Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Здравый смысл в обработке естественного языка (NLP)× | Генерация с дополненной выборкой (Retrieval-Augmented Generation, RAG)× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2019 (landmark benchmarks) | 2020 |
| Автор метода≠ | Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019) | Lewis, Patrick et al. (Meta AI / Facebook AI Research) |
| Тип≠ | NLP reasoning task | Hybrid retrieval + generation pipeline |
| Основополагающий источник≠ | Sap, M. et al. (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI. link ↗ | Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474. DOI ↗ |
| Другие названия≠ | commonsense NLP, if-then reasoning, Sağduyu Akıl Yürütme (Commonsense Reasoning) | RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG) |
| Связанные≠ | 6 | 7 |
| Сводка≠ | Commonsense reasoning in NLP refers to the capacity of a language model or inference system to draw on implicit, world-knowledge facts that humans take for granted — facts not stated in the text — to answer questions, complete stories, or interpret dialogue. Landmark benchmarks formalising the task include ATOMIC (Sap et al., 2019), an if-then commonsense knowledge graph, and HellaSwag (Zellers et al., 2019), a sentence-completion challenge that exposed gaps in machine understanding of everyday events. | Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model. |
| ScholarGateНабор данных ↗ |
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