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| 자연어 생성× | GPT 파인튜닝× | |
|---|---|---|
| 분야≠ | 텍스트 마이닝 | 딥러닝 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | 2019 |
| 창시자≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | Radford, A. et al. (OpenAI) |
| 유형≠ | NLP generative task — structured data to natural language | Fine-tuning of pretrained autoregressive language models |
| 원전≠ | 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 ↗ |
| 별칭 | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning |
| 관련≠ | 7 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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