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

Authors: Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Suranga Nanayakkara

for associated code, see https://github.com/huggingface/transformers/tree/master/examples/research_projects/rag-end2end-retriever

License: CC BY-SA 4.0

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.

Submitted to arXiv on 22 Jun. 2021

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