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| Fine-Tuning GPT× | LoRA dan PEFT× | Random Forest× | |
|---|---|---|---|
| Bidang≠ | Pembelajaran Mendalam | Pembelajaran Mendalam | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning | Machine learning |
| Tahun asal≠ | 2019 | 2022 | 2001 |
| Pencetus≠ | Radford, A. et al. (OpenAI) | Hu, E. J. et al.; Lester, B. et al. | Breiman, L. |
| Tipe≠ | Fine-tuning of pretrained autoregressive language models | Parameter-efficient fine-tuning of large pretrained models | Ensemble (bagging of decision trees) |
| Sumber perintis≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias≠ | 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 | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Terkait≠ | 5 | 5 | 4 |
| Ringkasan≠ | 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. | 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. |
| ScholarGateSet data ↗ |
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