dorsal/arxiv
View SchemaNL2Dashboard: 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 |
|---|---|
| Categories | |
| ArXiv ID | 2601.06126vv1 |
| URL | https://arxiv.org/abs/2601.06126 |
| 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.
{
"annotation_id": "5979836a-a591-464c-956f-9da59b176c85",
"date_created": "2026-02-17T05:53:05.098000Z",
"date_modified": "2026-02-17T05:53:05.098000Z",
"file_hash": "1a0d63b10c7936af314fadb5269f5b8bd01b49952c81efbddee84a9faa257119",
"private": false,
"record": {
"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\u0027s content, layout, and visual elements. Therefore, it confines the LLM\u0027s 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.",
"arxiv_id": "2601.06126",
"authors": [
"Boshen Shi",
"Kexin Yang",
"Yuanbo Yang",
"Guanguang Chang",
"Ce Chi",
"Zhendong Wang",
"Xing Wang",
"Junlan Feng"
],
"categories": [
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs",
"url": "https://arxiv.org/abs/2601.06126",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "71719021-404a-4d74-b3c0-2e3ad3fcd37f",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}