Large Language Model-based Data Science Agent: A Survey
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908670421368832 |
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| author | Chen, Ke Wang, Peiran Yu, Yaoning Zhan, Xianyang Wang, Haohan |
| author_facet | Chen, Ke Wang, Peiran Yu, Yaoning Zhan, Xianyang Wang, Haohan |
| contents | The rapid advancement of Large Language Models (LLMs) has driven novel applications across diverse domains, with LLM-based agents emerging as a crucial area of exploration. This survey presents a comprehensive analysis of LLM-based agents designed for data science tasks, summarizing insights from recent studies. From the agent perspective, we discuss the key design principles, covering agent roles, execution, knowledge, and reflection methods. From the data science perspective, we identify key processes for LLM-based agents, including data preprocessing, model development, evaluation, visualization, etc. Our work offers two key contributions: (1) a comprehensive review of recent developments in applying LLMbased agents to data science tasks; (2) a dual-perspective framework that connects general agent design principles with the practical workflows in data science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02744 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model-based Data Science Agent: A Survey Chen, Ke Wang, Peiran Yu, Yaoning Zhan, Xianyang Wang, Haohan Artificial Intelligence The rapid advancement of Large Language Models (LLMs) has driven novel applications across diverse domains, with LLM-based agents emerging as a crucial area of exploration. This survey presents a comprehensive analysis of LLM-based agents designed for data science tasks, summarizing insights from recent studies. From the agent perspective, we discuss the key design principles, covering agent roles, execution, knowledge, and reflection methods. From the data science perspective, we identify key processes for LLM-based agents, including data preprocessing, model development, evaluation, visualization, etc. Our work offers two key contributions: (1) a comprehensive review of recent developments in applying LLMbased agents to data science tasks; (2) a dual-perspective framework that connects general agent design principles with the practical workflows in data science. |
| title | Large Language Model-based Data Science Agent: A Survey |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.02744 |