विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| प्राकृतिक भाषा जनरेशन× | जीपीटी फाइन-ट्यूनिंग× | |
|---|---|---|
| क्षेत्र≠ | पाठ खनन | गहन अधिगम |
| परिवार≠ | Process / pipeline | Machine learning |
| उद्भव वर्ष≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | 2019 |
| प्रवर्तक≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | Radford, A. et al. (OpenAI) |
| प्रकार≠ | NLP generative task — structured data to natural language | Fine-tuning of pretrained autoregressive language models |
| मौलिक स्रोत≠ | 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. link ↗ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ |
| उपनाम | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning |
| संबंधित≠ | 7 | 5 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
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