DocXEval: A Systematic Evaluation of Explainability in Multimodal Document Understanding
Multimodal transformer models have driven unprecedented performance breakthroughs in modern document understanding. However, the inherent black-box nature of these models remains a major obstacle to their safe deployment in high-stakes sectors. Despite significant progress in explainability for vision and language tasks, explainable AI (XAI) research tailored to multimodal document understanding has received little attention. To address this gap, we present \ourframework{}, the first comprehensive explainability framework that adapts existing XAI methods and evaluation metrics to the multimodal (text, layout, and image) document understanding context, while introducing efficient multi-tar...
| Authors | Saifullah Saifullah, David Dembinsky, Stefan Agne et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 October 2026 |
| Last updated at source | 27 October 2026 |
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