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DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?

Paper · 2024 · Liqiang Jing, Zhehui Huang, Xiaoyang Wang et al.

Summary

Current benchmarks for data science agents are much simpler than real practice. They tend to use short, text-only instructions, narrow code-completion checks, or fixed Python environments. DSBench aims to close this gap with realistic, end-to-end tasks taken from two well-known competition platforms. The analysis part draws on ModelOff, a financial modelling contest normally solved in Excel. Agents answer multiple-choice or fill-in-the-blank questions that depend on long, multimodal task descriptions and on workbooks with several sheets and tables. The modelling part draws on Kaggle competitions. Here the agent must train a predictor and produce a submission file. Because Kaggle tasks are scored with many different metrics, the authors introduce the Relative Performance Gap (RPG), which puts all results on one comparable scale. The authors tested GPT-4o, Claude, Gemini and several agent frameworks, and most tasks went unsolved. The work shows that autonomous data science agents remain far from expert level and gives the field a demanding, reproducible yardstick.

Key points

  • Data analysis tasks: 466 questions from 38 ModelOff challenges, filtered down from 43 original challenges. Answers are scored by accuracy.
  • Data modeling tasks: 74 Kaggle competitions covering 18 different evaluation metrics, which are unified through the proposed RPG measure.
  • Task length: descriptions average about 798 words, far longer than in DS-1000 or Arcade. Tasks include images, tables and large or multi-table data files.
  • Best results: the strongest agent reached only 34.12% accuracy on analysis tasks and 34.74% RPG on modelling tasks.
  • Availability: data and code are released on GitHub (LiqiangJing/DSBench).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

Cite

Cite as Liqiang Jing, Zhehui Huang, Xiaoyang Wang, Wenlin Yao, Wenhao Yu, Kaixin Ma, Hongming Zhang, Xinya Du and Dong Yu. 2024. DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?. arXiv:2409.07703.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2409.07703 · arXiv 2409.07703

@misc{jing2024dsbench,
  title = {{DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?}},
  author = {Liqiang Jing, Zhehui Huang, Xiaoyang Wang, Wenlin Yao, Wenhao Yu, Kaixin Ma, Hongming Zhang, Xinya Du and Dong Yu},
  year = {2024},
  doi = {10.48550/arxiv.2409.07703},
  eprint = {2409.07703},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2409.07703}
}

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