Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks¶
Lewis, P. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Reality Monitoring
- Cognitive science (origin) — the source-monitoring framework distinguishing memories of perceived events from imagined ones, and the systematic errors when the discrimination fails. Generative-AI safety — distinguishing retrieved factual claims from parametric hallucinations; retrieval-augmented architectures are essentially reality-monitoring systems that tag generations with source metadata.
This sourceIntroduces retrieval-augmented generation, separating retrieved grounded claims from parametric model output.
- Cognitive science (origin) — the source-monitoring framework distinguishing memories of perceived events from imagined ones, and the systematic errors when the discrimination fails. Generative-AI safety — distinguishing retrieved factual claims from parametric hallucinations; retrieval-augmented architectures are essentially reality-monitoring systems that tag generations with source metadata.
Domain-specific¶
Verification¶
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