Captioning Visualizations with Large Language Models (CVLLM): A Tutorial
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Abstract (arXiv)
Automatically captioning visualizations is not new, but recent advances in large language models(LLMs) open exciting new possibilities. In this tutorial, after providing a brief review of Information Visualization (InfoVis) principles and past work in captioning, we introduce neural models and the transformer architecture used in generic LLMs. We then discuss their recent applications in InfoVis, with a focus on captioning. Additionally, we explore promising future directions in this field.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 27 Jun 2024.
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8 of 17 references.
References · 8
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2023Large Language Modelscited by 774
- 2023NExT-GPT: Any-to-Any Multimodal LLMcited by 610
- 2023VisText: A Benchmark for Semantically Rich Chart Captioningcited by 111
- 2023Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioningcited by 47
- 2021Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Contentcited by 38
- 2024ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarizationcited by 19
- 2022Why More Text is (Often) Better: Themes from Reader Preferences for Integration of Charts and Textcited by 4
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