This study introduces the leveled-text generation task, aiming to rewrite
educational materials to specific readability levels while preserving meaning.
We assess the capability of GPT-3.5, LLaMA-2 70B, and Mixtral 8x7B, to generate
content at various readability levels through zero-shot and few-shot prompting.
Evaluating 100 processed educational materials reveals that few-shot prompting
significantly improves performance in readability manipulation and information
preservation. LLaMA-2 70B performs better in achieving the desired difficulty
range, while GPT-3.5 maintains original meaning. However, manual inspection
highlights concerns such as misinformation introduction and inconsistent edit
distribution. These findings emphasize the need for further research to ensure
the quality of generated educational content.

本研究介绍了分级文本生成任务，旨在将教育材料重写为特定可读性水平同时保持意义不变。通过零 - shot 和少量样本提示，我们评估了 GPT-3.5、LLaMA-2 70B 和 Mixtral 8x7B 在不同可读性水平上生成内容的能力。对 100 份处理过的教育材料进行评估，结果显示少量样本提示显著提高了可读性操作和信息保留的性能。LLaMA-2 70B 在实现所需难度范围方面表现更好，而 GPT-3.5 保持了原始意义。然而，手动检查还揭示出了诸如引入错误信息和不一致的编辑分布等问题。这些发现强调了进一步研究以确保生成的教育内容的质量的需求。