方法对比
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| 提示工程× | GPT模型微调× | |
|---|---|---|
| 领域≠ | 文本挖掘 | 深度学习 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 2020 (few-shot prompting); 2022 (chain-of-thought) | 2019 |
| 提出者≠ | Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022) | Radford, A. et al. (OpenAI) |
| 类型≠ | NLP pipeline — structured instruction design for large language models | Fine-tuning of pretrained autoregressive language models |
| 开创性文献≠ | Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901. link ↗ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ |
| 别名≠ | instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering) | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning |
| 相关≠ | 7 | 5 |
| 摘要≠ | Prompt engineering is the practice of crafting structured natural-language instructions — prompts — to elicit targeted outputs from large language models (LLMs). Formalised by Brown et al. (2020) in the context of GPT-3 and extended by Wei et al. (2022) with chain-of-thought prompting, it encompasses four main strategies: zero-shot, few-shot, chain-of-thought, and tree-of-thought. Rather than re-training a model, the analyst shapes the model's behaviour entirely through the design of the input text. | 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. |
| ScholarGate数据集 ↗ |
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