ChartNet: 一个百万级、高质量的多模态数据集，用于稳健的图表理解
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
AI总结：
ChartNet通过生成150万张不同图表样本，提升图表解释和推理能力，包含代码、图像、表格、自然语言和问答数据，支持多模态对齐，公开可用。
摘要：
理解图表需要模型联合推理几何视觉模式、结构化数值数据和自然语言——这是当前视觉语言模型（VLMs）仍有限的能力。我们介绍了ChartNet，一个高质量、百万级的多模态数据集，旨在推进图表解释和推理。ChartNet利用新的代码引导合成管道生成150万张图表样本，涵盖24种图表类型和6种绘图库。每个样本由五个对齐的组件组成：绘图代码、渲染图表图像、数据表格、自然语言摘要和带有推理的问答，提供细粒度的跨模态对齐。为了捕捉图表理解的完整范围，ChartNet还包含专门的子集，涵盖人工标注数据、真实世界数据、安全性和基础性。此外，一个严格的质量过滤管道确保了图表表示在视觉保真度、语义准确性和多样性方面。在ChartNet上进行微调能显著提升基准测试结果，证明其作为大规模监督数据集的实用性。作为同类最大开源数据集，ChartNet旨在支持具有稳健和可推广的数据可视化理解能力的基础模型的发展。该数据集可在https://huggingface.co/datasets/ibm-granite/ChartNet公开获取。
英文摘要：
Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a novel code-guided synthesis pipeline to generate 1.5 million diverse chart samples spanning 24 chart types and 6 plotting libraries. Each sample consists of five aligned components: plotting code, rendered chart image, data table, natural language summary, and question-answering with reasoning, providing fine-grained cross-modal alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at https://huggingface.co/datasets/ibm-granite/ChartNet
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。
