AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science

• URL: http://arxiv.org/abs/2502.16395v2
• Date: Sat, 02 Aug 2025 20:17:22 GMT
• Title: AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science
• Authors: Qiuhai Zeng, Claire Jin, Xinyue Wang, Yuhan Zheng, Qunhua Li,
• Abstract summary: Large language models (LLMs) are increasingly used to automate data analysis through executable code generation.<n>We present $itAIRepr, an $itA$nalyst - $itI$nspector framework for automatically evaluating and improving the $itRepr$oducibility of LLM-generated data analysis.
• Score: 5.064778712920176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly used to automate data analysis through executable code generation. Yet, data science tasks often admit multiple statistically valid solutions, e.g. different modeling strategies, making it critical to understand the reasoning behind analyses, not just their outcomes. While manual review of LLM-generated code can help ensure statistical soundness, it is labor-intensive and requires expertise. A more scalable approach is to evaluate the underlying workflows - the logical plans guiding code generation. However, it remains unclear how to assess whether a LLM-generated workflow supports reproducible implementations. To address this, we present $\it{AIRepr}$, an $\it{A}$nalyst - $\it{I}$nspector framework for automatically evaluating and improving the $\it{Repr}$oducibility of LLM-generated data analysis workflows. Our framework is grounded in statistical principles and supports scalable, automated assessment. We introduce two novel reproducibility-enhancing prompting strategies and benchmark them against standard prompting across 15 analyst-inspector LLM pairs and 1,032 tasks from three public benchmarks. Our findings show that workflows with higher reproducibility also yield more accurate analyses, and that reproducibility-enhancing prompts substantially improve both metrics. This work provides a foundation for more transparent, reliable, and efficient human-AI collaboration in data science. Our code is publicly available.

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