Year: 2025
Type: conference-paper
Abstract: Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-level discrepancies.To address this, we propose a rubric-based evaluation framework that integrates multi-level structural descriptors with fine-grained contextual signals, enabling more precise and consistent table comparison.Building on this, we introduce TabXEval, an eXhaustive and eXplainable two-phase evaluation framework.TabXEval first aligns reference and predicted tables structurally via TabAlign, then performs semantic and syntactic comparison using TabCompare, offering interpretable and granular feedback.We evaluate TabXEval on TabXBench, a diverse, multi-domain benchmark featuring realistic table perturbations and human annotations.A sensitivity-specificity analysis further demonstrates the robustness and explainability of TabXEval across varied table tasks.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Arizona State University
OpenAlex ID: W4412887684