The increase in academic dishonesty cases among college students has raised
concern, particularly due to the shift towards online learning caused by the
pandemic. We aim to develop and implement a method capable of generating
tailored questions for each student. The use of Automatic Question Generation
(AQG) is a possible solution. Previous studies have investigated AQG frameworks
in education, which include validity, user-defined difficulty, and personalized
problem generation. Our new AQG approach produces logical equivalence problems
for Discrete Mathematics, which is a core course for year-one computer science
students. This approach utilizes a syntactic grammar and a semantic attribute
system through top-down parsing and syntax tree transformations. Our
experiments show that the difficulty level of questions generated by our AQG
approach is similar to the questions presented to students in the textbook [1].
These results confirm the practicality of our AQG approach for automated
question generation in education, with the potential to significantly enhance
learning experiences.

通过使用自动化问题生成的方法，我们实现了针对每个学生的定制问题，研究了针对离散数学的逻辑等价问题的自动问题生成方法，并证实了该方法在教育中自动化问题生成方面的实用性。

命题逻辑等价关系自动问句生成

Automatic question generation for propositional logical equivalences

A common way of assessing language learners' mastery of vocabulary is via
multiple-choice cloze (i.e., fill-in-the-blank) questions. But the creation of
test items can be laborious for individual teachers or in large-scale language
programs. In this paper, we evaluate a new method for automatically generating
these types of questions using large language models (LLM). The VocaTT
(vocabulary teaching and training) engine is written in Python and comprises
three basic steps: pre-processing target word lists, generating sentences and
candidate word options using GPT, and finally selecting suitable word options.
To test the efficiency of this system, 60 questions were generated targeting
academic words. The generated items were reviewed by expert reviewers who
judged the well-formedness of the sentences and word options, adding comments
to items judged not well-formed. Results showed a 75% rate of well-formedness
for sentences and 66.85% rate for suitable word options. This is a marked
improvement over the generator used earlier in our research which did not take
advantage of GPT's capabilities. Post-hoc qualitative analysis reveals several
points for improvement in future work including cross-referencing
part-of-speech tagging, better sentence validation, and improving GPT prompts.

使用大型语言模型自动生成填空题，并评估其有效性和适用性，通过生成适当的句子和选择合适的答案，以提高学生的词汇掌握水平。

使用 GPT-turbo 3.5 自动生成英语词汇多选填空问题

Automated Generation of Multiple-Choice Cloze Questions for Assessing  English Vocabulary Using GPT-turbo 3.5

Automated question generation is an important approach to enable
personalisation of English comprehension assessment. Recently,
transformer-based pretrained language models have demonstrated the ability to
produce appropriate questions from a context paragraph. Typically, these
systems are evaluated against a reference set of manually generated questions
using n-gram based metrics, or manual qualitative assessment. Here, we focus on
a fully automated multiple-choice question generation (MCQG) system where both
the question and possible answers must be generated from the context paragraph.
Applying n-gram based approaches is challenging for this form of system as the
reference set is unlikely to capture the full range of possible questions and
answer options. Conversely manual assessment scales poorly and is expensive for
MCQG system development. In this work, we propose a set of performance criteria
that assess different aspects of the generated multiple-choice questions of
interest. These qualities include: grammatical correctness, answerability,
diversity and complexity. Initial systems for each of these metrics are
described, and individually evaluated on standard multiple-choice reading
comprehension corpora.

本文提出一种多选题智能生成系统，利用预训练的语言模型，根据规定的性能指标，通过读取理解语料库来生成语法准确，具有可回答性、多样性和复杂性的问题及选项。

多项选择题生成：面向自动化评估框架的发展

Multiple-Choice Question Generation: Towards an Automated Assessment Framework

Prior work on automated question generation has almost exclusively focused on
generating simple questions whose answers can be extracted from a single
document. However, there is an increasing interest in developing systems that
are capable of more complex multi-hop question generation, where answering the
questions requires reasoning over multiple documents. In this work, we
introduce a series of strong transformer models for multi-hop question
generation, including a graph-augmented transformer that leverages relations
between entities in the text. While prior work has emphasized the importance of
graph-based models, we show that we can substantially outperform the
state-of-the-art by 5 BLEU points using a standard transformer architecture. We
further demonstrate that graph-based augmentations can provide complimentary
improvements on top of this foundation. Interestingly, we find that several
important factors--such as the inclusion of an auxiliary contrastive objective
and data filtering could have larger impacts on performance. We hope that our
stronger baselines and analysis provide a constructive foundation for future
work in this area.

该论文探讨了自动化生成问题的新领域：多跳问题生成。通过引入一系列强大的转换器模型，包括使用多文档之间关系增强的图形增强转换器，比简单的 transformer 模型提高了 5 个 BLEU 点，并验证了数据过滤等能极大地提升模型性能。