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

Madhav Sachdeva, Christopher Narayanan, Marvin Wiedenkeller, Jana Sedlakova, Jürgen Bernard

TL;DR

The paper addresses the need for structured, interpretable validation of LLM-generated tabular data by proposing a two-dimensional design space that separates Data Source (LLM values, ground truth, explanations, and their combinations) from Item/Attribute Granularity (ranging from fine-grained item-level checks to across-attribute analyses). It systematically reviews 19 works, maps existing validation approaches into the space, and demonstrates the framework through two exemplar methods (iScore and LLM Comparator). Key contributions include a formal design-space, a cross-cutting task taxonomy, and a discussion of limitations and future extensions, particularly the role of visual analytics in enabling trustworthy validation. The framework aims to guide researchers and tool developers in building scalable, interpretable validation interfaces that support robust data-driven decision-making in varied domains.

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.

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

TL;DR

The paper addresses the need for structured, interpretable validation of LLM-generated tabular data by proposing a two-dimensional design space that separates Data Source (LLM values, ground truth, explanations, and their combinations) from Item/Attribute Granularity (ranging from fine-grained item-level checks to across-attribute analyses). It systematically reviews 19 works, maps existing validation approaches into the space, and demonstrates the framework through two exemplar methods (iScore and LLM Comparator). Key contributions include a formal design-space, a cross-cutting task taxonomy, and a discussion of limitations and future extensions, particularly the role of visual analytics in enabling trustworthy validation. The framework aims to guide researchers and tool developers in building scalable, interpretable validation interfaces that support robust data-driven decision-making in varied domains.

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.
Paper Structure (14 sections, 2 figures, 1 table)

This paper contains 14 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Design Space
  3. Methodology and Overview
  4. Design Space Dimension: Data Source
  5. Design Space Dimension: Item and Attribute Granularity
  6. Validation Tasks at the Crosscut of Dimensions
  7. Focus
  8. Focus
  9. Focus
  10. Focus
  11. Focus
  12. Validation Examples
  13. Limitations and Discussion
  14. Conclusion

Figures (2)

  • Figure 1: Mapping the supported tasks and user workflows of two existing validation approaches to our design space. Purple: iScore coscia2024iscore, Yellow: LLM Comparator kahng2024llm
  • Figure 2: Screenshot with marked validation steps of iScore reproduced from Coscia et al.coscia2024iscore with permission (left) and LLM Comparator kahng2024llmhttp://www.apache.org/licenses/LICENSE-2.0 (right)