# Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering

**Authors:** Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Suranga Nanayakkara  
**arXiv:** [2106.11517](https://arxiv.org/abs/2106.11517)  
**Date:** 2021-06-22  
**Summary page:** https://www.summarizepaper.com/fr/paper/2106.11517v1/

## Abstract

In this paper, we illustrate how to fine-tune the entire Retrieval Augment Generation (RAG) architecture in an end-to-end manner. We highlighted the main engineering challenges that needed to be addressed to achieve this objective. We also compare how end-to-end RAG architecture outperforms the original RAG architecture for the task of question answering. We have open-sourced our implementation in the HuggingFace Transformers library.