Macro-Queries: An Exploration into Guided Chart Generation from High Level Prompts
Lee, Christopher J., Tran, Giorgio, Tabalba, Roderick, Leigh, Jason, Longman, Ryan
–arXiv.org Artificial Intelligence
This paper explores the intersection of data visualization and Large Language Models (LLMs). Driven by the need to make a broader range of data visualization types accessible for novice users, we present a guided LLM-based pipeline designed to transform data, guided by high-level user questions (referred to as macro-queries), into a diverse set of useful visualizations. This approach leverages various prompting techniques, fine-tuning inspired by Abela's Chart Taxonomy, and integrated SQL tool usage.
arXiv.org Artificial Intelligence
Aug-22-2024
- Country:
- North America > United States
- Florida (0.04)
- District of Columbia > Washington (0.04)
- New York > New York County
- New York City (0.04)
- Hawaii > Honolulu County
- Honolulu (0.04)
- Asia > Middle East
- Republic of Türkiye > Batman Province > Batman (0.05)
- North America > United States
- Genre:
- Research Report (0.64)
- Industry:
- Education (0.68)
- Automobiles & Trucks > Manufacturer (0.46)
- Technology: