Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Monipäinen itsehuomio (Multi-Head Self-Attention)× | LoRA ja PEFT× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2017 | 2022 |
| Kehittäjä≠ | Vaswani, A. et al. | Hu, E. J. et al.; Lester, B. et al. |
| Tyyppi≠ | Attention mechanism (Transformer core) | Parameter-efficient fine-tuning of large pretrained models |
| Alkuperäislähde≠ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ | Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. link ↗ |
| Rinnakkaisnimet≠ | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention | LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | 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. |
| ScholarGateAineisto ↗ |
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