Linganisha mbinu

	Uzalishaji wa Lugha Asilia	Utekelezaji wa GPT (GPT Fine-Tuning)
Nyanja	Uchimbaji wa Matini	Ujifunzaji wa Kina
Familia	Process / pipeline	Machine learning
Mwaka wa asili	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	2019
Mwanzilishi	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	Radford, A. et al. (OpenAI)
Aina	NLP generative task — structured data to natural language	Fine-tuning of pretrained autoregressive language models
Chanzo asilia	Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170.	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.
Majina mbadala	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning
Zinazohusiana	7	5
Muhtasari	Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents.	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.