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Tabular data is essential for applying machine learning tasks across variousindustries. However, traditional data processing methods do not fully utilizeall the information available in the tables, ignoring important contextualinformation such as column header descriptions. In addition, pre-processingdata into a tabular format can remain a labor-intensive bottleneck in modeldevelopment. This work introduces TabText, a processing and feature extractionframework that extracts contextual information from tabular data structures.TabText addresses processing difficulties by converting the content intolanguage and utilizing pre-trained large language models (LLMs). We evaluateour framework on nine healthcare prediction tasks ranging from patientdischarge, ICU admission, and mortality. We show that 1) applying our TabTextframework enables the generation of high-performing and simple machine learningbaseline models with minimal data pre-processing, and 2) augmentingpre-processed tabular data with TabText representations improves the averageand worst-case AUC performance of standard machine learning models by as muchas 6%.

TabText: A Flexible and Contextual Approach to Tabular Data Representation