mlsanityData to 7 Oct 2026

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering

arXiv 2104.07242 ↗First posted 15 Apr 2021cs.CLcited by 5

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Abstract (arXiv)

In open-domain question answering (QA), retrieve-and-read mechanism has the inherent benefit of interpretability and the easiness of adding, removing, or editing knowledge compared to the parametric approaches of closed-book QA models. However, it is also known to suffer from its large storage footprint due to its document corpus and index. Here, we discuss several orthogonal strategies to drastically reduce the footprint of a retrieve-and-read open-domain QA system by up to 160x. Our results indicate that retrieve-and-read can be a viable option even in a highly constrained serving environment such as edge devices, as we show that it can achieve better accuracy than a purely parametric model with comparable docker-level system size.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

22 of 28 references.

References · 22

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10 more
  1. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1; 2025: 1.

5 papers.

  1. 2022A Survey for Efficient Open Domain Question Answeringcited by 20
  2. 2021Boosted Dense Retrievercited by 10
  3. 2022Bridging the Training-Inference Gap for Dense Phrase Retrievalcited by 1
  4. 2025PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generationcited by 1
  5. 2024Building Efficient and Effective OpenQA Systems for Low-Resource Languagescited by 0