  Open-domain questions are likely to be open-ended and ambiguous, leading to
multiple valid answers. Existing approaches typically adopt the
rerank-then-read framework, where a reader reads top-ranking evidence to
predict answers. According to our empirical analysis, this framework faces
three problems: first, to leverage a large reader under a memory constraint,
the reranker should select only a few relevant passages to cover diverse
answers, while balancing relevance and diversity is non-trivial; second, the
small reading budget prevents the reader from accessing valuable retrieved
evidence filtered out by the reranker; third, when using a generative reader to
predict answers all at once based on all selected evidence, whether a valid
answer will be predicted also pathologically depends on the evidence of some
other valid answer(s). To address these issues, we propose to answer
open-domain multi-answer questions with a recall-then-verify framework, which
separates the reasoning process of each answer so that we can make better use
of retrieved evidence while also leveraging large models under the same memory
constraint. Our framework achieves state-of-the-art results on two multi-answer
datasets, and predicts significantly more gold answers than a rerank-then-read
system that uses an oracle reranker.
