Self-supervised pre-training on one real table yields strong tabular transfer, where feature count predicts usefulness and in-context generalization is retrieval-based.
STRABLE introduces 108 real-world string-and-number tables and benchmarks 445 pipelines, finding simple embeddings with advanced learners suffice for categorical tables while LLMs help on free-text tables.