Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Software Engineering

arXiv:2305.03380 (cs)
[Submitted on 5 May 2023 (v1), last revised 11 May 2023 (this version, v2)]

Title:Visualization in the Era of Artificial Intelligence: Experiments for Creating Structural Visualizations by Prompting Large Language Models

Authors:Hans-Georg Fill, Fabian Muff
View PDF
Abstract:Large Language Models (LLMs) have revolutionized natural language processing by generating human-like text and images from textual input. However, their potential to generate complex 2D/3D visualizations has been largely unexplored. We report initial experiments showing that LLMs can generate 2D/3D visualizations that may be used for legal visualization. Further research is needed for complex 2D visualizations and 3D scenes. LLMs can become a powerful tool for many industries and applications, generating complex visualizations with minimal training.
Comments: 9 Pages, 4 Figures
Subjects: Software Engineering (cs.SE); Graphics (cs.GR)
Cite as: arXiv:2305.03380 [cs.SE]
  (or arXiv:2305.03380v2 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2305.03380
Related DOI: https://doi.org/10.48550/arXiv.2305.03380

Submission history

From: Fabian Muff [view email]
[v1] Fri, 5 May 2023 09:16:59 UTC (1,541 KB)
[v2] Thu, 11 May 2023 09:21:46 UTC (866 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.SE
< prev   |   next >
new | recent | 2023-05
Change to browse by:
cs
cs.GR

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)