利用大语言模型生成阅读理解练习用于教育应用
Generating Reading Comprehension Exercises with Large Language Models for Educational Applications
AI总结：
本文提出RCEG框架，利用大语言模型自动生成高质量、个性化的英语阅读理解练习，通过内容候选生成、判别器筛选和综合评估指标提升练习质量。
摘要：
随着大语言模型（LLMs）的快速发展，其应用范围显著扩大。在教育领域，LLMs展现出巨大潜力，特别是在自动文本生成方面，能够创建智能和自适应的学习内容。本文提出了一种新的LLMs框架，称为阅读理解练习生成（RCEG）。它可以自动生成高质量且个性化的英语阅读理解练习。首先，RCEG使用微调的LLMs生成内容候选。然后，它使用判别器选择最佳候选。最后，生成内容的质量得到了显著提升。为了评估RCEG的性能，构建了一个专门用于英语阅读理解的数据集进行实验，并使用综合评估指标分析实验结果。这些指标包括内容多样性、事实准确性、语言毒性以及教学适应性。实验结果表明，RCEG显著提高了生成练习的相关性和认知适切性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
