Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question
  Answering and Summarization
Xuan Long Do Mohammad Hassanpour Ahmed Masry Parsa Kavehzadeh Enamul Hoque Shafiq Joty
A number of tasks have been proposed recently to facilitate easy access to
charts such as chart QA and summarization. The dominant paradigm to solve these
tasks has been to fine-tune a pretrained model on the task data. However, this
approach is not only expensive but also not generalizable to unseen tasks. On
the other hand, large language models (LLMs) have shown impressive
generalization capabilities to unseen tasks with zero- or few-shot prompting.
However, their application to chart-related tasks is not trivial as these tasks
typically involve considering not only the underlying data but also the visual
features in the chart image. We propose PromptChart, a multimodal few-shot
prompting framework with LLMs for chart-related applications. By analyzing the
tasks carefully, we have come up with a set of prompting guidelines for each
task to elicit the best few-shot performance from LLMs. We further propose a
strategy to inject visual information into the prompts. Our experiments on
three different chart-related information consumption tasks show that with
properly designed prompts LLMs can excel on the benchmarks, achieving
state-of-the-art.