  Many open-domain questions are under-specified and thus have multiple
possible answers, each of which is correct under a different interpretation of
the question. Answering such ambiguous questions is challenging, as it requires
retrieving and then reasoning about diverse information from multiple passages.
We present a new state-of-the-art for answering ambiguous questions that
exploits a database of unambiguous questions generated from Wikipedia. On the
challenging ASQA benchmark, which requires generating long-form answers that
summarize the multiple answers to an ambiguous question, our method improves
performance by 15% (relative improvement) on recall measures and 10% on
measures which evaluate disambiguating questions from predicted outputs.
Retrieving from the database of generated questions also gives large
improvements in diverse passage retrieval (by matching user questions q to
passages p indirectly, via questions q' generated from p).
