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README.md

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-ChartX presents two primary contributions. (1) To comprehensively and rigorously benchmark the ability of the off-the-shelf MLLMs in chart domain, we construct an evaluation set covering multi-modal (image, code, csv, text description), multi-task, multi-disciplinary, high-quality chart data, and evaluate the performance of mainstream MLLMs. (2) We develop ChartVLM, offering a new perspective on handling the multi-modal tasks that strongly depend on interpretable patterns such as reasoning tasks in the field of chart or geometric images.
+ChartX presents two primary contributions. 
+
+- (1) To comprehensively and rigorously benchmark the ability of the off-the-shelf MLLMs in chart domain, we construct an evaluation set covering multi-modal (image, code, csv, text description), multi-task, multi-disciplinary, high-quality chart data, and evaluate the performance of mainstream MLLMs.
+- (2) We develop ChartVLM, offering a new perspective on handling the multi-modal tasks that strongly depend on interpretable patterns such as reasoning tasks in the field of chart or geometric images.