Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
This work addresses a key engineering challenge in improving RAG systems for question-answering tasks, though it appears incremental as it builds on existing RAG methods.
The paper tackles the problem of fine-tuning the entire Retrieval Augmented Generation (RAG) architecture end-to-end for question-answering, showing that this approach outperforms the original RAG architecture.
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.