Data Interpreter: An LLM Agent for Data Science
Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
Correct Metadata for
Abstract
Large Language Model (LLM)-based agents have excelled in various domains but face significant challenges when applied to data science workflows due to their complex, multi-stage nature. Current LLM-based agents struggle with non-linear relationships, recursive dependencies, implicit data- and logic-dependent reasoning, and managing extensive context. In this paper, we introduce Data Interpreter, an LLM-based agent that addresses these challenges through hierarchical graph-based modeling to represent the complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. Extensive experiments confirm the effectiveness of Data Interpreter. On InfiAgent-DABench, it boosts performance by 25% (from 75.9% to 94.9%), and on machine learning and open-ended tasks, it lifts accuracy from 88% to 95% and from 60% to 97%, respectively. Moreover, our method surpasses state-of-the-art baselines by 26% on the MATH dataset. We will release the code upon publication.- Anthology ID:
- 2025.findings-acl.1016
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2025
- Month:
- July
- Year:
- 2025
- Address:
- Vienna, Austria
- Editors:
- Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 19796–19821
- Language:
- URL:
- https://aclanthology.org/2025.findings-acl.1016/
- DOI:
- 10.18653/v1/2025.findings-acl.1016
- Bibkey:
- Cite (ACL):
- Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, and Chenglin Wu. 2025. Data Interpreter: An LLM Agent for Data Science. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19796–19821, Vienna, Austria. Association for Computational Linguistics.
- Cite (Informal):
- Data Interpreter: An LLM Agent for Data Science (Hong et al., Findings 2025)
- Copy Citation:
- PDF:
- https://aclanthology.org/2025.findings-acl.1016.pdf
Export citation
@inproceedings{hong-etal-2025-data,
title = "Data Interpreter: An {LLM} Agent for Data Science",
author = "Hong, Sirui and
Lin, Yizhang and
Liu, Bang and
Liu, Bangbang and
Wu, Binhao and
Zhang, Ceyao and
Li, Danyang and
Chen, Jiaqi and
Zhang, Jiayi and
Wang, Jinlin and
Zhang, Li and
Zhang, Lingyao and
Yang, Min and
Zhuge, Mingchen and
Guo, Taicheng and
Zhou, Tuo and
Tao, Wei and
Tang, Robert and
Lu, Xiangtao and
Zheng, Xiawu and
Liang, Xinbing and
Fei, Yaying and
Cheng, Yuheng and
Ni, Yongxin and
Gou, Zhibin and
Xu, Zongze and
Luo, Yuyu and
Wu, Chenglin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1016/",
doi = "10.18653/v1/2025.findings-acl.1016",
pages = "19796--19821",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Model (LLM)-based agents have excelled in various domains but face significant challenges when applied to data science workflows due to their complex, multi-stage nature. Current LLM-based agents struggle with non-linear relationships, recursive dependencies, implicit data- and logic-dependent reasoning, and managing extensive context. In this paper, we introduce Data Interpreter, an LLM-based agent that addresses these challenges through hierarchical graph-based modeling to represent the complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. Extensive experiments confirm the effectiveness of Data Interpreter. On InfiAgent-DABench, it boosts performance by 25{\%} (from 75.9{\%} to 94.9{\%}), and on machine learning and open-ended tasks, it lifts accuracy from 88{\%} to 95{\%} and from 60{\%} to 97{\%}, respectively. Moreover, our method surpasses state-of-the-art baselines by 26{\%} on the MATH dataset. We will release the code upon publication."
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%0 Conference Proceedings %T Data Interpreter: An LLM Agent for Data Science %A Hong, Sirui %A Lin, Yizhang %A Liu, Bang %A Liu, Bangbang %A Wu, Binhao %A Zhang, Ceyao %A Li, Danyang %A Chen, Jiaqi %A Zhang, Jiayi %A Wang, Jinlin %A Zhang, Li %A Zhang, Lingyao %A Yang, Min %A Zhuge, Mingchen %A Guo, Taicheng %A Zhou, Tuo %A Tao, Wei %A Tang, Robert %A Lu, Xiangtao %A Zheng, Xiawu %A Liang, Xinbing %A Fei, Yaying %A Cheng, Yuheng %A Ni, Yongxin %A Gou, Zhibin %A Xu, Zongze %A Luo, Yuyu %A Wu, Chenglin %Y Che, Wanxiang %Y Nabende, Joyce %Y Shutova, Ekaterina %Y Pilehvar, Mohammad Taher %S Findings of the Association for Computational Linguistics: ACL 2025 %D 2025 %8 July %I Association for Computational Linguistics %C Vienna, Austria %@ 979-8-89176-256-5 %F hong-etal-2025-data %X Large Language Model (LLM)-based agents have excelled in various domains but face significant challenges when applied to data science workflows due to their complex, multi-stage nature. Current LLM-based agents struggle with non-linear relationships, recursive dependencies, implicit data- and logic-dependent reasoning, and managing extensive context. In this paper, we introduce Data Interpreter, an LLM-based agent that addresses these challenges through hierarchical graph-based modeling to represent the complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. Extensive experiments confirm the effectiveness of Data Interpreter. On InfiAgent-DABench, it boosts performance by 25% (from 75.9% to 94.9%), and on machine learning and open-ended tasks, it lifts accuracy from 88% to 95% and from 60% to 97%, respectively. Moreover, our method surpasses state-of-the-art baselines by 26% on the MATH dataset. We will release the code upon publication. %R 10.18653/v1/2025.findings-acl.1016 %U https://aclanthology.org/2025.findings-acl.1016/ %U https://doi.org/10.18653/v1/2025.findings-acl.1016 %P 19796-19821
Markdown (Informal)
[Data Interpreter: An LLM Agent for Data Science](https://aclanthology.org/2025.findings-acl.1016/) (Hong et al., Findings 2025)
- Data Interpreter: An LLM Agent for Data Science (Hong et al., Findings 2025)
ACL
- Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, and Chenglin Wu. 2025. Data Interpreter: An LLM Agent for Data Science. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19796–19821, Vienna, Austria. Association for Computational Linguistics.