# Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness

arXiv: https://arxiv.org/abs/2407.11229 · HTML: https://mlsanity.com/paper/2407.11229/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model](https://mlsanity.com/paper/2401.16420/) (2024)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)
- [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://mlsanity.com/paper/2406.18521/) (2024)
- [DePlot: One-shot visual language reasoning by plot-to-table translation](https://mlsanity.com/paper/2212.10505/) (2022)
- [UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning](https://mlsanity.com/paper/2305.14761/) (2023)
- [MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning](https://mlsanity.com/paper/2311.10774/) (2023)
- [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://mlsanity.com/paper/2212.09662/) (2022)
- [ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning](https://mlsanity.com/paper/2402.12185/) (2024)
- [ChartBench: A Benchmark for Complex Visual Reasoning in Charts](https://mlsanity.com/paper/2312.15915/) (2023)
- [ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning](https://mlsanity.com/paper/2401.02384/) (2024)
- [SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs](https://mlsanity.com/paper/2308.03349/) (2023)
- [OpenCQA: Open-ended Question Answering with Charts](https://mlsanity.com/paper/2210.06628/) (2022)
- [LEAF-QA: Locate, Encode & Attend for Figure Question Answering](https://mlsanity.com/paper/1907.12861/) (2019)
- [Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs](https://mlsanity.com/paper/2406.00257/) (2024)
- [Enhancing Question Answering on Charts Through Effective Pre-training Tasks](https://mlsanity.com/paper/2406.10085/) (2024)

## Built on by (most cited first, 17 of 17)

- [Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation](https://mlsanity.com/paper/2502.14846/) (2025)
- [MapIQ: Evaluating Multimodal Large Language Models for Map Question Answering](https://mlsanity.com/paper/2507.11625/) (2025)
- [Does visualization help AI understand data?](https://mlsanity.com/paper/2507.18022/) (2025)
- [Do MLLMs Really Understand the Charts?](https://mlsanity.com/paper/2509.04457/) (2025)
- [Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach](https://mlsanity.com/paper/2506.06175/) (2025)
- [ViStruct: Simulating Expert-Like Reasoning Through Task Decomposition and Visual Attention Cues](https://mlsanity.com/paper/2506.21762/) (2025)
- [Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement](https://mlsanity.com/paper/2602.10138/) (2026)
- [Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks](https://mlsanity.com/paper/2510.06071/) (2025)
- [Seeing isn't Hearing: Benchmarking Vision Language Models at Interpreting Spectrograms](https://mlsanity.com/paper/2511.13225/) (2025)
- [Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts](https://mlsanity.com/paper/2604.21344/) (2026)
- [Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table Translation](https://mlsanity.com/paper/2604.24987/) (2026)
- [Unlocking UML Class Diagram Understanding in Vision Language Models](https://mlsanity.com/paper/2605.11634/) (2026)
- [VLMs Trace Without Tracking: Diagnosing Failures in Visual Path Following](https://mlsanity.com/paper/2605.15672/) (2026)
- [MMClima: A Framework for Multimodal Climate Science Data and Evaluation](https://mlsanity.com/paper/2606.10194/) (2026)
- [EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering](https://mlsanity.com/paper/2606.23724/) (2026)
- [When Does Consensus Mean Correctness? Measuring the Agreement-Accuracy Coupling with Semantics-Preserving Re-Rendering](https://mlsanity.com/paper/2608.05670/) (2026)
- [Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading](https://mlsanity.com/paper/2608.05675/) (2026)

## Abstract (arXiv)

Chart question answering (CQA) is a crucial area of Visual Language Understanding. However, the robustness and consistency of current Visual Language Models (VLMs) in this field remain under-explored. This paper evaluates state-of-the-art VLMs on comprehensive datasets, developed specifically for this study, encompassing diverse question categories and chart formats. We investigate two key aspects: 1) the models' ability to handle varying levels of chart and question complexity, and 2) their robustness across different visual representations of the same underlying data. Our analysis reveals significant performance variations based on question and chart types, highlighting both strengths and weaknesses of current models. Additionally, we identify areas for improvement and propose future research directions to build more robust and reliable CQA systems. This study sheds light on the limitations of current models and paves the way for future advancements in the field.