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LLMs drive automated feature engineering for structured data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Evolutionary Feature Engineering (EFE), a novel framework that leverages large language models (LLMs) to automatically discover preprocessing transformations for structured data. EFE represents these transformations as Python programs, enabling seamless integration into existing machine learning pipelines. The framework refines candidate programs using dataset context, summary statistics, and downstream performance feedback. EFE has demonstrated success in time-series forecasting, reducing errors by 3% or more with models like Chronos-2, and in tabular prediction, where it evolves compact, interpretable feature programs that match or exceed existing LLM-based methods. AI

IMPACT Automates complex data preprocessing, potentially improving accuracy and interpretability of ML models across various domains.

RANK_REASON The cluster describes a new research paper detailing a novel framework for feature engineering using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs drive automated feature engineering for structured data

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The cluster describes a new research paper detailing a novel framework for feature engineering using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz, Petros Mol, Abhimanyu Das, Karthik Duraisamy, Samet Oymak ·

    Evolutionary Feature Engineering for Structured Data

    arXiv:2607.01548v1 Announce Type: cross Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing tran…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Evolutionary Feature Engineering for Structured Data

    Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data. EFE represents tr…

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