Chen, Olivia Chou, Kara Nagpal, Rashmi Palacios Hielscher, Rafael Gupta, Amar 2026-06-15T04:49:33Z 2026-06-15T04:49:33Z http://hdl.handle.net/11531/110742 Machine learning models on tabular datasets often struggle to understand the context between features, which can limit their accuracy. We propose SemTab, a hybrid framework for generating semantic features that utilizes an open-source Large Language Model (LLM). We evaluated our framework using three benchmark datasets: Adult Income, German Credit, and Bank Marketing. We compared its performance against several off-the-shelf LLMs. The results show that SemTab achieved the highest accuracy across all the classification tasks. For instance, on the Bank Marketing dataset, SemTab achieved an accuracy of 8 0%, which is approximately 2 0% improvement over the baseline models. This work highlights that a hybrid architecture is a practical approach for applying language models to structured tabular data, yielding accurate and interpretable results for various downstream tasks. application/pdf en-GB SemTab: A Hybrid Framework for Semantic Feature Generation on Tabular Data info:eu-repo/semantics/workingPaper info:eu-repo/semantics/draft info:eu-repo/semantics/restrictedAccess Tabular Data, Semantic Feature Generation, LLMs, Model Interpretability