Порівняння методів

	Доналаштування GPT	Випадковий ліс	Варіаційний автокодувальник
Галузь	Глибоке навчання	Машинне навчання	Глибоке навчання
Родина	Machine learning	Machine learning	Machine learning
Рік появи	2019	2001	2014
Автор методу	Radford, A. et al. (OpenAI)	Breiman, L.	Kingma, D. P. & Welling, M.
Тип	Fine-tuning of pretrained autoregressive language models	Ensemble (bagging of decision trees)	Deep generative latent-variable model (encoder–decoder)
Основоположне джерело	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Інші назви	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	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Пов'язані	5	4	5
Підсумок	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.	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.