arXiv 2605.060582026-05-07cs.LGcs.CV

迈向具有解释链预测的自解释文档视觉问答

Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions

Department of Informatics, University of Oslo（奥斯陆大学信息学院）；Integreat – Norwegian Centre for Knowledge-driven Machine Learning（挪威知识驱动机器学习中心）；TRUST – The Norwegian Centre for Trustworthy AI（挪威可信人工智能中心）

Kjetil Indrehus, Adrian Duric, Changkyu Choi, Ali Ramezani-Kebrya

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出CoExVQA框架，通过解释链设计实现文档视觉问答的自解释，通过证据识别、答案定位和基于证据解码三步流程提升可解释性，实验显示在PFL-DocVQA上取得SotA性能，ANLS提升12%。

摘要：

文档视觉问答（DocVQA）要求视觉语言模型不仅确定文档中与问题相关的信息，还需确定答案在页面上的位置。现有DocVQA模型将相关证据和答案定位结合，操作如黑箱，难以验证预测如何依赖视觉证据。我们提出CoExVQA，一种具有基于解释链的自我解释DocVQA框架。CoExVQA首先识别问题相关证据，然后显式定位答案区域，最后仅从该区域解码答案。通过CoExVQA的解释链预测，可以跨模态直接检查和验证推理过程。实验结果表明，限制解码到基于证据的证据可实现PFL-DocVQA上的SotA可解释DocVQA性能，比当前可解释基线提高12%的ANLS，同时提供透明且可验证的预测。

英文摘要：

Document Visual Question Answering (DocVQA) requires vision-language models to reason not only about what information in a document is relevant to a question, but also where the answer is grounded on the page. Existing DocVQA models entangle question-relevant evidence and answer localization and operate largely as black boxes, offering limited means to verify how predictions depend on visual evidence. We propose CoExVQA, a self-explainable DocVQA framework with a grounded reasoning process through a chain-of-explanation design. CoExVQA first identifies question-relevant evidence, then explicitly localizes the answer region, and finally decodes the answer exclusively from the grounded region. Prediction via CoExVQA's chain-of-explanation enables direct inspection and verification of the reasoning process across modalities. Empirical results show that restricting decoding to grounded evidence achieves SotA explainable DocVQA performance on PFL-DocVQA, improving ANLS by 12% over the current explainable baselines while providing transparent and verifiable predictions.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。