Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented
Generation
Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented
Generation
Retrieval-Augmented Generation (RAG) mitigates issues of the factual errors and hallucinated outputs generated by Large Language Models (LLMs) in open-domain question-answering tasks (OpenQA) via introducing external knowledge. For complex QA, however, existing RAG methods use LLMs to actively predict retrieval timing and directly use the retrieved information for generation, regardless …