Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Тонкая настройка GPT× | Случайный лес× | Многоголовочное самовнимание× | |
|---|---|---|---|
| Область≠ | Глубокое обучение | Машинное обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Год появления≠ | 2019 | 2001 | 2017 |
| Автор метода≠ | Radford, A. et al. (OpenAI) | Breiman, L. | Vaswani, A. et al. |
| Тип≠ | Fine-tuning of pretrained autoregressive language models | Ensemble (bagging of decision trees) | Attention mechanism (Transformer core) |
| Основополагающий источник≠ | 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 ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| Другие названия | 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 | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention |
| Связанные≠ | 5 | 4 | 5 |
| Сводка≠ | 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. | Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5. |
| ScholarGateНабор данных ↗ |
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