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%.

该研究旨在通过变压器模型和提示工程找到从文本数据中生成问题的最佳方法。通过对 SQuAD 问答数据集上的预训练 distilBERT 模型的微调以生成问题，并应用 LLaMA 模型以有效地生成问题，将生成的问题与 SQuAD 数据集中的基准问题进行比较，评估了四种不同提示的有效性，其中有 30% 的提示生成的问题获得了大于 70% 的高相似度分数。