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| 설명 가능한 RoBERTa 기반 분류× | BERT 기반 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019–2020 | 2019 |
| 창시자≠ | Liu et al. (RoBERTa, 2019); Lundberg & Lee (SHAP, 2017); Ribeiro et al. (LIME, 2016) | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) |
| 유형≠ | Pre-trained transformer classifier with post-hoc XAI | Pre-trained language model with fine-tuning |
| 원전≠ | Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692. link ↗ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics. DOI ↗ |
| 별칭 | XAI-RoBERTa, Interpretable RoBERTa Classifier, RoBERTa with SHAP/LIME, Transparent RoBERTa NLP | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| 관련≠ | 5 | 4 |
| 요약≠ | Explainable RoBERTa-based classification fine-tunes a RoBERTa transformer model on labeled text data and then applies post-hoc interpretability methods — such as SHAP, LIME, or attention analysis — to reveal which tokens or features drove each prediction. This bridges state-of-the-art NLP performance with human-understandable reasoning, satisfying both accuracy and transparency requirements. | BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data. |
| ScholarGate데이터셋 ↗ |
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