What is Self-RAG?

Self-RAG, introduced by Asai and colleagues in 2023, fine-tunes a language model to emit special reflection tokens that control when to retrieve, rate the relevance of retrieved passages, and critique its own answer. The model decides adaptively whether retrieval is needed, which chunks to use, and whether its draft answer is well-grounded. This beats static retrieval pipelines on many benchmarks but requires a specially fine-tuned model with the reflection tokens learned during training, limiting deployment to open-weight models or custom fine-tunes. ---