LLM-Assisted Visual Analytics: Opportunities and Challenges
Not on the map. Why?
Abstract (arXiv)
We explore the integration of large language models (LLMs) into visual analytics (VA) systems to transform their capabilities through intuitive natural language interactions. We survey current research directions in this emerging field, examining how LLMs are integrated into data management, language interaction, visualisation generation, and language generation processes. We highlight the new possibilities that LLMs bring to VA, especially how they can change VA processes beyond the usual use cases. We especially highlight building new visualisation-language models, allowing access of a breadth of domain knowledge, multimodal interaction, and opportunities with guidance. Finally, we carefully consider the prominent challenges of using current LLMs in VA tasks. Our discussions in this paper aim to guide future researchers working on LLM-assisted VA systems and help them navigate common obstacles when developing these systems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 60 references that are in the map. First posted on arXiv 4 Sep 2024.
Builds on
28 of 60 references.
References · 28
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2022Language Models are Realistic Tabular Data Generatorscited by 290
- 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
- 2023Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solvecited by 175
- 2023LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Modelscited by 80
- 2021SciCap: Generating Captions for Scientific Figurescited by 76
- 2023Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflowcited by 76
- 2020NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queriescited by 73
- 2023ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Languagecited by 71
- 2021Towards Natural Language Interfaces for Data Visualization: A Surveycited by 58
- 2023Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Modelscited by 58
16 more
- 2021AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualizationcited by 32
- 2023Foundation Models Meet Visualizations: Challenges and Opportunitiescited by 31
- 2020Calliope: Automatic Visual Data Story Generation from a Spreadsheetcited by 30
- 2024Large Language Models are Efficient Learners of Noise-Robust Speech Recognitioncited by 30
- 2020A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualizationcited by 28
- 2024How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?cited by 27
- 2022Chart Question Answering: State of the Art and Future Directionscited by 26
- 2023DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articlescited by 24
- 2024LEVA: Using Large Language Models to Enhance Visual Analyticscited by 24
- 2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
- 2023Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Modelcited by 21
- 2022Facilitating Conversational Interaction in Natural Language Interfaces for Visualizationcited by 12
- 2023InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebookscited by 11
- 2024PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasetscited by 9
- 2020VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendationscited by 5
- 2024Probing Language Models' Gesture Understanding for Enhanced Human-AI Interactioncited by 2
Built on by
Citing papers per year: 2025: 2.
2 papers.