手法を比較

	自己教師あり質問応答	検索拡張生成（Retrieval-Augmented Generation, RAG）
分野	深層学習	テキストマイニング
系統	Machine learning	Process / pipeline
提唱年	2019	2020
提唱者	Lewis, P.; Alberti, C. et al. (multiple independent groups ~2019)	Lewis, Patrick et al. (Meta AI / Facebook AI Research)
種類	Self-supervised NLP training paradigm	Hybrid retrieval + generation pipeline
原典	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.	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.
別名	SSQA, unsupervised question answering, self-supervised QA, zero-label question answering	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)
関連	1	7
概要	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.