# Are Large Language Models Fit For Guided Reading?

arXiv: https://arxiv.org/abs/2305.10645 · HTML: https://mlsanity.com/paper/2305.10645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [Recent Advances in Neural Question Generation](https://mlsanity.com/paper/1905.08949/) (2019)
- [Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization](https://mlsanity.com/paper/2203.14187/) (2022)
- [It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://mlsanity.com/paper/2109.03423/) (2021)
- [Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features](https://mlsanity.com/paper/1809.02637/) (2018)
- [Revisiting Precision and Recall Definition for Generative Model Evaluation](https://mlsanity.com/paper/1905.05441/) (2019)

## Built on by (most cited first, 2 of 2)

- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [An Educator-Guided LLM Pedagogical Agent for Scaffolded Feedback in Conceptual Database Design](https://mlsanity.com/paper/2610.00870/) (2026)

## Abstract (arXiv)

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.