# LEVA: Using Large Language Models to Enhance Visual Analytics

arXiv: https://arxiv.org/abs/2403.05816 · HTML: https://mlsanity.com/paper/2403.05816/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2024.

## Built on by (most cited first, 24 of 24)

- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent](https://mlsanity.com/paper/2507.18165/) (2025)
- [InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions](https://mlsanity.com/paper/2503.04110/) (2025)
- [ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset](https://mlsanity.com/paper/2501.09349/) (2025)
- [Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework](https://mlsanity.com/paper/2501.13778/) (2025)
- [V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions](https://mlsanity.com/paper/2406.15259/) (2024)
- [Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking](https://mlsanity.com/paper/2601.11459/) (2026)
- [Zero-shot Emotion Annotation in Facial Images Using Large Multimodal Models: Benchmarking and Prospects for Multi-Class, Multi-Frame Approaches](https://mlsanity.com/paper/2502.12454/) (2025)
- [LLM-Assisted Visual Analytics: Opportunities and Challenges](https://mlsanity.com/paper/2409.02691/) (2024)
- [Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations](https://mlsanity.com/paper/2409.18764/) (2024)
- [A2P-Vis: an Analyzer-to-Presenter Agentic Pipeline for Visual Insights Generation and Reporting](https://mlsanity.com/paper/2512.22101/) (2025)
- [Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Models](https://mlsanity.com/paper/2410.20975/) (2024)
- [Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective](https://mlsanity.com/paper/2503.02631/) (2025)
- [A Review on Large Language Models for Visual Analytics](https://mlsanity.com/paper/2503.15176/) (2025)
- [Do Language Model Agents Align with Humans in Rating Visualizations? An Empirical Study](https://mlsanity.com/paper/2505.06702/) (2025)
- [ClimateSOM: A Visual Analysis Workflow for Climate Ensemble Datasets](https://mlsanity.com/paper/2508.06732/) (2025)
- [Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics](https://mlsanity.com/paper/2508.07390/) (2025)
- [Scaling Unverifiable Rewards: A Case Study on Visual Insights](https://mlsanity.com/paper/2512.22650/) (2025)
- [Bridging Natural Language and Interactive What-If Interfaces via LLM-Generated Declarative Specifications](https://mlsanity.com/paper/2604.07652/) (2026)
- [Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy](https://mlsanity.com/paper/2607.15176/) (2026)
- [MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction](https://mlsanity.com/paper/2607.19910/) (2026)
- [Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets](https://mlsanity.com/paper/2608.22045/) (2026)
- [WidgetVA: A Widget-Centric Framework and Benchmark for Agentic Visual Analytics](https://mlsanity.com/paper/2609.24094/) (2026)

## Abstract (arXiv)

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.