TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings
TILE is a framework that connects Large Language Models (LLMs) to Table Foundation Models for tabular learning, especially when tables have string columns. It solves the problem of poor representation extraction by using four key techniques: row serialization for context, intermediate-layer extraction, columnar pooling to keep structure, and by-pass features for precise numerical data. This method achieves state-of-the-art results across 100 datasets.