  Question Answer Generation (QAG) is an effective data augmentation technique
to improve the accuracy of question answering systems, especially in
low-resource domains. While recent pretrained and large language model-based
QAG methods have made substantial progress, they face the critical issue of
redundant QA pair generation, affecting downstream QA systems. Implicit
diversity techniques such as sampling and diverse beam search are proven
effective solutions but often yield smaller diversity. We present explicit
diversity conditions for QAG, focusing on spatial aspects, question types, and
entities, substantially increasing diversity in QA generation. Our work
emphasizes the need of explicit diversity conditions for generating diverse
question-answer synthetic data by showing significant improvements in
downstream QA task over existing widely adopted implicit diversity techniques.
In particular, generated QA pairs from explicit diversity conditions when used
to train the downstream QA model results in an average 4.1% exact match and
4.5% F1 improvement over QAG from implicit sampling techniques on SQuADDU. Our
work emphasizes the need for explicit diversity conditions even more in
low-resource datasets (SubjQA), where average downstream QA performance
improvements are around 12% EM.
