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Existing benchmarks for open-domain question answering (ODQA) typically focuson questions whose answers can be extracted from a single paragraph. Bycontrast, many natural questions, such as "What players were drafted by theBrooklyn Nets?" have a list of answers. Answering such questions requiresretrieving and reading from many passages, in a large corpus. We introduceQAMPARI, an ODQA benchmark, where question answers are lists of entities,spread across many paragraphs. We created QAMPARI by (a) generating questionswith multiple answers from Wikipedia's knowledge graph and tables, (b)automatically pairing answers with supporting evidence in Wikipedia paragraphs,and (c) manually paraphrasing questions and validating each answer. We trainODQA models from the retrieve-and-read family and find that QAMPARI ischallenging in terms of both passage retrieval and answer generation, reachingan F1 score of 32.8 at best. Our results highlight the need for developing ODQAmodels that handle a broad range of question types, including single andmulti-answer questions.

QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs