@inproceedings{ouyang-etal-2025-nvagent,
title = "nv{A}gent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow",
author = "Ouyang, Geliang and
Chen, Jingyao and
Nie, Zhihe and
Gui, Yi and
Wan, Yao and
Zhang, Hongyu and
Chen, Dongping",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.960/",
doi = "10.18653/v1/2025.acl-long.960",
pages = "19534--19567",
ISBN = "979-8-89176-251-0",
abstract = "*Natural Language to Visualization* (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from large-scale data. Recent advancements in *Large Language Models* (LLMs) show promise in automating code generation to transform tabular data into accessible visualizations. However, they often struggle with complex queries that require reasoning across multiple tables. To address this limitation, we propose a collaborative agent workflow, termed **nvAgent**, for NL2Vis. Specifically, **nvAgent** comprises three agents: a processor agent for database processing and context filtering, a composer agent for planning visualization generation, and a validator agent for code translation and output verification. Comprehensive evaluations on the new VisEval benchmark demonstrate that **nvAgent** consistently surpasses state-of-the-art baselines, achieving a 7.88{\%} improvement in single-table and a 9.23{\%} improvement in multi-table scenarios. Qualitative analyses further highlight that **nvAgent** maintains nearly a 20{\%} performance margin over previous models, underscoring its capacity to produce high-quality visual representations from complex, heterogeneous data sources. All datasets and source code are available at: [https://github.com/geliang0114/nvAgent](https://github.com/geliang0114/nvAgent)."
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<abstract>*Natural Language to Visualization* (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from large-scale data. Recent advancements in *Large Language Models* (LLMs) show promise in automating code generation to transform tabular data into accessible visualizations. However, they often struggle with complex queries that require reasoning across multiple tables. To address this limitation, we propose a collaborative agent workflow, termed **nvAgent**, for NL2Vis. Specifically, **nvAgent** comprises three agents: a processor agent for database processing and context filtering, a composer agent for planning visualization generation, and a validator agent for code translation and output verification. Comprehensive evaluations on the new VisEval benchmark demonstrate that **nvAgent** consistently surpasses state-of-the-art baselines, achieving a 7.88% improvement in single-table and a 9.23% improvement in multi-table scenarios. Qualitative analyses further highlight that **nvAgent** maintains nearly a 20% performance margin over previous models, underscoring its capacity to produce high-quality visual representations from complex, heterogeneous data sources. All datasets and source code are available at: [https://github.com/geliang0114/nvAgent](https://github.com/geliang0114/nvAgent).</abstract>
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%0 Conference Proceedings
%T nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow
%A Ouyang, Geliang
%A Chen, Jingyao
%A Nie, Zhihe
%A Gui, Yi
%A Wan, Yao
%A Zhang, Hongyu
%A Chen, Dongping
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F ouyang-etal-2025-nvagent
%X *Natural Language to Visualization* (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from large-scale data. Recent advancements in *Large Language Models* (LLMs) show promise in automating code generation to transform tabular data into accessible visualizations. However, they often struggle with complex queries that require reasoning across multiple tables. To address this limitation, we propose a collaborative agent workflow, termed **nvAgent**, for NL2Vis. Specifically, **nvAgent** comprises three agents: a processor agent for database processing and context filtering, a composer agent for planning visualization generation, and a validator agent for code translation and output verification. Comprehensive evaluations on the new VisEval benchmark demonstrate that **nvAgent** consistently surpasses state-of-the-art baselines, achieving a 7.88% improvement in single-table and a 9.23% improvement in multi-table scenarios. Qualitative analyses further highlight that **nvAgent** maintains nearly a 20% performance margin over previous models, underscoring its capacity to produce high-quality visual representations from complex, heterogeneous data sources. All datasets and source code are available at: [https://github.com/geliang0114/nvAgent](https://github.com/geliang0114/nvAgent).
%R 10.18653/v1/2025.acl-long.960
%U https://aclanthology.org/2025.acl-long.960/
%U https://doi.org/10.18653/v1/2025.acl-long.960
%P 19534-19567
Markdown (Informal)
[nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://aclanthology.org/2025.acl-long.960/) (Ouyang et al., ACL 2025)
ACL