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

TL;DR

This paper illustrates how to fine-tune the entire Retrieval Augment Generation (RAG) architecture in an end-to-end manner and compares how end- to-end R AG architecture outperforms the original RAG architecture for the task of question answering.

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.