@inproceedings{tang-etal-2026-deepchart,
title = "{DEEPCHART}: How Far are {LLM}s from Faithful Data-Science Chart Generation?",
author = "Tang, Jiahui and
Dong, Kuicai and
Li, Dexun and
Gu, Hongchao and
Yu, Haocheng and
Han, Wei and
Zhang, Chen and
Liu, Yong and
Wang, Hao and
Chen, Enhong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1627/",
pages = "37005--37031",
ISBN = "979-8-89176-459-0",
abstract = "Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities, and rendering them accurately. Modern LLMs can produce visually plausible, instruction-compliant charts, yet data-level hallucinations remain difficult to detect in long, noisy, and multimodal contexts. To measure this gap, we introduce , an expert-annotated benchmark of 1,482 task-conditioned chart-generation instances drawn from real-world scientific papers, financial filings, and ecosystem reports. DEEPCHART formulates chart generation as an Extract{--}Reason{--}Visualize pipeline and evaluates source-data extraction, derived-data reasoning, and chart rendering stage by stage. Experiments with state-of-the-art models show that visually plausible charts often conceal data-level hallucinations, with extraction and reasoning errors common in realistic long and multimodal settings. These findings suggest that larger context windows alone are insufficient; faithful chart generation also requires reliable evidence extraction and quantitative reasoning before rendering."
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<abstract>Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities, and rendering them accurately. Modern LLMs can produce visually plausible, instruction-compliant charts, yet data-level hallucinations remain difficult to detect in long, noisy, and multimodal contexts. To measure this gap, we introduce , an expert-annotated benchmark of 1,482 task-conditioned chart-generation instances drawn from real-world scientific papers, financial filings, and ecosystem reports. DEEPCHART formulates chart generation as an Extract–Reason–Visualize pipeline and evaluates source-data extraction, derived-data reasoning, and chart rendering stage by stage. Experiments with state-of-the-art models show that visually plausible charts often conceal data-level hallucinations, with extraction and reasoning errors common in realistic long and multimodal settings. These findings suggest that larger context windows alone are insufficient; faithful chart generation also requires reliable evidence extraction and quantitative reasoning before rendering.</abstract>
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%0 Conference Proceedings
%T DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?
%A Tang, Jiahui
%A Dong, Kuicai
%A Li, Dexun
%A Gu, Hongchao
%A Yu, Haocheng
%A Han, Wei
%A Zhang, Chen
%A Liu, Yong
%A Wang, Hao
%A Chen, Enhong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tang-etal-2026-deepchart
%X Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities, and rendering them accurately. Modern LLMs can produce visually plausible, instruction-compliant charts, yet data-level hallucinations remain difficult to detect in long, noisy, and multimodal contexts. To measure this gap, we introduce , an expert-annotated benchmark of 1,482 task-conditioned chart-generation instances drawn from real-world scientific papers, financial filings, and ecosystem reports. DEEPCHART formulates chart generation as an Extract–Reason–Visualize pipeline and evaluates source-data extraction, derived-data reasoning, and chart rendering stage by stage. Experiments with state-of-the-art models show that visually plausible charts often conceal data-level hallucinations, with extraction and reasoning errors common in realistic long and multimodal settings. These findings suggest that larger context windows alone are insufficient; faithful chart generation also requires reliable evidence extraction and quantitative reasoning before rendering.
%U https://aclanthology.org/2026.emnlp-main.1627/
%P 37005-37031
Markdown (Informal)
[DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?](https://aclanthology.org/2026.emnlp-main.1627/) (Tang et al., EMNLP 2026)
ACL
- Jiahui Tang, Kuicai Dong, Dexun Li, Hongchao Gu, Haocheng Yu, Wei Han, Chen Zhang, Yong Liu, Hao Wang, and Enhong Chen. 2026. DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 37005–37031, Budapest, Hungary. Association for Computational Linguistics.