  Despite rapid advancements in large language models (LLMs), QG remains a
challenging problem due to its complicated process, open-ended nature, and the
diverse settings in which question generation occurs. A common approach to
address these challenges involves fine-tuning smaller, custom models using
datasets containing background context, question, and answer. However,
obtaining suitable domain-specific datasets with appropriate context is often
more difficult than acquiring question-answer pairs. In this paper, we
investigate training QG models using synthetic contexts generated by LLMs from
readily available question-answer pairs. We conduct a comprehensive study to
answer critical research questions related to the performance of models trained
on synthetic contexts and their potential impact on QG research and
applications. Our empirical results reveal: 1) contexts are essential for QG
tasks, even if they are synthetic; 2) fine-tuning smaller language models has
the capability of achieving better performances as compared to prompting larger
language models; and 3) synthetic context and real context could achieve
comparable performances. These findings highlight the effectiveness of
synthetic contexts in QG and paves the way for future advancements in the
field.
