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。

基于 Prompt-Engineering 和 Transformer 的问题生成和评估

Prompt-Engineering and Transformer-based Question Generation and  Evaluation

We introduce Goat, a fine-tuned LLaMA model that significantly outperforms
GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated
dataset, Goat achieves state-of-the-art performance on BIG-bench arithmetic
sub-task. In particular, the zero-shot Goat-7B matches or even surpasses the
accuracy achieved by the few-shot PaLM-540B. Surprisingly, Goat can achieve
near-perfect accuracy on large-number addition and subtraction through
supervised fine-tuning only, which is almost impossible with previous
pretrained language models, such as Bloom, OPT, GPT-NeoX, etc. We attribute
Goat's exceptional performance to LLaMA's consistent tokenization of numbers.
To tackle more challenging tasks like large-number multiplication and division,
we propose an approach that classifies tasks based on their learnability, and
subsequently decomposes unlearnable tasks, such as multi-digit multiplication
and division, into a series of learnable tasks by leveraging basic arithmetic
principles. We thoroughly examine the performance of our model, offering a
comprehensive evaluation of the effectiveness of our proposed decomposition
steps. Additionally, Goat-7B can be easily trained using LoRA on a 24GB VRAM
GPU, facilitating reproducibility for other researchers. We release our model,
dataset, and the Python script for dataset generation.

Goat，用 LLaMA 模型细调后，在一系列算术任务中显著优于 GPT-4，特别是在大数字加法和减法方面表现出了非常高的准确性。 对于更具挑战性的任务，如大数字乘法和除法，我们提出了一种根据其可学习性分类任务的方法，并通过利用基本算术原则将不可学习的任务（如多位数乘法和除法）分解为一系列可学习的任务。