Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study

• URL: http://arxiv.org/abs/2506.19794v3
• Date: Tue, 05 Aug 2025 10:29:19 GMT
• Title: Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study
• Authors: Yuqi Zhu, Yi Zhong, Jintian Zhang, Ziheng Zhang, Shuofei Qiao, Yujie Luo, Lun Du, Da Zheng, Ningyu Zhang, Huajun Chen,
• Abstract summary: Large Language Models (LLMs) hold promise in automating data analysis tasks.<n>Yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios.<n>In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs.
• Score: 55.09905978813599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs. By curating a seed dataset of diverse, realistic scenarios, we evaluate model behavior across three core dimensions: data understanding, code generation, and strategic planning. Our analysis reveals three key findings: (1) Strategic planning quality serves as the primary determinant of model performance; (2) Interaction design and task complexity significantly influence reasoning capabilities; (3) Data quality demonstrates a greater impact than diversity in achieving optimal performance. We leverage these insights to develop a data synthesis methodology, demonstrating significant improvements in open-source LLMs' analytical reasoning capabilities. Code is available at https://github.com/zjunlp/DataMind.

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