Retrieval & language models · 2020 · Patrick Lewis et al.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Combine parametric generation with explicit non-parametric memory so evidence can be retrieved, updated, and inspected separately from model weights.
The central move
Combine parametric generation with explicit non-parametric memory so evidence can be retrieved, updated, and inspected separately from model weights.
Why it had to exist
A model’s parameters are an opaque, lossy, and difficult-to-update store of factual knowledge. Knowledge-intensive answers need an external evidence path.
Where it leads
Search plus generation → grounded assistants → context engineering, provenance, and evidence-aware evaluation.