Generating Reading Comprehension Exercises with Large Language Models for Educational Applications

TL;DR

This paper introduces RCEG, a framework utilizing fine-tuned large language models and a discriminator to automatically generate high-quality, personalized English reading comprehension exercises for educational purposes.

Contribution

It presents a novel LLM-based framework with a discriminator for improved quality and relevance in automatically generated reading exercises, along with a dedicated dataset and comprehensive evaluation.

Findings

RCEG significantly enhances exercise relevance and cognitive appropriateness.

The framework achieves high content diversity and factual accuracy.

Experimental results validate the effectiveness of the proposed method.

Abstract

With the rapid development of large language models (LLMs), the applications of LLMs have grown substantially. In the education domain, LLMs demonstrate significant potential, particularly in automatic text generation, which enables the creation of intelligent and adaptive learning content. This paper proposes a new LLMs framework, which is named as Reading Comprehension Exercise Generation (RCEG). It can generate high-quality and personalized English reading comprehension exercises automatically. Firstly, RCEG uses fine-tuned LLMs to generate content candidates. Then, it uses a discriminator to select the best candidate. Finally, the quality of the generated content has been improved greatly. To evaluate the performance of RCEG, a dedicated dataset for English reading comprehension is constructed to perform the experiments, and comprehensive evaluation metrics are used to analyze the experimental results. These metrics include content diversity, factual accuracy, linguistic toxicity, and pedagogical alignment. Experimental results show that RCEG significantly improves the relevance and cognitive appropriateness of the generated exercises.