# Does visualization help AI understand data?

arXiv: https://arxiv.org/abs/2507.18022 · HTML: https://mlsanity.com/paper/2507.18022/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Chart-to-Text: A Large-Scale Benchmark for Chart Summarization](https://mlsanity.com/paper/2203.06486/) (2022)
- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [Vision language models are blind: Failing to translate detailed visual features into words](https://mlsanity.com/paper/2407.06581/) (2024)
- [VisText: A Benchmark for Semantically Rich Chart Captioning](https://mlsanity.com/paper/2307.05356/) (2023)
- [Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data](https://mlsanity.com/paper/2402.17644/) (2024)
- [Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking](https://mlsanity.com/paper/2301.11843/) (2023)
- [Exploring the Capability of LLMs in Performing Low-Level Visual Analytic Tasks on SVG Data Visualizations](https://mlsanity.com/paper/2404.19097/) (2024)
- [Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness](https://mlsanity.com/paper/2407.11229/) (2024)
- [Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models](https://mlsanity.com/paper/2411.00257/) (2024)
- [A Review on Large Language Models for Visual Analytics](https://mlsanity.com/paper/2503.15176/) (2025)

## Built on by (most cited first, 3 of 3)

- [Toward a Machine Bertin: Why Visualization Needs Design Principles for Machine Cognition](https://mlsanity.com/paper/2602.01527/) (2026)
- [EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering](https://mlsanity.com/paper/2606.23724/) (2026)
- [Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding](https://mlsanity.com/paper/2607.17999/) (2026)

## 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.