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Fine-tune the Entire RAG Architecture for Question-Answering

2021-06-22 03:17:59
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Suranga Nanayakkara
arXiv_CL
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.11517

PDF

https://arxiv.org/pdf/2106.11517.pdf

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