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MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks.

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Summary
This summary is machine-generated.

MatplotAlt is a Python package that automatically generates alt text for Matplotlib charts in Jupyter notebooks. It improves chart accessibility by using heuristic and LLM-based methods, even refining LLM accuracy.

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Area of Science:

  • Data Visualization
  • Scientific Computing
  • Accessibility

Background:

  • Matplotlib is a widely used Python library for creating static, animated, and interactive visualizations.
  • Generating descriptive alternative text (alt text) for complex data visualizations remains a challenge, impacting accessibility for visually impaired users.
  • Existing methods for automated alt text generation often lack accuracy or customization options.

Purpose of the Study:

  • To introduce MatplotAlt, an open-source Python package designed to simplify the addition of alt text to Matplotlib figures.
  • To enable Jupyter notebook authors to automatically generate and display chart descriptions with minimal code.
  • To provide customizable options for alt text generation and display, catering to user preferences and accessibility requirements.

Main Methods:

  • Developed MatplotAlt as a Python package integrated with Jupyter notebooks.
  • Implemented heuristic-based and large language model (LLM)-based methods for generating alt text.
  • Evaluated the accuracy of generated alt text for various Matplotlib figures, including univariate and complex plots.
  • Investigated methods to improve LLM accuracy, such as prompting with heuristic-based alt text or parsed data tables.

Main Results:

  • MatplotAlt successfully generates accurate long-form descriptions for both simple and complex Matplotlib figures.
  • Both heuristic and LLM-based methods within MatplotAlt demonstrate effectiveness in creating descriptive alt text.
  • State-of-the-art LLMs exhibit factual inaccuracies when describing charts independently.
  • Prompting GPT-4 Turbo with heuristic-based alt text or data tables significantly enhances description accuracy.

Conclusions:

  • MatplotAlt offers a practical and efficient solution for enhancing the accessibility of Matplotlib visualizations.
  • The package empowers researchers and educators to create more inclusive data narratives within Jupyter environments.
  • Combining heuristic approaches with LLMs, particularly through informed prompting, is crucial for improving the factual accuracy of automated chart descriptions.