השוואת שיטות

	LSTM מוסבר	טרנספורמר ניתן להסבר
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2017–2019	2017–2021
הוגה השיטה	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
סוג	Interpretable deep learning (post-hoc explainability)	Interpretable deep learning model
מקור מכונן	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
כינויים	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
קשורות	5	4
תקציר	Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.	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.