Despite their remarkable capabilities, large language models (LLMs) often
produce responses containing factual inaccuracies due to their sole reliance on
the parametric knowledge they encapsulate. Retrieval-Augmented Generation
(RAG), an ad hoc approach that augments LMs with retrieval of relevant
knowledge, decreases such issues. However, indiscriminately retrieving and
incorporating a fixed number of retrieved passages, regardless of whether
retrieval is necessary, or passages are relevant, diminishes LM versatility or
can lead to unhelpful response generation. We introduce a new framework called
Self-Reflective Retrieval-Augmented Generation (Self-RAG) that enhances an LM's
quality and factuality through retrieval and self-reflection. Our framework
trains a single arbitrary LM that adaptively retrieves passages on-demand, and
generates and reflects on retrieved passages and its own generations using
special tokens, called reflection tokens. Generating reflection tokens makes
the LM controllable during the inference phase, enabling it to tailor its
behavior to diverse task requirements. Experiments show that Self-RAG (7B and
13B parameters) significantly outperforms state-of-the-art LLMs and
retrieval-augmented models on a diverse set of tasks. Specifically, Self-RAG
outperforms ChatGPT and retrieval-augmented Llama2-chat on Open-domain QA,
reasoning and fact verification tasks, and it shows significant gains in
improving factuality and citation accuracy for long-form generations relative
to these models.

通过检索和自我反思，Self-RAG 框架提高了语言模型的质量和准确性，在多样任务中显著优于其他模型。