השוואת שיטות
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| הסבר שאלות ותשובות× | טרנספורמר ניתן להסבר× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2016–2020 | 2017–2021 |
| הוגה השיטה≠ | Community (DeYoung et al.; Rajpurkar et al.) | Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community |
| סוג≠ | Interpretable NLP pipeline | Interpretable deep learning model |
| מקור מכונן≠ | DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020). ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of ACL 2020, pp. 4443–4458. DOI ↗ | 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 ↗ |
| כינויים | XQA, interpretable QA, transparent question answering, rationale-based QA | XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model |
| קשורות≠ | 5 | 4 |
| תקציר≠ | Explainable Question Answering (XQA) combines neural reading-comprehension models — typically BERT-family transformers — with interpretability methods such as rationale extraction, attention visualization, LIME, or SHAP to reveal why the model selected a particular answer span. The goal is not just accuracy but trustworthy, auditable reasoning that users and domain experts can inspect and verify. | 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. |
| ScholarGateמערך נתונים ↗ |
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