  Question generation (QG) is the task of generating a valid and fluent
question based on a given context and the target answer. According to various
purposes, even given the same context, instructors can ask questions about
different concepts, and even the same concept can be written in different ways.
However, the evaluation for QG usually depends on single reference-based
similarity metrics, such as n-gram-based metric or learned metric, which is not
sufficient to fully evaluate the potential of QG methods. To this end, we
propose to paraphrase the reference question for a more robust QG evaluation.
Using large language models such as GPT-3, we created semantically and
syntactically diverse questions, then adopt the simple aggregation of the
popular evaluation metrics as the final scores. Through our experiments, we
found that using multiple (pseudo) references is more effective for QG
evaluation while showing a higher correlation with human evaluations than
evaluation with a single reference.
