Prompt-Engineering and Transformer-based Question Generation and
  Evaluation
Rubaba Amyeen
Question generation has numerous applications in the educational context.
Question generation can prove helpful for students when reviewing content and
testing themselves. Furthermore, a question generation model can aid teachers
by lessening the burden of creating assessments and other practice material.
This paper aims to find the best method to generate questions from textual data
through a transformer model and prompt engineering. In this research, we
finetuned a pretrained distilBERT model on the SQuAD question answering dataset
to generate questions. In addition to training a transformer model, prompt
engineering was applied to generate questions effectively using the LLaMA
model. The generated questions were compared against the baseline questions in
the SQuAD dataset to evaluate the effectiveness of four different prompts. All
four prompts demonstrated over 60% similarity on average. Of the
prompt-generated questions, 30% achieved a high similarity score greater than
70%.