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Active In-Context Learning for Tabular Foundation Models: When, Where, How
This paper studies active in-context learning for tabular foundation models (TFMs), where labels are acquired iteratively to build the context. The authors propose and evaluate target-aware and information-gain-based acquisition rules against classical methods across twenty benchmarks. Key findings show that diversity-based acquisition works best early, target-aware nearest-neighbor selection is crucial, and the EPIC rule retains 80-87% of full-data performance with only 5-10% of the pool labeled.
Why it matters: Provides concrete guidance on label-efficient strategies for tabular foundation models, a practical concern for real-world deployment.
active learningtabular datafoundation models