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).

翻译：许多开放领域的问题存在定义不充分的现象，因此具有多个可能的答案，每个答案在问题的不同解释下都是正确的。回答此类模糊问题颇具挑战性，因为它需要从多个段落中检索并推理多样化的信息。我们提出了一种回答模糊问题的最新方法，该方法利用了从维基百科生成的无歧义问题数据库。在具有挑战性的ASQA基准测试中，该基准要求生成以长文本形式总结模糊问题多个答案的答案，我们的方法在召回率指标上提升了15%（相对提升），在评估从预测输出中消除歧义问题的指标上提升了10%。从生成的问题数据库中进行检索还能在多样化段落检索中带来巨大改进（通过将用户问题q间接匹配到段落p，即利用从p生成的问题q'）。