Machine learningDeep learning / NLP / CV
Explainable Transformer
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.
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Sources
- 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. link ↗
- Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791. DOI: 10.1109/CVPR46437.2021.00084 ↗
Related methods
Referenced by
Explainable BERT-based ClassificationExplainable Graph Neural NetworkExplainable GRUExplainable LSTMExplainable Multilayer PerceptronExplainable Named Entity RecognitionExplainable Question AnsweringExplainable Recurrent Neural NetworkExplainable RoBERTa-based ClassificationExplainable Sentence EmbeddingsExplainable Text Summarization