用大模型自动生成个性化英语阅读理解题，提升内容相关性与适切性。
Generating Reading Comprehension Exercises with Large Language Models for Educational Applications
- 分三步生成：先候选、再筛选、最后优化质量。
- 生成题目在相关性与认知难度上显著优于基线方法。
- 适合教育科技开发者与语言教学研究者使用。
随着大语言模型（LLMs）的快速发展，其在教育领域的应用日益广泛，尤其在自动文本生成方面展现出巨大潜力，可实现智能、自适应的学习内容生成。本文提出一种名为阅读理解题生成（RCEG）的新框架，能够自动创建高质量、个性化的英语阅读理解练习题。RCEG首先利用微调后的LLM生成内容候选，接着通过判别器筛选最优结果，最终显著提升生成内容的质量。为评估性能，构建了一个专门用于英语阅读理解的测试数据集，并采用内容多样性、事实准确性、语言毒性及教学契合度等综合指标进行分析。实验结果表明，RCEG在生成题目的相关性和认知适切性方面均有显著提升。
原文摘要 · Abstract (English)
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.
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