LLMs as Feature Engineers for Text-and-Tabular Prediction

Abstract:We introduce an iterative framework that automates the extraction of interpretable, schema-bound categorical features from unstructured text for tabular prediction models. To navigate the feature space, a generator LLM proposes semantic definitions, a separate extractor LLM materializes the features, and a downstream tabular model evaluates their predictive performance. We optimize this search by translating explicit model errors, such as AUC ranking inversions, into natural-language feedback, steering the LLM to resolve specific predictive failures. Evaluated across three public datasets, this error-driven loop accelerates feature discovery by up to $3\times$ compared to unguided search. Empirically, the generated features demonstrate strong multi-view complementarity, strictly outperforming any subset when combined with TF-IDF and dense embeddings. Finally, the framework guarantees instance-level interpretability: the discovered features dominate SHAP importance rankings and provide a fully transparent, semantic audit trail for every prediction.

Machine Learning (cs.LG)

arXiv:2609.21894 [cs.LG]

(or arXiv:2609.21894v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.21894 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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