NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs

• URL: http://arxiv.org/abs/2601.06126v1
• Date: Sun, 04 Jan 2026 13:26:04 GMT
• Title: NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs
• Authors: Boshen Shi, Kexin Yang, Yuanbo Yang, Guanguang Chang, Ce Chi, Zhendong Wang, Xing Wang, Junlan Feng,
• Abstract summary: We propose NL2Dashboard, a lightweight framework for dashboard generation.<n>We introduce a structured intermediate representation (IR) that encapsulates the dashboard's content, layout, and visual elements.<n>Building upon this framework, we develop a multi-agent system in which the IR-driven algorithm is instantiated as a suite of tools.
• Score: 38.148675833251325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Large Language Models (LLMs) have demonstrated remarkable proficiency in generating standalone charts, synthesizing comprehensive dashboards remains a formidable challenge. Existing end-to-end paradigms, which typically treat dashboard generation as a direct code generation task (e.g., raw HTML), suffer from two fundamental limitations: representation redundancy due to massive tokens spent on visual rendering, and low controllability caused by the entanglement of analytical reasoning and presentation. To address these challenges, we propose NL2Dashboard, a lightweight framework grounded in the principle of Analysis-Presentation Decoupling. We introduce a structured intermediate representation (IR) that encapsulates the dashboard's content, layout, and visual elements. Therefore, it confines the LLM's role to data analysis and intent translation, while offloading visual synthesis to a deterministic rendering engine. Building upon this framework, we develop a multi-agent system in which the IR-driven algorithm is instantiated as a suite of tools. Comprehensive experiments conducted with this system demonstrate that NL2Dashboard significantly outperforms state-of-the-art baselines across diverse domains, achieving superior visual quality, significantly higher token efficiency, and precise controllability in both generation and modification tasks.

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