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arXiv · 2505.19825

Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding

Tassilo Klein·Johannes Hoffart
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Abstract

This position paper argues that foundation models for tabular data face inherent limitations when isolated from operational context - the procedural logic, declarative rules, and domain knowledge that define how data is created and governed. Current approaches focus on single-table generalization or schema-level relationships, fundamentally missing the operational knowledge that gives data meaning. We introduce Semantically Linked Tables (SLT) and Foundation Models for SLT (FMSLT) as a new model class that grounds tabular data in its operational context. We propose dual-phase training: pre-training on open-source code-data pairs and synthetic systems to learn business logic mechanics, followed by zero-shot inference on proprietary data. We introduce the ``Operational Turing Test'' benchmark and argue that operational grounding is essential for autonomous agents in complex data environments.

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Tassilo Klein, Johannes Hoffart. 2025-05-26. Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding. https://arxiv.org/abs/2505.19825

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