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| GPT 파인튜닝× | LoRA 및 PEFT× | Variational Autoencoder× | |
|---|---|---|---|
| 분야 | 딥러닝 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning | Machine learning |
| 기원 연도≠ | 2019 | 2022 | 2014 |
| 창시자≠ | Radford, A. et al. (OpenAI) | Hu, E. J. et al.; Lester, B. et al. | Kingma, D. P. & Welling, M. |
| 유형≠ | Fine-tuning of pretrained autoregressive language models | Parameter-efficient fine-tuning of large pretrained models | Deep generative latent-variable model (encoder–decoder) |
| 원전≠ | 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 ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| 별칭≠ | 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 | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| 관련 | 5 | 5 | 5 |
| 요약≠ | 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
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