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| BERT fine-tuning× | GPT fino-podešavanje× | Slučajna šuma× | |
|---|---|---|---|
| Oblast≠ | Duboko učenje | Duboko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 2019 | 2019 | 2001 |
| Tvorac≠ | Devlin, J. et al. | Radford, A. et al. (OpenAI) | Breiman, L. |
| Tip≠ | Transfer learning (fine-tuning a pre-trained transformer) | Fine-tuning of pretrained autoregressive language models | Ensemble (bagging of decision trees) |
| Temeljni izvor≠ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. DOI ↗ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Drugi nazivi | BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Srodne≠ | 5 | 5 | 4 |
| Sažetak≠ | 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. | GPT fine-tuning adapts pretrained autoregressive language models such as GPT-2/3/4 or LLaMA — introduced in OpenAI's 2019 work by Radford and colleagues — to domain-specific data or to instruction following via reinforcement learning from human feedback (RLHF) or DPO. It is used for instruction following, domain adaptation, and generative tasks. | 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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