পদ্ধতির তুলনা করুন

	GPT ফাইন-টিউনিং	XGBoost
ক্ষেত্র	গভীর শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2019	2016
প্রবর্তক	Radford, A. et al. (OpenAI)	Chen, T. & Guestrin, C.
ধরন	Fine-tuning of pretrained autoregressive language models	Ensemble (gradient-boosted decision trees)
মৌলিক উৎস	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
অপর নাম	GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning	XGBoost, extreme gradient boosting, scalable tree boosting
সম্পর্কিত	5	5
সারসংক্ষেপ	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.