# Prompt-Engineering and Transformer-based Question Generation and Evaluation

arXiv: https://arxiv.org/abs/2310.18867 · HTML: https://mlsanity.com/paper/2310.18867/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 29 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://mlsanity.com/paper/1908.04942/) (2019)
- [Recent Advances in Neural Question Generation](https://mlsanity.com/paper/1905.08949/) (2019)
- [Machine Comprehension by Text-to-Text Neural Question Generation](https://mlsanity.com/paper/1705.02012/) (2017)

## Built on by (most cited first, 1 of 1)

- [Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity](https://mlsanity.com/paper/2408.12850/) (2024)

## Abstract (arXiv)

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%.