RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation

• URL: http://arxiv.org/abs/2603.25804v1
• Date: Thu, 26 Mar 2026 18:11:46 GMT
• Title: RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation
• Abstract summary: Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains.<n>However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed.<n>We introduce textbftextttRealChart2Code, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets.
• Score: 44.54920245253688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce \textbf{\texttt{RealChart2Code}}, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets and featuring tasks with clear analytical intent. Crucially, it is the first benchmark to systematically evaluate chart generation from large-scale raw data and assess iterative code refinement in a multi-turn conversational setting. Our comprehensive evaluation of 14 leading VLMs on \texttt{RealChart2Code} reveals significant performance degradation compared to simpler benchmarks, highlighting their struggles with complex plot structures and authentic data. Our analysis uncovers a substantial performance gap between proprietary and open-weight models and confirms that even state-of-the-art VLMs often fail to accurately replicate intricate, multi-panel charts. These findings provide valuable insights into the current limitations of VLMs and guide future research directions. We release the benchmark and code at \url{https://github.com/Speakn0w/RealChart2Code}.

Related papers

• ChartAnchor: Chart Grounding with Structural-Semantic Fidelity [19.798612765001746]
  Chart grounding refers to the bidirectional alignment between a chart's visual appearance and the structured semantics.<n>ChartAnchor is a benchmark of 8k+ chart-table-code triples spanning 30 chart types drawn from diverse real-world and augmented sources.<n>A multi-level evaluation framework integrates semantic validation, stylistic analysis, and perceptual metrics to assess both structural and content-level correctness.
  arXiv  Detail & Related papers  (2025-11-30T18:28:09Z)
• ChartAB: A Benchmark for Chart Grounding & Dense Alignment [17.16234793106]
  We introduce a novel "ChartAlign Benchmark (ChartAB)" to provide a comprehensive evaluation of vision-language models (VLMs)<n>By incorporating a novel two-stage inference workflow, the benchmark can further evaluate VLMs capability to align and compare elements/attributes across two charts.<n>Our analysis of evaluations reveals new insights into their perception biases, weaknesses, robustness, and hallucinations in chart understanding.
  arXiv  Detail & Related papers  (2025-10-30T17:56:31Z)
• PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization [82.96200364977737]
  We introduce PlotCraft, a new benchmark featuring 1k challenging visualization tasks.<n>PlotCraft is structured around seven high-level visualization tasks and encompasses 48 distinct chart types.<n>It is the first to systematically evaluate both single-turn generation and multi-turn refinement across a diverse spectrum of task complexities.
  arXiv  Detail & Related papers  (2025-10-15T10:14:39Z)
• BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning [51.472854950300416]
  We propose BigCharts, a dataset creation pipeline that generates visually diverse chart images.<n>Unlike purely synthetic datasets, BigCharts incorporates real-world data, ensuring authenticity and visual diversity.<n>By introducing novel reward signals specifically designed for chart reasoning, our approach enhances model robustness and generalization.
  arXiv  Detail & Related papers  (2025-08-13T13:39:17Z)
• ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation [51.297873393639456]
  ArtifactsBench is a framework for automated visual code generation evaluation.<n>Our framework renders each generated artifact and captures its dynamic behavior through temporal screenshots.<n>We construct a new benchmark of 1,825 diverse tasks and evaluate over 30 leading Large Language Models.
  arXiv  Detail & Related papers  (2025-07-07T12:53:00Z)
• GLBench: A Comprehensive Benchmark for Graph with Large Language Models [41.89444363336435]
  We introduce GLBench, the first comprehensive benchmark for evaluating GraphLLM methods in both supervised and zero-shot scenarios. GLBench provides a fair and thorough evaluation of different categories of GraphLLM methods, along with traditional baselines such as graph neural networks.
  arXiv  Detail & Related papers  (2024-07-10T08:20:47Z)
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis [50.972595036856035]
  We present a code that successfully replicates results from six popular and recent graph recommendation models. We compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. By investigating the information flow from users' neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure.
  arXiv  Detail & Related papers  (2023-08-01T09:31:44Z)
• Open Graph Benchmark: Datasets for Machine Learning on Graphs [86.96887552203479]
  We present the Open Graph Benchmark (OGB) to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics.
  arXiv  Detail & Related papers  (2020-05-02T03:09:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.