Feature Generation Using LLMs: An Evolutionary Algorithm Approach
A crucial step in machine learning pipelines is to present each entity with features or attributes that are representative of the characteristics of the processed entities. Feature engineering is an important step in finding a relation among attributes that otherwise may not be processed by the ML algorithms. Meanwhile, Large Language Models have shown promising abilities in coding, mathematical reasoning, and processing world knowledge. In this work, we utilize an LLM for the problem of feature generation from tabular data based on the previously given features. We have created a pipeline that takes a set of attributes and a prompt to generate new features. Then, our selection algorithm ...
| Authors | Aria Nourbakhsh, Benoît Alcaraz, Christoph Schommer |
|---|---|
| Primary category | cs.LG |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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