end on this task.
DePlot can then be used off-the-shelf together with LLMs in a plug-and-play
fashion. Compared with a SOTA model finetuned on more than >28k data points,
DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over
finetuned SOTA on human-written queries from the task of chart QA.
