TabuLLM: Feature extraction and interpretation of text columns in tabular data using large language models
Year: 2026
Type: software-paper
Abstract: TabuLLM is a Python package for extracting and interpreting LLM-based text features from datasets that combine free-text and structured columns. Motivated by a clinical study predicting postoperative acute kidney injury in paediatric cardiopulmonary bypass patients from electronic health records, the package integrates large language model (LLM) embeddings into scikit-learn-compatible pipelines for tabular datasets containing text columns alongside structured features. The package provides three core components: 1) TextColumnTransformer wraps the LangChain embedding ecosystem in a scikit-learn transformer interface with native multi-column handling; 2) GMMFeatureExtractor extends scikit-learn's Gaussian mixture model with a transform() method that returns per-cluster log-joint features for dimensionality reduction; and 3) ClusterExplainer generates natural language cluster descriptions using LLMs with recursive summarization, optional outcome-based statistical testing, and narrative synthesis. On a fraud detection benchmark, incorporating text features via TabuLLM improves test AUC from 0.92 to 0.98, with stacking ensemble variants reaching 0.99. TabuLLM thus lowers the barrier to text-tabular integration: state-of-the-art embeddings become a drop-in addition to existing scikit-learn pipelines, with interpretability tools to understand what the text features capture. The package is available via pip install tabullm.
Source: SoftwareX
Institution: Imperial College London
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W7165015965