  The recent introduction of multimodal large language models (MLLMs) combine
the inherent power of large language models (LLMs) with the renewed
capabilities to reason about the multimodal context. The potential usage
scenarios for MLLMs significantly outpace their text-only counterparts. Many
recent works in visualization have demonstrated MLLMs' capability to understand
and interpret visualization results and explain the content of the
visualization to users in natural language. In the machine learning community,
the general vision capabilities of MLLMs have been evaluated and tested through
various visual understanding benchmarks. However, the ability of MLLMs to
accomplish specific visualization tasks based on visual perception has not been
properly explored and evaluated, particularly, from a visualization-centric
perspective.
  In this work, we aim to fill the gap by utilizing the concept of
visualization literacy to evaluate MLLMs. We assess MLLMs' performance over two
popular visualization literacy evaluation datasets (VLAT and mini-VLAT). Under
the framework of visualization literacy, we develop a general setup to compare
different multimodal large language models (e.g., GPT4-o, Claude 3 Opus, Gemini
1.5 Pro) as well as against existing human baselines. Our study demonstrates
MLLMs' competitive performance in visualization literacy, where they outperform
humans in certain tasks such as identifying correlations, clusters, and
hierarchical structures.
