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This is a Literature Review study.

August 18, 2025Open Access

Learning to Retrieve, Generate, and Compress: A Unified View of Efficient RAG

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Authors

FBFaruq Brontes JGJeanie GenesisLancaster University ZNZachariah Noa

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Implication

Survey reveals computational challenges and efficiency methods for improved retrieval-augmented generation in AI, highlighting key open research directions.

Key Points

Retrieval-augmented generation improves factuality and adaptability of AI systems, but presents unique computational challenges.
Key metrics highlight trade-offs between retrieval cost, generation accuracy, and document redundancy across multiple settings.
This survey categorizes RAG paradigms and analyzes efficiency-oriented methods to minimize latency and memory consumption.
Future directions include optimizing retrieval-aware generation, multilingual integration, and enhancing transparency of RAG systems.

Cite This Study

Brontes et al. (2025) studied this question.

synapsesocial.com/papers/68af453fad7bf08b1ead2c90 https://doi.org/10.20944/preprints202508.1211.v1

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Implication

Survey reveals computational challenges and efficiency methods for improved retrieval-augmented generation in AI, highlighting key open research directions.

Learning to Retrieve, Generate, and Compress: A Unified View of Efficient RAG

Discussion

Member takes

Implication

Key Points

Cite This Study

Learning to Retrieve, Generate, and Compress: A Unified View of Efficient RAG

Discussion

Member takes

Implication

Key Points

Cite This Study

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