Macro-Queries: An Exploration into Guided Chart Generation from High Level Prompts
This addresses the challenge of data visualization accessibility for non-experts, though it appears incremental in combining existing techniques.
The paper tackles the problem of making diverse data visualizations accessible to novice users by developing a guided LLM-based pipeline that transforms data based on high-level user prompts, resulting in a system that generates useful visualizations.
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.