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Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study
Created by
Haebom
Author
Yuqi Zhu, Yi Zhong, Jintian Zhang, Ziheng Zhang, Shuofei Qiao, Yujie Luo, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang
Outline
This paper studies strategies to improve the data analysis capability of open source large-scale language models (LLMs). Using seed datasets composed of various realistic scenarios, we evaluate the model in three aspects: data understanding, code generation, and strategic planning. The evaluation results reveal three key findings: the quality of strategic planning is a key determinant of model performance; interaction design and task complexity have a significant impact on inference capability; and data quality has a greater impact than diversity on achieving optimal performance. Based on these insights, we develop a data synthesis methodology to significantly improve the analytical inference capability of open source LLMs.
Takeaways, Limitations
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Takeaways:
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Presenting an effective data synthesis methodology to improve data analysis capabilities of open source LLM
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Emphasizes the importance of strategic planning to improve model performance
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Suggesting the need to consider interaction design and task complexity
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Emphasize the importance of data quality
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Limitations:
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Further research is needed on the generalizability and versatility of the seed dataset used.
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The applicability of the proposed data synthesis methodology to other open source LLMs and various data analysis tasks needs to be verified.
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Need to find ways to ensure objectivity and reliability in strategic plan quality assessment
