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| Tinh chỉnh BERT× | LoRA và PEFT× | Rừng ngẫu nhiên× | |
|---|---|---|---|
| Lĩnh vực≠ | Học sâu | Học sâu | Học máy |
| Họ | Machine learning | Machine learning | Machine learning |
| Năm ra đời≠ | 2019 | 2022 | 2001 |
| Người khởi xướng≠ | Devlin, J. et al. | Hu, E. J. et al.; Lester, B. et al. | Breiman, L. |
| Loại≠ | Transfer learning (fine-tuning a pre-trained transformer) | Parameter-efficient fine-tuning of large pretrained models | Ensemble (bagging of decision trees) |
| Công trình gốc≠ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. DOI ↗ | Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Tên gọi khác≠ | BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT | LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Liên quan≠ | 5 | 5 | 4 |
| Tóm tắt≠ | BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data. | LoRA (Low-Rank Adaptation), introduced by Hu et al. in 2022, and the broader family of parameter-efficient fine-tuning (PEFT) methods adapt large pretrained language models to new tasks by training only a small number of extra parameters instead of every weight in the model. This makes fine-tuning possible with far less GPU memory and compute while leaving the original model largely untouched. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
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