# VizLinter: A Linter and Fixer Framework for Data Visualization

arXiv: https://arxiv.org/abs/2108.10299 · HTML: https://mlsanity.com/paper/2108.10299/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2021.

## Built on by (most cited first, 20 of 20)

- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?](https://mlsanity.com/paper/2407.17291/) (2024)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Protecting multimodal large language models against misleading visualizations](https://mlsanity.com/paper/2502.20503/) (2025)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling](https://mlsanity.com/paper/2304.08366/) (2023)
- [Is this chart lying to me? Automating the detection of misleading visualizations](https://mlsanity.com/paper/2508.21675/) (2025)
- [VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference](https://mlsanity.com/paper/2512.18853/) (2025)
- [Adversarial Attacks on Machine Learning-Aided Visualizations](https://mlsanity.com/paper/2409.02485/) (2024)
- [From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model](https://mlsanity.com/paper/2601.19053/) (2026)
- [Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning](https://mlsanity.com/paper/2402.15997/) (2024)
- [Formative Study for AI-assisted Data Visualization](https://mlsanity.com/paper/2409.06892/) (2024)
- [Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models](https://mlsanity.com/paper/2505.00455/) (2025)
- [Automated Visualization Makeovers with LLMs](https://mlsanity.com/paper/2508.05637/) (2025)
- [Do Large Language Models Understand Data Visualization Principles?](https://mlsanity.com/paper/2602.20084/) (2026)
- [Do Large Language Models Understand Data Visualization Rules?](https://mlsanity.com/paper/2602.20137/) (2026)
- [De-rendering, Reasoning, and Repairing Charts with Vision-Language Models](https://mlsanity.com/paper/2602.20291/) (2026)
- [When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations](https://mlsanity.com/paper/2603.22368/) (2026)
- [Exploring Agentic Approaches for Data Issue Detection and Repair in AI-Assisted Visualization](https://mlsanity.com/paper/2608.21602/) (2026)

## Abstract (arXiv)

Despite the rising popularity of automated visualization tools, existing systems tend to provide direct results which do not always fit the input data or meet visualization requirements. Therefore, additional specification adjustments are still required in real-world use cases. However, manual adjustments are difficult since most users do not necessarily possess adequate skills or visualization knowledge. Even experienced users might create imperfect visualizations that involve chart construction errors. We present a framework, VizLinter, to help users detect flaws and rectify already-built but defective visualizations. The framework consists of two components, (1) a visualization linter, which applies well-recognized principles to inspect the legitimacy of rendered visualizations, and (2) a visualization fixer, which automatically corrects the detected violations according to the linter. We implement the framework into an online editor prototype based on Vega-Lite specifications. To further evaluate the system, we conduct an in-lab user study. The results prove its effectiveness and efficiency in identifying and fixing errors for data visualizations.