  Large language models (LLMs) inevitably exhibit hallucinations since the
accuracy of generated texts cannot be secured solely by the parametric
knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a
practicable complement to LLMs, it relies heavily on the relevance of retrieved
documents, raising concerns about how the model behaves if retrieval goes
wrong. To this end, we propose the Corrective Retrieval Augmented Generation
(CRAG) to improve the robustness of generation. Specifically, a lightweight
retrieval evaluator is designed to assess the overall quality of retrieved
documents for a query, returning a confidence degree based on which different
knowledge retrieval actions can be triggered. Since retrieval from static and
limited corpora can only return sub-optimal documents, large-scale web searches
are utilized as an extension for augmenting the retrieval results. Besides, a
decompose-then-recompose algorithm is designed for retrieved documents to
selectively focus on key information and filter out irrelevant information in
them. CRAG is plug-and-play and can be seamlessly coupled with various
RAG-based approaches. Experiments on four datasets covering short- and
long-form generation tasks show that CRAG can significantly improve the
performance of RAG-based approaches.
