arXiv Machine Learning

What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation

arXiv:2607. 04726v1 Announce Type: cross Abstract: Chart-to-code generation is commonly trained with supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target.

arXiv AI

Aug 21

CharTool: Tool-Integrated Visual Reasoning for Chart Understanding

arXiv:2604. 02794v2 Announce Type: replace Abstract: Charts are ubiquitous in scientific and financial literature for presenting structured data.

By Situo Zhang, Yifan Zhang, Zichen Zhu, Da Ma, Lei Pan, Danyang Zhang, Zihan Zhao, Lu Chen, Kai Yu

llms agents reinforcement-learning multimodal benchmarks data-engineering

arXiv AI

3d ago

ChartRevise: A Dataset and Evaluation Protocol for Exact Chart Editing via Code

ChartRevise is a new dataset and evaluation protocol designed for exact chart editing via code. It contains 92,438 records covering 344 edit types across 20 chart types and three plotting libraries, built using the grammar of graphics and source‑program checks to ensure applicability. The protocol measures atomic requirement completion, detects gratuitous changes and missed coupled updates, and combines these with execution and rendering success to determine exact‑edit success.

By Jiaxiang Tang, Yi Zhou, Chad DeLuca, Rogerio Feris, Ahmed Khalil Omran, Zhi-Li Zhang, Pengyuan Li, Ali Anwar

fine-tuning multimodal benchmarks

arXiv AI

Aug 5

ChartAnno: Evaluating MLLMs for Chart Annotation Generation

arXiv:2608. 03464v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored.

By Zhenghan Chen, Zekai Shao, Lidan Tan, Xin Lin, Xingchen Zeng, Yi Shan, Ziyue Lin, Xiaoliang Fu, Xinyuan Liu, Yuetong Guo, Fen Wang, Bongshin Lee, Siming Chen

llms multimodal benchmarks

arXiv AI

3d ago

From Charts to Code: A Hierarchical Benchmark for Multimodal Models

arXiv:2510.17932v5 Announce Type: replace-cross Abstract: We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (...

By Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang

llms multimodal benchmarks

arXiv AI

Sep 15

ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation

arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large...

By Zhenghan Chen, Zekai Shao, Lidan Tan, Xin Lin, Xingchen Zeng, Yi Shan, Ziyue Lin, Xiaoliang Fu, Xinyuan Liu, Yuetong Guo, Fen Wang, Bongshin Lee, Siming Chen

llms multimodal benchmarks

Hugging Face Trending Papers

Aug 4

ChartAnno: Evaluating MLLMs for Chart Annotation Generation

Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored. Annotating charts is a common yet challenging communicative task, requiring models to infer intended messages, interpret chart semantics, and place appropriate textual or graphical elements.

llms multimodal benchmarks

arXiv AI

Aug 28

DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?

DEEPCHART is a new benchmark that evaluates large language models (LLMs) on faithful data‑science chart generation. It contains 1,482 expert‑annotated instances from scientific papers, financial filings, and ecosystem reports, and assesses chart creation through an Extract–Reason–Visualize pipeline. Experiments show that while LLMs can produce visually plausible charts, they frequently hallucinate data at the extraction and reasoning stages, especially in long, noisy, and multimodal contexts.

By Jiahui tang, Kuicai Dong, Dexun Li, Hongchao Gu, Haocheng Yu, Wei Han, Chen Zhang, Yong Liu, Hao Wang, Enhong Chen

llms multimodal benchmarks safety

arXiv Computer Vision

Sep 22

ChartJudgeBench: Evaluating LMM Judges for Chart-to-Code Generation

arXiv:2609.24210v1 Announce Type: new Abstract: Building strong chart-to-code systems increasingly relies on reinforcement learning, whose effectiveness depends critically on the quality of the rewar...

By Lijian Wu, Henry Hengyuan Zhao, Zijian Zhang, Jiahao Tang, Jiajun Wu, Alex Jinpeng Wang

agents reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 30

Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework

arXiv:2606. 29808v1 Announce Type: cross Abstract: Chart data extraction, which reverse-engineers data tables from chart images, is essential for reproducibility, analysis, retrieval, and redesign.

By Yuchen He, Peizhi Ying, Liqi Cheng, Kuilin Peng, Yuan Tian, Dazhen Deng, Yingcai Wu

llms multimodal benchmarks

arXiv Machine Learning

Aug 28

Chart2SVG: Editable SVG Generation from Raster Chart Images

Chart2SVG is a multimodal large language model that transforms static raster chart images into editable SVGs enriched with semantic structure. By embedding chart‑specific semantic tokens into a vision‑language framework and training on the Beagle+ dataset of 33K distilled chart samples, the model captures both geometric primitives and their functional roles. The resulting SVGs are visually accurate and structurally consistent, and the accompanying Chart Structure Graph (CSG) exposes visual dependencies for interactive exploration, chart repurposing, and layout reuse.

By Jinning Cui, Lu Chen, Haoyan Shi, Yue He, Chenglong Wang, Mengyu Zhou, Weidong Huang, Yunhai Wang

llms multimodal

arXiv Computer Vision

Sep 22

LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventions

arXiv:2609.24172v1 Announce Type: new Abstract: Legends are fundamental to chart understanding, as reliable interpretation requires correctly binding legend entries to corresponding visual marks. Whi...

By Xinnuo Zhang, Zhike Tang, Jing Xu, Haoyuan Zhao, Weikai Yang

llms fine-tuning multimodal benchmarks

arXiv AI

Aug 18

Who Leads Now? Token-Level Modality Arbitration for Chart-to-Code Generation

arXiv:2608. 15510v1 Announce Type: new Abstract: Chart-to-code generation requires a model to read the fine-grained visual details of a chart and write executable code that reproduces it.

By Qinghao Fu, Yarong Wang, Shunlei Ning, Yilin Wang, Shunwen Bai, Xinda Wang, Jiaotuan Wang, Yinan Nie, Wei Zhou

reinforcement-learning fine-tuning multimodal benchmarks

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