方法对比

	可解释门控循环单元 (Explainable GRU)	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	2014 (GRU); 2016–2017 (XAI integration)	2017–2021
提出者	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
类型	Recurrent neural network with post-hoc or attention-based interpretability	Interpretable deep learning model
开创性文献	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	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	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
相关	5	4
摘要	Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.	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.