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| Генериране с разширение за извличане (Retrieval-Augmented Generation, RAG)× | Автоматично обобщаване на текст× | |
|---|---|---|
| Област | Извличане на текст | Извличане на текст |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2020 | — |
| Създател≠ | Lewis, Patrick et al. (Meta AI / Facebook AI Research) | — |
| Тип≠ | Hybrid retrieval + generation pipeline | NLP text-generation / text-reduction task |
| Основополагащ източник≠ | Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474. DOI ↗ | Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval. DOI ↗ |
| Други названия | RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG) | automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme |
| Свързани≠ | 7 | 4 |
| Резюме≠ | 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. | Automatic text summarization is a natural-language-processing task that condenses long documents into shorter summaries while preserving their key information. It works through one of two families of approaches — extractive summarization, which selects the most important spans from the source, or abstractive summarization, which generates new text. The field was consolidated by Nenkova and McKeown (2011), and sequence-to-sequence models such as BART (Lewis et al., 2020) advanced the abstractive side. |
| ScholarGateНабор от данни ↗ |
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