Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

By Patrick L., et al

Published on April 12, 2021

Read the original document by opening this link in a new tab.

Table of Contents

Abstract
1 Introduction
2 Methods
2.1 Models
2.2 Retriever: DPR
2.3 Generator: BART
2.4 Training
2.5 Decoding
3 Experiments
3.1 Open-domain Question Answering
3.2 Abstractive Question Answering
3.3 Jeopardy Question Generation
3.4 Fact Verification
4 Results

Summary

The document discusses the concept of retrieval-augmented generation (RAG) for knowledge-intensive natural language processing tasks. It explores the integration of parametric and non-parametric memory components to enhance language generation models. RAG models are fine-tuned on a wide range of tasks and achieve state-of-the-art results in open-domain question answering, abstractive question answering, Jeopardy question generation, and fact verification tasks. The paper presents detailed methods, experimental setup, and results demonstrating the effectiveness of RAG models.