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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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MisVisFix: An Interactive Dashboard for Detecting, Explaining, and Correcting Misleading Visualizations using Large

Amit Kumar Das, Klaus Mueller

IEEE Transactions on Visualization and Computer Graphics

|November 26, 2025

Summary

This summary is machine-generated.

MisVisFix is a new tool that uses Large Language Models (LLMs) to find, explain, and fix misleading data visualizations. It achieves high accuracy, improving data communication and visualization literacy.

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Area of Science:

Data Visualization
Artificial Intelligence
Information Science

Background:

Misleading data visualizations hinder accurate interpretation.
Existing tools for detecting visualization misinformation lack comprehensive explanation and correction capabilities.
Large Language Models (LLMs) show promise for identifying misinformation but require practical applications.

Purpose of the Study:

To introduce MisVisFix, an interactive dashboard for detecting, explaining, and correcting misleading visualizations.
To leverage Claude and GPT models for a complete misinformation workflow.
To enhance user interaction and adaptability to new misinformation strategies.

Main Methods:

Developed an interactive dashboard integrating LLMs (Claude, GPT).
Implemented functionalities for detection, explanation, and automated correction of visualization issues.
Incorporated a chat interface for user queries and modifications.
Evaluated through user studies with visualization experts and fact-checking tool developers.

Main Results:

MisVisFix accurately identifies 96% of visualization issues.
Addresses all 74 known types of visualization misinformation, classifying them by severity.
Provides detailed explanations, actionable suggestions, and generates corrected charts.
User studies confirm accurate issue identification and useful suggestions.

Conclusions:

MisVisFix offers a practical, interactive platform for addressing misleading visualizations.
Transforms LLM capabilities into accessible tools for enhanced visualization literacy.
Supports the creation of more trustworthy data communication and fact-checking processes.