  Designing high-quality educational questions is a challenging and
time-consuming task. In this work, we propose a novel approach that utilizes
prompt-based techniques to generate descriptive and reasoning-based questions.
However, current question-answering (QA) datasets are inadequate for conducting
our experiments on prompt-based question generation (QG) in an educational
setting. Therefore, we curate a new QG dataset called EduProbe for school-level
subjects, by leveraging the rich content of NCERT textbooks. We carefully
annotate this dataset as quadruples of 1) Context: a segment upon which the
question is formed; 2) Long Prompt: a long textual cue for the question (i.e.,
a longer sequence of words or phrases, covering the main theme of the context);
3) Short Prompt: a short textual cue for the question (i.e., a condensed
representation of the key information or focus of the context); 4) Question: a
deep question that aligns with the context and is coherent with the prompts. We
investigate several prompt-based QG methods by fine-tuning pre-trained
transformer-based large language models (LLMs), namely PEGASUS, T5, MBART, and
BART. Moreover, we explore the performance of two general-purpose pre-trained
LLMs such as Text-Davinci-003 and GPT-3.5-Turbo without any further training.
By performing automatic evaluation, we show that T5 (with long prompt)
outperforms all other models, but still falls short of the human baseline.
Under human evaluation criteria, TextDavinci-003 usually shows better results
than other models under various prompt settings. Even in the case of human
evaluation criteria, QG models mostly fall short of the human baseline. Our
code and dataset are available at: https://github.com/my625/PromptQG
