arXiv AI

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.

arXiv AI

Oct 2

Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation

The paper introduces Frame‑Evidence Co‑Adaptation (FECA), an evidence‑adaptive visual planning framework for generating analytical charts in multimodal deep research. FECA treats chart generation as an iterative interaction between visual frames and retrieved evidence, allowing frames to be guided, revised, or dropped based on evidence availability. Experiments on 100 real‑world research topics demonstrate that FECA improves numerical fidelity while maintaining report quality and chart utility.

By Yuxin Yue, Yingchen Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Xueqi Cheng

multimodal

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 AI

Oct 1

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

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

Oct 1

ChartDensity-Bench: Benchmarking MLLMs for Numerical Data Reconstruction under Visual Density

ChartDensity-Bench is a benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to reconstruct structured numerical data from scientific charts that vary in visual density. The benchmark uses charts paired with source-level ground-truth data and systematically changes the number of simultaneously presented charts (k = 1, 3, 6, 9) to assess how density affects reconstruction performance. A multi‑dimensional evaluation framework measures structural reliability, reconstruction completeness, parseability, and numerical fidelity, revealing that numerical reconstruction generally worsens as visual density increases, with varying degrees of degradation across different models.

By Xinhe Wu, Yadong Jin

llms nlp multimodal benchmarks

arXiv AI

Jun 10

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.

By Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Sumitra Ganesh, Manuela Veloso

llms agents nlp multimodal benchmarks

arXiv AI

Oct 15

Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis

arXiv:2610.14173v1 Announce Type: cross Abstract: Digital libraries increasingly host scientific publications whose figures encode primary research findings, yet making this visual content searchable...

By Daniel J. Evans, J. P. Naiman, JooYoung Seo, J. Stephen Downie

llms nlp multimodal

arXiv Computer Vision

Sep 15

Structure-Token Evidence-Anchored Reasoning for Scientific Chart Understanding

arXiv:2609.13267v1 Announce Type: new Abstract: Scientific charts encode quantities in axes, legends, and geometric marks, yet large vision-language models still treat them as natural photographs. Vi...

By Alberlucia Rafael Soarez, Camila Ferreira, Daniel Kim, Mariana Costa, Alejandro Torres

llms computer-vision multimodal safety

arXiv Computation and Language

Sep 23

Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?

arXiv:2609.26208v1 Announce Type: new Abstract: Data visualization is central to analytical reasoning, but real-world analysis increasingly requires language-driven interactive interfaces rather than...

By Mizanur Rahman, Aaryaman Kartha, Enamul Hoque Prince

llms nlp benchmarks

arXiv AI

Jul 29

Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualization

arXiv:2607. 25021v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can connect visualization patterns to external causes, consequences, and domain knowledge, but the evidential basis of these interpretations is often unclear.

By Ishrat Jahan Eliza, Md Dilshadur Rahman

llms multimodal

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

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

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