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Open-domain Question Answering models which directly leverage question-answer(QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, showpromise in terms of speed and memory compared to conventional models whichretrieve and read from text corpora. QA-pair retrievers also offerinterpretable answers, a high degree of control, and are trivial to update attest time with new knowledge. However, these models lack the accuracy ofretrieve-and-read systems, as substantially less knowledge is covered by theavailable QA-pairs relative to text corpora like Wikipedia. To facilitateimproved QA-pair models, we introduce Probably Asked Questions (PAQ), a verylarge resource of 65M automatically-generated QA-pairs. We introduce a newQA-pair retriever, RePAQ, to complement PAQ. We find that PAQ preempts andcaches test questions, enabling RePAQ to match the accuracy of recentretrieve-and-read models, whilst being significantly faster. Using PAQ, wetrain CBQA models which outperform comparable baselines by 5%, but trail RePAQby over 15%, indicating the effectiveness of explicit retrieval. RePAQ can beconfigured for size (under 500MB) or speed (over 1K questions per second)whilst retaining high accuracy. Lastly, we demonstrate RePAQ's strength atselective QA, abstaining from answering when it is likely to be incorrect. Thisenables 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 thestate-of-the-art model alone.

PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them