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New framework DocXEval evaluates AI explainability in multimodal documents

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced DocXEval, a novel framework designed to systematically evaluate explainability in multimodal document understanding models. This framework adapts existing explainable AI (XAI) methods and metrics for tasks involving text, layout, and image data, addressing a gap in current XAI research. Through extensive evaluation of 13 XAI methods across various tasks, datasets, and models, the study found that Occlusion performed best in terms of faithfulness and interpretability, while also identifying potential dataset biases and modality contributions. AI

IMPACT Provides a benchmark and practical guide for developing more transparent and trustworthy AI systems in document understanding.

RANK_REASON The cluster contains a research paper detailing a new evaluation framework for AI explainability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework DocXEval evaluates AI explainability in multimodal documents

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The cluster contains a research paper detailing a new evaluation framework for AI explainability. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Saifullah Saifullah, David Dembinsky, Stefan Agne, Andreas Dengel, Sheraz Ahmed ·

    DocXEval: A Systematic Evaluation of Explainability in Multimodal Document Understanding

    arXiv:2610.36929v1 Announce Type: new Abstract: 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-sta…

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