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

arXiv 2408.12726 ↗First posted 22 Aug 2024cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 22 Aug 2024.

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9 of 29 references.

References · 9

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  4. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 117
  5. 2023LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Modelscited by 82
  6. 2021Towards Natural Language Interfaces for Data Visualization: A Surveycited by 59
  7. 2023Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Modelscited by 59
  8. 2021Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizationscited by 31
  9. 2023Enhancing Text-to-SQL Translation for Financial System Designcited by 12

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