Data Interpreter: An LLM Agent for Data Science
Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen
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.
BibTeX
@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"
}