Papers › Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Suranga Nanayakkara
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | SQuAD | RAG-end2end | Exact Match | 40.02 | #2 of 2 | Archive leaderboard | report |
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