This paper looks at the ability of large language models to participate in
educational guided reading. We specifically, evaluate their ability to generate
meaningful questions from the input text, generate diverse questions both in
terms of content coverage and difficulty of the questions and evaluate their
ability to recommend part of the text that a student should re-read based on
the student's responses to the questions. Based on our evaluation of ChatGPT
and Bard, we report that,
1) Large language models are able to generate high quality meaningful
questions that have high correlation with the input text, 2) They generate
diverse question that cover most topics in the input text even though this
ability is significantly degraded as the input text increases, 3)The large
language models are able to generate both low and high cognitive questions even
though they are significantly biased toward low cognitive question, 4) They are
able to effectively summarize responses and extract a portion of text that
should be re-read.

本篇研究探讨了大型语言模型在参与教育指导阅读方面的能力，着重评估它们从输入文本中生成有意义问题和多样化问题的能力，以及根据学生对问题的回答生成要求他们重新阅读一部分文本的能力。通过我们对 ChatGPT 和 Bard 的评估，我们得出结论：1）大型语言模型能够生成高质量的有意义问题，与输入文本有很高的相关性，2）它们生成覆盖输入文本大多数主题的多样化问题，尽管这种能力会随着输入文本的增加而明显降低，3）大型语言模型能够产生低和高认知问题，尽管它们明显偏向于低认知问题，4）它们能够有效地总结回答并提取应重新阅读的部分文本。

大型语言模型是否适用于导读阅读？

Are Large Language Models Fit For Guided Reading?

Question Generation is the task of automatically creating questions from
textual input. In this work we present a new Attentional Encoder--Decoder
Recurrent Neural Network model for automatic question generation. Our model
incorporates linguistic features and an additional sentence embedding to
capture meaning at both sentence and word levels. The linguistic features are
designed to capture information related to named entity recognition, word case,
and entity coreference resolution. In addition our model uses a copying
mechanism and a special answer signal that enables generation of numerous
diverse questions on a given sentence. Our model achieves state of the art
results of 19.98 Bleu_4 on a benchmark Question Generation dataset,
outperforming all previously published results by a significant margin. A human
evaluation also shows that these added features improve the quality of the
generated questions.

本篇论文提出了一种新的 Attentional Encoder-Decoder Recurrent Neural Network 模型用于自动生成问题，该模型融合语言特征和句子嵌入来捕捉句子和单词层面上的意义，并采用复制机制和特殊答案信号生成多样化的问题，在基准问题生成数据集上取得了 19.98 的 Bleu_4 结果，超过了以前发表的所有结果，并且人类评估显示这些新增的功能提高了生成的问题的质量。

利用答案焦点、语境和语言特征进行不同类型问题的神经生成

Neural Generation of Diverse Questions using Answer Focus, Contextual  and Linguistic Features

Automatic question generation aims to generate questions from a text passage
where the generated questions can be answered by certain sub-spans of the given
passage. Traditional methods mainly use rigid heuristic rules to transform a
sentence into related questions. In this work, we propose to apply the neural
encoder-decoder model to generate meaningful and diverse questions from natural
language sentences. The encoder reads the input text and the answer position,
to produce an answer-aware input representation, which is fed to the decoder to
generate an answer focused question. We conduct a preliminary study on neural
question generation from text with the SQuAD dataset, and the experiment
results show that our method can produce fluent and diverse questions.

本研究提出了一种基于神经编码器 - 解码器模型的自动问题生成方法，可以根据给定的段落生成多样化、有意义的问题并利用 SQuAD 数据集进行了初步实验研究，实验结果显示该方法可以产生流畅且多样化的问题。