NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs
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| Format: | Preprint |
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2026
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| _version_ | 1866912813885161472 |
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| author | Shi, Boshen Yang, Kexin Yang, Yuanbo Chang, Guanguang Chi, Ce Wang, Zhendong Wang, Xing Feng, Junlan |
| author_facet | Shi, Boshen Yang, Kexin Yang, Yuanbo Chang, Guanguang Chi, Ce Wang, Zhendong Wang, Xing Feng, Junlan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06126 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs Shi, Boshen Yang, Kexin Yang, Yuanbo Chang, Guanguang Chi, Ce Wang, Zhendong Wang, Xing Feng, Junlan Artificial Intelligence 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. |
| title | NL2Dashboard: A Lightweight and Controllable Framework for Generating Dashboards with LLMs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.06126 |