arXiv:2606.31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.e., tabular foundation models leveraging in-context learning (ICL), achieve state-of-the-art performance on tabular tasks. While LLMs are known to unintentionally memorize training data, the memorization dynamics of LTMs remain largely unexplored. We investigate the potential for parametric memorization in tabular ICL. We introduce ICLMEM, a probing framework designed to separate context-based predictions from parametric memorization. Our zero-information multiple-choice context strips away valid contextual patterns
The proliferation of Large Tabular Models (LTMs) and their reliance on in-context learning necessitates a deeper understanding of their potential for unintended memorization, mirroring concerns previously raised for LLMs.
Understanding LTM memorization is critical for ensuring data privacy, model explainability, and preventing the leakage of sensitive commercial or personal information within tabular data applications.
This research introduces a specific framework (ICLMEM) to quantitatively assess and differentiate context-based predictions from parametric memorization in LTMs, providing a new tool for auditing and designing safer tabular AI.
- · AI ethicists
- · Data privacy regulators
- · Enterprises using tabular AI
- · Users of tabular AI applications
- · Developers of un-auditable AI models
- · Organizations with lax data governance
- · Proprietary data competitors
Increased scrutiny and demand for transparent, auditable tabular AI models with robust memorization mitigation strategies.
Development of new regulatory frameworks and industry standards specifically targeting data privacy and memorization in large tabular models.
A potential shift towards federated learning or other privacy-preserving AI techniques for tabular data, especially in sensitive sectors like finance and healthcare.
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Read at arXiv cs.LG