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

Elliott Wen; Rivindu Weerasekera; Shamane Siriwardhana; Suranga Nanayakkara

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2106.11517 v1 pith:CUUF62UH submitted 2021-06-22 cs.IR cs.AIcs.CL

Shamane Siriwardhana , Rivindu Weerasekera , Elliott Wen , Suranga Nanayakkara This is my paper · ORCID

classification cs.IRcs.AIcs.CL

keywords architectureend-to-endentirefine-tuneachieveaddressedansweringaugment

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks
cs.CL 2025-05 reject novelty 3.0 of 10

Fine-tuning Llama-3-8b and Mistral-7b-v0.3 on LLM-generated QA pairs from IBM Technotes can improve no-context QA scores over training on human-annotated TechQA data, but the evaluation may be inflated by test-documen...

Pith tools