Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzgalvu paše-uzmanība× | GPT smalkoregulēšana× | LoRA un PEFT× | |
|---|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2017 | 2019 | 2022 |
| Autors≠ | Vaswani, A. et al. | Radford, A. et al. (OpenAI) | Hu, E. J. et al.; Lester, B. et al. |
| Tips≠ | Attention mechanism (Transformer core) | Fine-tuning of pretrained autoregressive language models | Parameter-efficient fine-tuning of large pretrained models |
| Pirmavots≠ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ | Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. link ↗ |
| Citi nosaukumi≠ | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning | LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning |
| Saistītās | 5 | 5 | 5 |
| Kopsavilkums≠ | 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. | 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. | 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. |
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