AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
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AssistedDS：外部领域知识辅助大语言模型自动数据科学的评测
- 研究问题
- 大语言模型在表格预测任务中，能否批判性地区分并利用有益与有害领域知识，而不是直接采纳外部资料中的建议？
- 方法与思路
- 作者构建包含已知生成机制的合成数据及 Kaggle 任务的基准，配套有益与对抗文档，评估提交有效性、信息召回和预测表现。
- 材料陈述的贡献
- 摘要称，模型常不加辨别地采纳信息，对抗资料会损害预测；研究还记录时间序列处理、跨折特征工程和类别变量解释错误。
- 阅读关注点
- 应核对文档的构造与标注、任务和模型覆盖范围、实验预算及重复性，以及知识使用能力如何与一般编程错误区分。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出模型版本、性能数值或统计不确定性，不能外推至所有自动数据科学系统。
原文摘要
Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. To answer this question, we introduce AssistedDS (Assisted Data Science), a benchmark designed to systematically evaluate how LLMs handle domain knowledge in tabular prediction tasks. AssistedDS features both synthetic datasets with explicitly known generative mechanisms and real-world Kaggle competitions, each accompanied by curated bundles of helpful and adversarial documents. These documents provide domain-specific insights into data cleaning, feature engineering, and model selection. We assess state-of-the-art LLMs on their ability to discern and apply beneficial versus harmful domain knowledge, evaluating submission validity, information recall, and predictive performance. Our results demonstrate three key findings: (1) LLMs frequently exhibit an uncritical adoption of provided information, significantly impairing their predictive performance when adversarial content is introduced, (2) helpful guidance is often insufficient to counteract the negative influence of adversarial information, and (3) in Kaggle datasets, LLMs often make errors in handling time-series data, applying consistent feature engineering across different folds, and interpreting categorical variables correctly. These findings highlight a substantial gap in current models' ability to critically evaluate and leverage expert knowledge, underscoring an essential research direction for developing more robust, knowledge-aware automated data science systems. Our data and code are publicly available here: https://github.com/jeremyxianx/Assisted-DS