Summary of Can Llms Generate Visualizations with Dataless Prompts?, by Darius Coelho et al.
Can LLMs Generate Visualizations with Dataless Prompts?
by Darius Coelho, Harshit Barot, Naitik Rathod, Klaus Mueller
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the capability of large language models, specifically GPT-3 and GPT-4, to provide accurate data visualizations in response to queries about publicly available data. It examines their ability to generate visualizations with “dataless prompts,” where no accompanying data is provided. The study compares the results of these models to visualization cheat sheets created by experts, evaluating their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks into how large language models can help answer questions about public information by providing useful pictures and numbers. It tests if these models can make good pictures even when there’s no extra data given. The researchers compare what the models do with what expert visualization makers suggest is best practice. |
Keywords
» Artificial intelligence » Gpt
