So sánh phương pháp

	Phân loại dựa trên BERT có thể giải thích	Phân loại dựa trên BERT tinh chỉnh
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019–2020	2019
Người khởi xướng	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)
Loại	Pre-trained transformer classifier with post-hoc or intrinsic explainability	Pre-trained transformer fine-tuned for classification
Công trình gốc	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
Tên gọi khác	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
Liên quan	6	5
Tóm tắt	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.	Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.