What is Self-RAG?
Self-RAG is a retrieval-augmented generation technique where the model dynamically decides when retrieval is necessary and generates special reflection tokens to evaluate its own outputs. Rather than always retrieving or never retrieving, Self-RAG trained models learn to assess whether they need external knowledge for a given query, retrieve information when needed, and critically evaluate whether their generated content is supported by the retrieved sources. This adaptive approach improves both efficiency and accuracy.
The system generates reflection tokens at various points: deciding whether to retrieve (based on query complexity and knowledge requirements), assessing relevance of retrieved passages, evaluating whether generated content is supported by sources, and judging overall response quality. These reflection tokens guide the generation process and can trigger retrieval or revision. The model is trained to generate these tokens alongside content, learning when retrieval helps and when it's unnecessary or harmful.
Self-RAG addresses several limitations of traditional RAG: it avoids unnecessary retrieval for queries the model can answer from parametric knowledge, reduces exposure to irrelevant retrieved documents, and provides self-evaluation of factual grounding. Research has shown Self-RAG outperforms both standard RAG and purely parametric approaches across diverse tasks. The technique represents a move toward more autonomous, self-aware retrieval systems that can adapt their behavior based on task requirements rather than following fixed retrieval patterns.