ChartComplete: A Taxonomy-based Inclusive Chart Dataset
This provides a more inclusive benchmark for evaluating multimodal large language models in chart understanding, though it is incremental as it expands on existing datasets.
The authors tackled the limitation of existing chart understanding datasets by proposing ChartComplete, a dataset covering thirty chart types based on a visualization taxonomy, but it does not include a learning signal.
With advancements in deep learning (DL) and computer vision techniques, the field of chart understanding is evolving rapidly. In particular, multimodal large language models (MLLMs) are proving to be efficient and accurate in understanding charts. To accurately measure the performance of MLLMs, the research community has developed multiple datasets to serve as benchmarks. By examining these datasets, we found that they are all limited to a small set of chart types. To bridge this gap, we propose the ChartComplete dataset. The dataset is based on a chart taxonomy borrowed from the visualization community, and it covers thirty different chart types. The dataset is a collection of classified chart images and does not include a learning signal. We present the ChartComplete dataset as is to the community to build upon it.