VizLinter: A Linter and Fixer Framework for Data Visualization
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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.
Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Aug 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 5; 2025: 6; 2026: 6.
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- 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
- 2024VisEval: A Benchmark for Data Visualization in the Era of Large Language Modelscited by 43
- 2024How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?cited by 27
- 2023Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaborationcited by 13
- 2025DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoningcited by 8
- 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytellingcited by 7
- 2025Protecting multimodal large language models against misleading visualizationscited by 6
- 2025Is this chart lying to me? Automating the detection of misleading visualizationscited by 4
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inferencecited by 3
- 2024Adversarial Attacks on Machine Learning-Aided Visualizationscited by 2
- 2026From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Modelcited by 1
- 2024Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learningcited by 0
8 more
- 2024Formative Study for AI-assisted Data Visualizationcited by 0
- 2025Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Modelscited by 0
- 2025Automated Visualization Makeovers with LLMscited by 0
- 2026Do Large Language Models Understand Data Visualization Principles?cited by 0
- 2026Do Large Language Models Understand Data Visualization Rules?cited by 0
- 2026De-rendering, Reasoning, and Repairing Charts with Vision-Language Modelscited by 0
- 2026When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizationscited by 0
- 2026Exploring Agentic Approaches for Data Issue Detection and Repair in AI-Assisted Visualizationcited by 0
- 2026Exploring Agentic Approaches for Data Issue Detection and Repair in AI-Assisted Visualization21 Aug 2026
- 2026When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations23 Mar 2026
- 2026De-rendering, Reasoning, and Repairing Charts with Vision-Language Models23 Feb 2026
- 2026Do Large Language Models Understand Data Visualization Rules?23 Feb 2026
- 2026Do Large Language Models Understand Data Visualization Principles?23 Feb 2026
- 2026From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model27 Jan 2026
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference21 Dec 2025
- 2025Is this chart lying to me? Automating the detection of misleading visualizations29 Aug 2025
- 2025DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning3 Aug 2025
- 2025Automated Visualization Makeovers with LLMs21 Jul 2025
- 2025Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models1 May 2025
- 2025Protecting multimodal large language models against misleading visualizations27 Feb 2025
8 more
- 2024Formative Study for AI-assisted Data Visualization10 Sep 2024
- 2024Adversarial Attacks on Machine Learning-Aided Visualizations4 Sep 2024
- 2024How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?24 Jul 2024
- 2024VisEval: A Benchmark for Data Visualization in the Era of Large Language Models1 Jul 2024
- 2024Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning25 Feb 2024
- 2023Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration27 Sep 2023
- 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling17 Apr 2023
- 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts4 Oct 2021
- 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
- 2024VisEval: A Benchmark for Data Visualization in the Era of Large Language Modelscited by 43
- 2023Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaborationcited by 13
- 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytellingcited by 7
- 2025Is this chart lying to me? Automating the detection of misleading visualizationscited by 4
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inferencecited by 3
- 2024Adversarial Attacks on Machine Learning-Aided Visualizationscited by 2
- 2026From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Modelcited by 1
- 2024Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learningcited by 0
- 2024Formative Study for AI-assisted Data Visualizationcited by 0
- 2025Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Modelscited by 0
- 2025Automated Visualization Makeovers with LLMscited by 0
12 most cited of 14 All 14 (JSON)
- 2026Do Large Language Models Understand Data Visualization Rules?23 Feb 2026
- 2026Do Large Language Models Understand Data Visualization Principles?23 Feb 2026
- 2026From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model27 Jan 2026
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference21 Dec 2025
- 2025Is this chart lying to me? Automating the detection of misleading visualizations29 Aug 2025
- 2025Automated Visualization Makeovers with LLMs21 Jul 2025
- 2025Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models1 May 2025
- 2024Formative Study for AI-assisted Data Visualization10 Sep 2024
- 2024Adversarial Attacks on Machine Learning-Aided Visualizations4 Sep 2024
- 2024VisEval: A Benchmark for Data Visualization in the Era of Large Language Models1 Jul 2024
- 2024Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning25 Feb 2024
- 2023Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration27 Sep 2023
12 newest of 14 All 14 (JSON)