research-article

Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study

Authors:

Hai JinAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 3

Article No.: 115, Pages 1 - 28

https://doi.org/10.1145/3654992

Published: 30 May 2024 Publication History

Abstract

The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their potential in generating visualizations, and explore the effectiveness of in-context learning prompts for enhancing this task. In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis. Our findings suggest that transforming structured tabular data into programs is effective, and it is essential to consider the table schema when formulating prompts. Furthermore, we evaluate two types of LLMs: finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5), against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench). The experimental results reveal that LLMs outperform baselines, with inference-only models consistently exhibiting performance improvements, at times even surpassing fine-tuned models when provided with certain few-shot demonstrations through in-context learning. Finally, we analyze when the LLMs fail in NL2Vis, and propose to iteratively update the results using strategies such as chain-of-thought, role-playing, and code-interpreter. The experimental results confirm the efficacy of iterative updates and hold great potential for future study.

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https://doi.org/10.1109/TVCG.2024.3456378
Wang HGordon MBattle LHeer J(2025)DracoGPT: Extracting Visualization Design Preferences from Large Language ModelsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345635031:1(710-720)Online publication date: Jan-2025
https://doi.org/10.1109/TVCG.2024.3456350
Xie LZheng CXia HQu HZhu-Tian C(2024)WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code VisualizationProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676374(1-14)Online publication date: 13-Oct-2024
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Index Terms

Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
1. Human-centered computing
  1. Visualization
    1. Empirical studies in visualization

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 3

SIGMOD

June 2024

1953 pages

EISSN:2836-6573

DOI:10.1145/3670010

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Divyakant Agrawal
UC Santa Barbara, United States

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Published: 30 May 2024

Published in PACMMOD Volume 2, Issue 3

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Cited By

Wang HHoffswell JThazin Thane SBursztyn VBearfield C(2025)How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying LayoutsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345637831:1(536-546)Online publication date: Jan-2025
https://doi.org/10.1109/TVCG.2024.3456378
Wang HGordon MBattle LHeer J(2025)DracoGPT: Extracting Visualization Design Preferences from Large Language ModelsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345635031:1(710-720)Online publication date: Jan-2025
https://doi.org/10.1109/TVCG.2024.3456350
Xie LZheng CXia HQu HZhu-Tian C(2024)WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code VisualizationProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676374(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676374
Hoque EIslam M(2024)Natural Language Generation for Visualizations: State of the Art, Challenges and Future DirectionsComputer Graphics Forum10.1111/cgf.15266Online publication date: 30-Oct-2024
https://doi.org/10.1111/cgf.15266
Shen LLi HWang YQu H(2024)From Data to Story: Towards Automatic Animated Data Video Creation with LLM-Based Multi-Agent Systems2024 IEEE VIS Workshop on Data Storytelling in an Era of Generative AI (GEN4DS)10.1109/GEN4DS63889.2024.00008(20-27)Online publication date: 13-Oct-2024
https://doi.org/10.1109/GEN4DS63889.2024.00008

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