用离散子图采样提升视觉问答的可解释性，兼顾准确率与推理透明度。
Discrete Subgraph Sampling for Interpretable Graph based Visual Question Answering
- 在图模型中引入多种离散子图采样方法，生成内在解释性子图。
- 在GQA数据集上实现解释性与准确率双赢，答案与问题词共现强。
- 人类评估验证：模型生成的解释符合人对可解释性的偏好。
可解释人工智能（XAI）致力于提升机器学习模型的透明性。尽管多数方法侧重于事后生成解释，但能与预测同步生成解释的内在可解释方法仍较稀少。本文将多种离散子集采样方法集成至基于图的视觉问答系统中，比较其在生成内在解释性子图方面的有效性。在GQA数据集上的实验表明，所提方法有效缓解了可解释性与答案准确率之间的权衡，同时实现了答案与问题词项间的强共现。此外，我们采用扩展的Bradley-Terry模型进行人工评估，结果表明答案与问题词项共现指标与人类偏好高度相关。代码已公开。
原文摘要 · Abstract (English)
Explainable artificial intelligence (XAI) aims to make machine learning models more transparent. While many approaches focus on generating explanations post-hoc, interpretable approaches, which generate the explanations intrinsically alongside the predictions, are relatively rare. In this work, we integrate different discrete subset sampling methods into a graph-based visual question answering system to compare their effectiveness in generating interpretable explanatory subgraphs intrinsically. We evaluate the methods on the GQA dataset and show that the integrated methods effectively mitigate the performance trade-off between interpretability and answer accuracy, while also achieving strong co-occurrences between answer and question tokens. Furthermore, we conduct a human evaluation to assess the interpretability of the generated subgraphs using a comparative setting with the extended Bradley-Terry model, showing that the answer and question token co-occurrence metrics strongly correlate with human preferences. Our source code is publicly available.
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