  Open-domain Question Answering models which directly leverage question-answer
(QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show
promise in terms of speed and memory compared to conventional models which
retrieve and read from text corpora. QA-pair retrievers also offer
interpretable answers, a high degree of control, and are trivial to update at
test time with new knowledge. However, these models lack the accuracy of
retrieve-and-read systems, as substantially less knowledge is covered by the
available QA-pairs relative to text corpora like Wikipedia. To facilitate
improved QA-pair models, we introduce Probably Asked Questions (PAQ), a very
large resource of 65M automatically-generated QA-pairs. We introduce a new
QA-pair retriever, RePAQ, to complement PAQ. We find that PAQ preempts and
caches test questions, enabling RePAQ to match the accuracy of recent
retrieve-and-read models, whilst being significantly faster. Using PAQ, we
train CBQA models which outperform comparable baselines by 5%, but trail RePAQ
by over 15%, indicating the effectiveness of explicit retrieval. RePAQ can be
configured for size (under 500MB) or speed (over 1K questions per second)
whilst retaining high accuracy. Lastly, we demonstrate RePAQ's strength at
selective QA, abstaining from answering when it is likely to be incorrect. This
enables RePAQ to ``back-off" to a more expensive state-of-the-art model,
leading to a combined system which is both more accurate and 2x faster than the
state-of-the-art model alone.
