mlsanityData to 7 Oct 2026

Does visualization help AI understand data?

arXiv 2507.18022 ↗First posted 24 Jul 2025cs.AI cs.HC cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Charts and graphs help people analyze data, but can they also be useful to AI systems? To investigate this question, we perform a series of experiments with two commercial vision-language models: GPT 4.1 and Claude 3.5. Across three representative analysis tasks, the two systems describe synthetic datasets more precisely and accurately when raw data is accompanied by a scatterplot, especially as datasets grow in complexity. Comparison with two baselines -- providing a blank chart and a chart with mismatched data -- shows that the improved performance is due to the content of the charts. Our results are initial evidence that AI systems, like humans, can benefit from visualization.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 24 Jul 2025.

Builds on

11 of 19 references.

References · 11

  1. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
  2. 2022Chart-to-Text: A Large-Scale Benchmark for Chart Summarizationcited by 166
  3. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  4. 2024Vision language models are blind: Failing to translate detailed visual features into wordscited by 144
  5. 2023VisText: A Benchmark for Semantically Rich Chart Captioningcited by 111
  6. 2024Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Datacited by 61
  7. 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checkingcited by 34
  8. 2024Exploring the Capability of LLMs in Performing Low-Level Visual Analytic Tasks on SVG Data Visualizationscited by 18
  9. 2024Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustnesscited by 17
  10. 2024Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Modelscited by 6
  11. 2025A Review on Large Language Models for Visual Analyticscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Toward a Machine Bertin: Why Visualization Needs Design Principles for Machine Cognitioncited by 0
  2. 2026EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answeringcited by 0
  3. 2026Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understandingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Models
  2. 2025Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorization
  3. 2026How Do LLMs See Charts? A Comparative Study on High-Level Visualization Comprehension in Humans and LLMs ↗
  4. 2026Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities
  5. 2026De-rendering, Reasoning, and Repairing Charts with Vision-Language Models
  6. 2025Diagnosing Bottlenecks in Data Visualization Understanding by Vision-Language Models
  7. 2026Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systems
  8. 2025Can AI agents understand spoken conversations about data visualizations in online meetings?