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.

翻译：问答（QA）模型通常依赖大规模训练数据集，这促使研究人员开发数据生成框架以降低人工标注成本。尽管近期研究已致力于生成单跨度答案的合成问题，但尚无针对多段非连续答案的列表型问题生成研究。为填补这一空白，我们提出LIQUID——一种从无标注语料自动生成列表问答数据集的框架。首先，我们将维基百科或PubMed中的段落转换为摘要，并从摘要文本中提取命名实体作为候选答案，从而选取上下文语义相关的答案，适用于构建列表型问题。随后，我们使用现成的问题生成器，基于提取的实体和原始段落创建问题。最后，通过迭代过滤和答案扩展确保答案的准确性与完整性。实验表明，使用我们的合成数据可将现有最佳列表问答模型的精确匹配F1分数显著提升：在MultiSpanQA上提升5.0，在Quoref上提升1.9，并在三个BioASQ基准测试中平均提升2.8。