方法对比
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| 提示工程× | 零样本分类× | |
|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 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) | Yin, Hay & Roth |
| 类型≠ | NLP pipeline — structured instruction design for large language models | NLP text-classification task |
| 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 ↗ | Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923. DOI ↗ |
| 别名 | instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering) | zero-shot text classification, entailment-based classification, Sıfır Atışlı Sınıflandırma (Zero-Shot Classification) |
| 相关≠ | 7 | 3 |
| 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. | Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets. |
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