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The Standardization Trap: Certifying Joint Label Processing in Tabular Foundation Models
Duong Nguyen, Nicolas Chesneau, Milan Bhan
Linear regression and kernel smoothing offer tractable explanations of in-context learning: in both, the features determine the weight assigned to each context label. However, whet…
TICDA: Tabular In-Context Data Attribution
Yacine Benihaddadene, Milan Bhan, Eliot Dugelay +4
Tabular foundation models (TFMs) achieve strong predictive performance by conditioning on labeled demonstrations provided in context, without any parameter update. Yet how individu…
How Do Uncertainty Estimators Align with Hallucination Occurrence in LLMs?
Yedidia Agnimo, Anna Korba, Annabelle Blangero +2
Large language models (LLMs) can hallucinate, but do model uncertainty estimators reliably reveal when they do? Although uncertainty estimators are increasingly used to identify un…
TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
Duong Nguyen, Mohammed Jawhar, Nicolas Chesneau
Tabular foundation models (TFMs), such as TabPFN-2.6, TabICLv2, ConTextTab, Mitra, LimiX, and TabDPT, achieve strong zero-shot performance through in-context learning, but their in…