方法对比

	可解释循环神经网络	可解释 Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2017–2020	2017–2021
提出者	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
类型	Interpretability framework applied to sequence models	Interpretable deep learning model
开创性文献	Arrieta, A. B., Diaz-Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
别名	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
相关	5	4
摘要	An Explainable Recurrent Neural Network (XAI-RNN) pairs a standard RNN architecture with a post-hoc or intrinsic interpretability method — such as SHAP, LIME, integrated gradients, or attention visualization — to reveal which input time steps or tokens most influence the model's sequential predictions, without sacrificing predictive accuracy.	An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.