mlsanityData to 7 Oct 2026

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

arXiv 2007.01282 ↗First posted 2 Jul 2020cs.CL cs.LGcited by 1,203

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Cited by 452 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method significantly improves when increasing the number of retrieved passages. This is evidence that generative models are good at aggregating and combining evidence from multiple passages.

Key facts. Cited by 1,203 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2020.

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Citing papers per year: 2021: 83; 2022: 149; 2023: 204; 2024: 264; 2025: 231; 2026: 272.

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50 most cited of 1,203 All 1,203 (JSON)

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