  Chart question answering (CQA) is a crucial area of Visual Language
Understanding. However, the robustness and consistency of current Visual
Language Models (VLMs) in this field remain under-explored. This paper
evaluates state-of-the-art VLMs on comprehensive datasets, developed
specifically for this study, encompassing diverse question categories and chart
formats. We investigate two key aspects: 1) the models' ability to handle
varying levels of chart and question complexity, and 2) their robustness across
different visual representations of the same underlying data. Our analysis
reveals significant performance variations based on question and chart types,
highlighting both strengths and weaknesses of current models. Additionally, we
identify areas for improvement and propose future research directions to build
more robust and reliable CQA systems. This study sheds light on the limitations
of current models and paves the way for future advancements in the field.
