# DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agent

arXiv: https://arxiv.org/abs/2504.12865 · HTML: https://mlsanity.com/paper/2504.12865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 39 of its 91 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 30 of 39)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024)
- [GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements](https://mlsanity.com/paper/2402.10963/) (2024)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [Structure-informed Language Models Are Protein Designers](https://mlsanity.com/paper/2302.01649/) (2023)
- [PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement](https://mlsanity.com/paper/2403.04014/) (2024)
- [Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works](https://mlsanity.com/paper/2210.08477/) (2022)
- [A Short Survey of Viewing Large Language Models in Legal Aspect](https://mlsanity.com/paper/2303.09136/) (2023)
- [Calliope: Automatic Visual Data Story Generation from a Spreadsheet](https://mlsanity.com/paper/2010.09975/) (2020)
- [Visualization Generation with Large Language Models: An Evaluation](https://mlsanity.com/paper/2401.11255/) (2024)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Design](https://mlsanity.com/paper/2401.07312/) (2024)
- [Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model](https://mlsanity.com/paper/2304.14630/) (2023)
- [Prompt4Vis: Prompting Large Language Models with Example Mining and Schema Filtering for Tabular Data Visualization](https://mlsanity.com/paper/2402.07909/) (2024)
- [AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers](https://mlsanity.com/paper/2502.14747/) (2025)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation](https://mlsanity.com/paper/2107.07823/) (2021)
- [ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives](https://mlsanity.com/paper/2103.03996/) (2021)
- [DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01232/) (2022)
- [Leveraging Foundation Models for Crafting Narrative Visualization: A Survey](https://mlsanity.com/paper/2401.14010/) (2024)
- [Keyframer: Empowering Animation Design using Large Language Models](https://mlsanity.com/paper/2402.06071/) (2024)

## Built on by (most cited first, 1 of 1)

- [NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs](https://mlsanity.com/paper/2601.06126/) (2026)

## Abstract (arXiv)

Performance dashboards are dashboards designed for and deployed within industrial settings (e.g., enterprises, government agencies) to showcase and monitor their operational performance. They have evolved into an important and well-commercialized format for data visualization. In practice, the ideation and negotiation phases demand rapid prototyping and iteration to align with evolving client needs. However, existing tools compel designers to compromise either on iteration speed or on the meticulous handling of visual complexities. To address these gaps, we introduce DashChat for generating performance dashboard prototypes. Collaborating with industry experts, we derived the design requirements and analyzed 114 dashboards to extract common design patterns. Informed by the findings, our solution integrates a chat interface with an LLM-driven multi-agent pipeline, translating textual requirements into prototypes. We evaluated the system by comparing it with a baseline, demonstrating its effectiveness in facilitating the prototyping process while ensuring design quality.