TabPFN Performance in Few-Shot Tabular Reasoning with Non-Causal Foundation Models

Abstract

Abstract: Tabular data is a pervasive modality spanning a wide range of domains, and the inherent diversity poses a considerable challenge for deep learning. Recent advancements using transformer-based in-context learning have shown promise on smaller and less complex datasets, but have struggled to scale to larger and more complex ones. To address this limitation, we propose a combination of retrieval and fine-tuning: we can adapt the transformer to a local subset of the data by collecting nearest neighbours, and then perform task-specific fine-tuning with this retrieved set of neighbours in context. U

Research Question

How does the in-context learning accuracy of TabPFN compare to other foundation models pre-trained on non-causal synthetic datasets when evaluated on few-shot tabular reasoning tasks using metrics like F1-score or AUC?

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