Enhancing Tabular Learners with Context-Aware Semantic Embeddings
While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the rich semantics inherent in feature names or cell entries. We propose CASE (Context-Aware Semantic Embeddings), a novel framework that bridges the gap between the semantic understanding of Large Language Models (LLMs) and the statistical capabilities of tabular learners. Unlike existing methods that embed rows in isolation, CASE utilizes a contextualization strategy: we pre-fill the KV cache of a custom-trained Gemma 3-based Tabular Language Model with a representative sample of rows to establish a persistent anchor...
| Authors | Günther Schindler, Maximilian Schambach, Johannes Höhne |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 9 October 2026 |
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