Marginal Response Surface Elicitation for Zero-Label Tabular Learning
MARS transforms LLM knowledge into a zero-shot tabular classifier by selecting representative feature values from unlabeled data and querying an LLM for support and importance scores. It aggregates these responses using weighted sums to create feature response functions, allowing predictions on new data without any ground-truth labels or local training.