Retrieval-Augmented Generation (RAG) for Knowledge-Intensive NLP Tasks Researchers have developed a novel strategy known as Retrieval-Augmented Generation (RAG) to get around this restriction In this article, we will explore the limitations of pre-trained models and learn about the RAG model and its configuration, training, and decoding methodologies
Question Decomposition for Retrieval-Augmented Generation Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves pas- sages that are semantically related to the ques- tion and then conditions the model on this evi- dence