  Question answering (QA) models often rely on large-scale training datasets,
which necessitates the development of a data generation framework to reduce the
cost of manual annotations. Although several recent studies have aimed to
generate synthetic questions with single-span answers, no study has been
conducted on the creation of list questions with multiple, non-contiguous spans
as answers. To address this gap, we propose LIQUID, an automated framework for
generating list QA datasets from unlabeled corpora. We first convert a passage
from Wikipedia or PubMed into a summary and extract named entities from the
summarized text as candidate answers. This allows us to select answers that are
semantically correlated in context and is, therefore, suitable for constructing
list questions. We then create questions using an off-the-shelf question
generator with the extracted entities and original passage. Finally, iterative
filtering and answer expansion are performed to ensure the accuracy and
completeness of the answers. Using our synthetic data, we significantly improve
the performance of the previous best list QA models by exact-match F1 scores of
5.0 on MultiSpanQA, 1.9 on Quoref, and 2.8 averaged across three BioASQ
benchmarks.
