RAG

Retrieval-Augmented Generation (RAG) is a task in the field of Natural Language Processing that combines the strengths of retrieval models and generative models. RAG uses a retrieval system to select relevant documents or passages from a large corpus, and then employs a generative model (usually a neural language model) to generate responses based on this retrieved information. This approach enhances the accuracy and coherence of generated text, particularly for tasks such as open-domain question answering, knowledge-driven dialogue, and summarization. It effectively integrates external information, reducing reliance on memorized knowledge and improving response quality based on the latest or specific domain information. The performance of RAG systems is typically evaluated using metrics such as precision, recall, F1 score, BLEU score, and exact match.