  Recent state-of-the-art open-domain QA models are typically based on a two
stage retriever-reader approach in which the retriever first finds the relevant
knowledge/passages and the reader then leverages that to predict the answer.
Prior work has shown that the performance of the reader usually tends to
improve with the increase in the number of these passages. Thus,
state-of-the-art models use a large number of passages (e.g. 100) for
inference. While the reader in this approach achieves high prediction
performance, its inference is computationally very expensive. We humans, on the
other hand, use a more efficient strategy while answering: firstly, if we can
confidently answer the question using our already acquired knowledge then we do
not even use the external knowledge, and in the case when we do require
external knowledge, we don't read the entire knowledge at once, instead, we
only read that much knowledge that is sufficient to find the answer. Motivated
by this procedure, we ask a research question "Can the open-domain QA reader
utilize external knowledge efficiently like humans without sacrificing the
prediction performance?"
  Driven by this question, we explore an approach that utilizes both
'closed-book' (leveraging knowledge already present in the model parameters)
and 'open-book' inference (leveraging external knowledge). Furthermore, instead
of using a large fixed number of passages for open-book inference, we
dynamically read the external knowledge in multiple 'knowledge iterations'.
Through comprehensive experiments on NQ and TriviaQA datasets, we demonstrate
that this dynamic reading approach improves both the 'inference efficiency' and
the 'prediction accuracy' of the reader. Comparing with the FiD reader, this
approach matches its accuracy by utilizing just 18.32% of its reader inference
cost and also outperforms it by achieving up to 55.10% accuracy on NQ Open.
