পদ্ধতির তুলনা করুন

	মাল্টি-হেড সেলফ-অ্যাটেনশন	GPT ফাইন-টিউনিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017	2019
প্রবর্তক	Vaswani, A. et al.	Radford, A. et al. (OpenAI)
ধরন	Attention mechanism (Transformer core)	Fine-tuning of pretrained autoregressive language models
মৌলিক উৎস	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.
অপর নাম	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention	GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning
সম্পর্কিত	5	5
সারসংক্ষেপ	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.	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.