FEST uses self-evolving trees to produce expert-aligned, auditable features from unstructured data and outperforms baselines on brand, authenticity, and stress tasks while releasing the BrandGuide dataset.
Large language models can automatically engineer features for few-shot tabular learning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
FELA deploys specialized LLM agents in an evolutionary framework to generate, validate, and refine explainable features from heterogeneous industrial event logs, improving downstream model performance.
LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
TAROT constructs and refines LLM-derived task-adaptive semantic graphs then applies GNN message passing to improve few-shot tabular prediction.
citing papers explorer
-
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
FEST uses self-evolving trees to produce expert-aligned, auditable features from unstructured data and outperforms baselines on brand, authenticity, and stress tasks while releasing the BrandGuide dataset.
-
FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data
FELA deploys specialized LLM agents in an evolutionary framework to generate, validate, and refine explainable features from heterogeneous industrial event logs, improving downstream model performance.
-
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
-
TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning
TAROT constructs and refines LLM-derived task-adaptive semantic graphs then applies GNN message passing to improve few-shot tabular prediction.