arXiv AI August 19, 2026 By Samirasadat Jamalidinan, Yue Xu, Kazem Cheshmi

ARASH: Adaptive Retrieval And Shot Selection for Tabular Prediction

ARASH is a method that improves the efficiency of Tabular Foundation Models by selecting optimal few-shot prompts based on local neighborhood analysis within the training set. It reduces the prompt length and memory usage of TabPFN by 1261.5× and 2.56×, respectively, while maintaining comparable accuracy. This approach addresses the challenge of identifying relevant rows for in-context learning in tabular data.

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arXiv Machine Learning

Aug 18

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By Beimnet Bekele Guta

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 26

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arXiv Machine Learning

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By Si-Yang Liu, Han-Jia Ye

llms

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TabFM: A Zero-Shot Foundation Model for Tabular Data

TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction

TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction

Localized TabICLv2: Scaling Tabular In-Context Learning through k-NN

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TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention