Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Проектування промптів× | Класифікація тексту з малою кількістю прикладів× | |
|---|---|---|
| Галузь | Інтелектуальний аналіз тексту | Інтелектуальний аналіз тексту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2020 (few-shot prompting); 2022 (chain-of-thought) | — |
| Автор методу≠ | Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022) | — |
| Тип≠ | NLP pipeline — structured instruction design for large language models | NLP text-classification task (low-resource) |
| Основоположне джерело≠ | Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901. link ↗ | Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL. DOI ↗ |
| Інші назви≠ | instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering) | few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot) |
| Пов'язані≠ | 7 | 4 |
| Підсумок≠ | 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. | Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels. |
| ScholarGateНабір даних ↗ |
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