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arXiv · 2505.04487

A Design Space for the Critical Validation of LLM-Generated Tabular Data

Madhav Sachdeva·Christopher Narayanan·Marvin Wiedenkeller·Jana Sedlakova·Jürgen Bernard
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Abstract

LLM-generated tabular data is creating new opportunities for data-driven applications in academia, business, and society. To leverage benefits like missing value imputation, labeling, and enrichment with context-aware attributes, LLM-generated data needs a critical validation process. The number of pioneering approaches is increasing fast, opening a promising validation space that, so far, remains unstructured. We present a design space for the critical validation of LLM-generated tabular data with two dimensions: First, the Analysis Granularity dimension: from within-attribute (single-item and multi-item) to across-attribute perspectives (1 x 1, 1 x m, and n x n). Second, the Data Source dimension: differentiating between LLM-generated values, ground truth values, explanations, and their combinations. We discuss analysis tasks for each dimension cross-cut, map 19 existing validation approaches, and discuss the characteristics of two approaches in detail, demonstrating descriptive power.

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Madhav Sachdeva, Christopher Narayanan, Marvin Wiedenkeller, Jana Sedlakova, Jürgen Bernard. 2025-05-07. A Design Space for the Critical Validation of LLM-Generated Tabular Data. https://arxiv.org/abs/2505.04487

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