Macro-Queries: An Exploration into Guided Chart Generation from High Level Prompts

TL;DR

This paper introduces a guided LLM-based pipeline that transforms high-level user questions into diverse data visualizations, making visualization more accessible for novices through prompting, fine-tuning, and SQL integration.

Contribution

It presents a novel pipeline combining prompting, fine-tuning, and SQL tools to generate visualizations from macro-queries, expanding visualization accessibility for beginners.

Findings

Effective transformation of macro-queries into visualizations.

Enhanced accessibility for novice users in data visualization.

Integration of prompting and SQL tools improves visualization generation.

Abstract

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.