مقایسهٔ روش‌ها

	سازوکار توجه	تنظیم دقیق GPT	جنگل تصادفی	توجه به خود چند-سری
حوزه	یادگیری عمیق	یادگیری عمیق	یادگیری ماشین	یادگیری عمیق
خانواده	Machine learning	Machine learning	Machine learning	Machine learning
سال پیدایش	2015	2019	2001	2017
پدیدآور	Bahdanau, D.; Luong, M.T.	Radford, A. et al. (OpenAI)	Breiman, L.	Vaswani, A. et al.
نوع	Neural attention layer (encoder-decoder)	Fine-tuning of pretrained autoregressive language models	Ensemble (bagging of decision trees)	Attention mechanism (Transformer core)
منبع بنیادین	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	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.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
نام‌های دیگر	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	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	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention
مرتبط	5	5	4	5
خلاصه	The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.	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.	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.