ChartAnchor: 图表接地与结构-语义保真
ChartAnchor: Chart Grounding with Structural-Semantic Fidelity
AI总结：
ChartAnchor提出一个综合图表接地基准测试，通过图表到代码生成和受控表格重建任务，评估模型在结构和数值层面的保真度，揭示MLLMs在数值精度和代码合成方面的局限性。
摘要：
近期多模态大语言模型（MLLMs）的进步突显了需要严格评估结构图表理解的基准测试的重要性。图表接地指的是图表的视觉外观与其结构语义之间的双向对齐。这项任务要求模型生成一个符号规范，以忠实捕捉图表的视觉和结构意图，同时恢复底层表格数据的精确值和关系。图表接地直接反映了模型在数值推理、多模态对齐和结构重建方面的能力，并具有重要的现实应用。现有的基准测试受限于图表多样性的狭窄、孤立的任务和不完整的评估框架，无法全面评估接地能力。为此，我们提出了ChartAnchor，一个包含8000多个图表-表格-代码三元组的综合基准测试，涵盖30种图表类型，来自多样化的现实世界和增强源。ChartAnchor引入了两个互补的任务：图表到代码生成和受控图表到表格重建，实现了视觉和数值保真的交叉验证。一个多级评估框架整合了语义验证、风格分析和感知度量，以评估结构和内容层面的正确性。在MLLMs上的广泛实验揭示了数值精度和代码合成方面的关键限制，强调了在表面感知之外进行结构推理的必要性。通过统一符号和数据驱动的接地，ChartAnchor建立了图表接地的严格基础，为在科学、金融和工业领域推进MLLMs提供了有意义的见解。
英文摘要：
Recent advances in multimodal large language models (MLLMs) highlight the need for benchmarks that rigorously evaluate structured chart comprehension. Chart grounding refers to the bidirectional alignment between a chart's visual appearance and its structured semantics. This task requires models to produce a symbolic specification that faithfully captures the chart's visual and structural intent, while also recovering the underlying tabular data with precise values and relationships. Chart grounding directly reflects a model's capabilities in numerical reasoning, multimodal alignment, and structural reconstruction, and has several important real-world applications. Existing benchmarks, constrained by narrow chart diversity, isolated tasks, and incomplete evaluation frameworks, fail to holistically assess grounding. To address this, we propose ChartAnchor, a comprehensive benchmark of 8k+ chart-table-code triples spanning 30 chart types drawn from diverse real-world and augmented sources. ChartAnchor introduces two complementary tasks: chart-to-code generation and controlled chart-to-table reconstruction, enabling cross-validation of visual and numerical fidelity. A multi-level evaluation framework integrates semantic validation, stylistic analysis, and perceptual metrics to assess both structural and content-level correctness. Extensive experiments on MLLMs reveal critical limitations in numerical precision and code synthesis, emphasizing the need for structured reasoning beyond surface-level perception. By unifying symbolic and data-driven grounding, ChartAnchor establishes a rigorous foundation for chart grounding, offering meaningful insights for advancing MLLMs in scientific, financial, and industrial domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
