Published In
Knowledge and Information Systems
Document Type
Article
Publication Date
6-2-2026
Subjects
Feature generation, Knowledge retrieval, Large language models, Reasoning
Abstract
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is to expand the current feature space using existing features and enriching the informational content. However, generating new, interpretable features usually requires domain-specific knowledge on top of the existing features. In this paper, we introduce a Retrieval-Augmented Feature Generation method, RAFG, to generate useful and explainable features specific to domain classification tasks. To increase the interpretability of the generated features, we conduct knowledge retrieval among the existing features in the domain to identify potential feature associations. These associations are expected to help generate useful features. Moreover, we develop a framework based on large language models (LLMs) for feature generation with reasoning to evaluate their semantic relevance, causal alignment, and expected utility for the downstream task. To mitigate the risk of overconfident or unsupported reasoning, we further introduce a counterfactual validation mechanism that compares reasoning-based predictions with observed performance changes. Experiments across several datasets in medical, economic, and geographic domains show that our RAFG method can produce high-quality, meaningful features and significantly improve classification performance compared with baseline methods.
Rights
Copyright (c) 2026 The Authors Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
https://doi.org/10.1007/s10115-026-02792-4
DOI
10.1007/s10115-026-02792-4
Persistent Identifier
https://archives.pdx.edu/ds/psu/45324
Publisher
Springer Science and Business Media LLC
Citation Details
Zhang, J., Zhang, X., Mo, F., Keerthi Chandra, D., Chen, Y.-Z., Xie, F., & Liu, K. (2026). Reliable retrieval-augmented feature generation with large language model reasoning. Knowledge and Information Systems, 68(1).