Meta AI2020
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis and colleagues describe a model that combines retrieved material with generation for knowledge-intensive language tasks.
What the source reports
- The paper presents retrieval-augmented generation for knowledge-intensive NLP tasks.
- It makes retrieval a distinct contribution to a language-model workflow.
Why it matters here
Chapter 4 explains what RAG contributes: bringing relevant retrieved material into a generative workflow.
Guided Intelligence interpretation
The source does not endorse Guided Intelligence. It helps distinguish retrieval and generation from the separate work of preserving governed reusable understanding.