Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously. However, their capacity to query information depicted in visual charts and engage in reasoning based on the queried contents remains under-explored. In this paper, to comprehensively and rigorously benchmark the ability of the off-the-shelf MLLMs in the chart domain, we construct ChartX, a multi-modal evaluation set covering 18 chart types, 7 chart tasks, 22 disciplinary topics, and high-quality chart data. Besides, we develop ChartVLM to offer a new perspective on handling multi-modal tasks that strongly depend on interpretable patterns, such as reasoning tasks in the field of charts or geometric images. We evaluate the chart-related ability of mainstream MLLMs and our ChartVLM on the proposed ChartX evaluation set. Extensive experiments demonstrate that ChartVLM surpasses both versatile and chart-related large models, achieving results comparable to GPT-4V. We believe that our study can pave the way for further exploration in creating a more comprehensive chart evaluation set and developing more interpretable multi-modal models. Both ChartX and ChartVLM are available at: https://github.com/UniModal4Reasoning/ChartVLM

翻译：近期，多种通用多模态大语言模型（MLLMs）不断涌现。然而，它们在查询视觉图表中的信息并基于查询内容进行推理的能力仍有待深入探索。为全面且严谨地评测现有MLLMs在图表领域的能力，我们构建了ChartX——一个覆盖18种图表类型、7类图表任务、22个学科主题及高质量图表数据的多模态评测集。此外，我们开发了ChartVLM，为处理高度依赖可解释模式的多模态任务（如图表或几何图像中的推理任务）提供了新视角。基于所提出的ChartX评测集，我们评估了主流MLLMs及ChartVLM的图表处理能力。大量实验表明，ChartVLM在性能上超越了通用型及图表专用型大模型，取得了与GPT-4V相当的结果。我们相信，本研究可为构建更全面的图表评测集与开发更具可解释性的多模态模型奠定基础。ChartX与ChartVLM均已开源：https://github.com/UniModal4Reasoning/ChartVLM