Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Ingeniería de Prompts× | Generación de Lenguaje Natural× | |
|---|---|---|
| Campo | Minería de texto | Minería de texto |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2020 (few-shot prompting); 2022 (chain-of-thought) | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) |
| Autor original≠ | Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022) | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) |
| Tipo≠ | NLP pipeline — structured instruction design for large language models | NLP generative task — structured data to natural language |
| Fuente seminal≠ | Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901. link ↗ | 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 ↗ |
| Alias≠ | instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering) | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) |
| Relacionados | 7 | 7 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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