TabReX: Tabular Referenceless eXplainable Evaluation

Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into text, ignoring structure, or rely on fixed references that limit generalization.We present TABREX, a reference-less, propertydriven framework for evaluating tabular generation via graph-based reasoning.TABREX converts both source text and generated tables into canonical knowledge graphs, aligns them through an LLM-guided matching process, and computes interpretable, rubric-aware scores that quantify structural and factual fidelity.The resulting metric provides controllable tradeoffs between sensitivity and specificity, yielding human-aligned judgments and cell-level error traces.To systematically assess metric robustness, we introduce TABREX-BENCH, a large-scale benchmark spanning six domains and twelve planner-driven perturbation types across three difficulty tiers.Empirical results show that TABREX achieves the highest correlation with expert rankings, remains stable under harder perturbations, and enables finegrained model-vs-prompt analysis establishing a new paradigm for trustworthy, explainable evaluation of structured generation systems.