@inproceedings{zou-etal-2026-promptfe,
    title = "{P}rompt{FE}: Automated Feature Engineering by Prompting",
    author = "Zou, Yufeng  and
      Utke, Jean  and
      Klabjan, Diego  and
      Liu, Han",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.28/",
    pages = "653--681",
    ISBN = "979-8-89176-380-7",
    abstract = "Automated feature engineering (AutoFE) liberates data scientists from the burden of manual feature construction. The semantic information of datasets contains rich context information for feature engineering but has been underutilized in many existing AutoFE works. We present PromptFE, a novel AutoFE framework that leverages large language models (LLMs) to automatically construct features in a compact string format and generate semantic explanations based on dataset descriptions. By learning the performance of constructed features in context, the LLM iteratively improves feature construction. We demonstrate through experiments on real-world datasets the superior performance of PromptFE over state-of-the-art AutoFE methods. We verify the impact of dataset semantic information and provide comprehensive study on the LLM-based feature construction process."
}