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A number of tasks have been proposed recently to facilitate easy access tocharts such as chart QA and summarization. The dominant paradigm to solve thesetasks has been to fine-tune a pretrained model on the task data. However, thisapproach is not only expensive but also not generalizable to unseen tasks. Onthe other hand, large language models (LLMs) have shown impressivegeneralization capabilities to unseen tasks with zero- or few-shot prompting.However, their application to chart-related tasks is not trivial as these taskstypically involve considering not only the underlying data but also the visualfeatures in the chart image. We propose PromptChart, a multimodal few-shotprompting framework with LLMs for chart-related applications. By analyzing thetasks carefully, we have come up with a set of prompting guidelines for eachtask to elicit the best few-shot performance from LLMs. We further propose astrategy to inject visual information into the prompts. Our experiments onthree different chart-related information consumption tasks show that withproperly designed prompts LLMs can excel on the benchmarks, achievingstate-of-the-art.

Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization