---
title: 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2005-11401-retrieval-augmented-generation.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Patrick Lewis and 11 coauthors (listed on the arXiv abs page), Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: RAG'
tags: arxiv, rag, retrieval, language-models
arxiv_id: 2005.11401
alias: 2005.11401-retrieval-augmented-generation
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks'
    publisher: arXiv
    authors: 'Patrick Lewis and 11 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/2005.11401
    version_read: 'v4 (12 Apr 2021)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

## Summary
[Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) (2020) is the original RAG paper. It combined a parametric language model with a non-parametric retriever so that the model could pull in documents at inference time instead of relying only on what was stored in its weights.

What it introduced: the RAG architecture that pairs a dense retriever (built on a DPR-style index) with a seq2seq generator, trained so that retrieval and generation work together on knowledge-intensive tasks.

What it led to: the default pattern behind today's assistant "RAG" stacks, later work such as REALM, Self-RAG, and RAGAS, and the embedding models that feed those indexes. Live notes: [genai-rag-embeddings-vector-search](https://iinventedlight.com/library/notes/genai-rag-embeddings-vector-search/), [2310-11511-self-rag](https://iinventedlight.com/library/notes/2310-11511-self-rag/), and [2309-15217-ragas-rag-evaluation](https://iinventedlight.com/library/notes/2309-15217-ragas-rag-evaluation/). This batch also points to Sentence-BERT and, in batch D, REALM.

Where to read it: https://arxiv.org/abs/2005.11401 (v4, last revised 12 Apr 2021). Accepted at NeurIPS 2020.

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).