Abstract:

The development of multimodal large models has transformed visualization charts from static information carriers into important objects for model understanding, reasoning, generation, and safety evaluation. This review summarizes recent advances in visualization and visual analytics in the era of large models, focusing on two major directions: visualization chart understanding and misleading visualization analysis. The former investigates how models recover chart structures, data relations, and visual encodings from different input forms, including bitmap images, vector graphics, and D3 charts, and further supports chart question answering, visual grounding, style design, and the generation of titles and descriptions. The latter examines how misleading charts emerge from data selection, visual encoding, textual explanation, and communication context, with emphasis on taxonomy, evaluation of visual language models, automatic detection, explainable analysis, interactive correction, and safety defense. Existing studies indicate that both chart understanding and misleading visualization analysis rely on reliable alignment among data, visual structures, text, and semantics. Future research should develop unified chart representations, fine-grained evidence grounding, trustworthy generation, human-AI collaborative correction, and visualization defense mechanisms to enhance the safety of large models.

Key words: data visualization, chart understanding, multimodal large models, misleading visualization, visual analytics