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| Samonadzorowane odpowiadanie na pytania× | Retrieval-Augmented Generation (RAG) (Generowanie Wzbogacone o Wyszukiwanie)× | |
|---|---|---|
| Dziedzina≠ | Uczenie głębokie | Eksploracja tekstu |
| Rodzina≠ | Machine learning | Process / pipeline |
| Rok powstania≠ | 2019 | 2020 |
| Twórca≠ | Lewis, P.; Alberti, C. et al. (multiple independent groups ~2019) | Lewis, Patrick et al. (Meta AI / Facebook AI Research) |
| Typ≠ | Self-supervised NLP training paradigm | Hybrid retrieval + generation pipeline |
| Źródło pierwotne≠ | Lewis, P., Denoyer, L., & Riedel, S. (2019). Unsupervised Question Answering by Cloze Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 4896–4910. DOI ↗ | Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474. DOI ↗ |
| Inne nazwy | SSQA, unsupervised question answering, self-supervised QA, zero-label question answering | RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG) |
| Pokrewne≠ | 1 | 7 |
| Podsumowanie≠ | Self-supervised Question Answering (SSQA) is a training paradigm that automatically generates question-answer pairs from unlabeled text — using cloze translation, span masking, or neural question generation — to train QA models without any human-labeled data. It enables high-quality reading comprehension systems even when annotated datasets are scarce or domain-specific. | 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. |
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