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

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering