Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Abstract

We present RAG, a general-purpose fine-tuning recipe that combines parametric memory with non-parametric memory for knowledge-intensive NLP tasks. RAG retrieves relevant documents and conditions generation on them.

Key Contributions

• →RAG framework
• →Parametric + non-parametric memory
• →Knowledge-intensive task improvement

Topics