Education that suits the individual learning level is necessary to improve
students' understanding. The first step in achieving this purpose by using
large language models (LLMs) is to adjust the textual difficulty of the
response to students. This work analyzes how LLMs can implicitly adjust text
difficulty between user input and its generated text. To conduct the
experiments, we created a new dataset from Stack-Overflow to explore the
performance of question-answering-based conversation. Experimental results on
the Stack-Overflow dataset and the TSCC dataset, including multi-turn
conversation show that LLMs can implicitly handle text difficulty between user
input and its generated response. We also observed that some LLMs can surpass
humans in handling text difficulty and the importance of instruction-tuning.

通过使用大型语言模型（LLMs），调整生成的文本难度以适应学生，以达到提高学生理解能力的个性化教育目的。用于分析 LLMs 如何在用户输入和生成的文本之间隐式调整文本难度。通过从 Stack-Overflow 创建新的数据集来进行实验，探讨基于问答式对话的性能。实验结果表明，LLMs 可以隐式处理用户输入和生成响应之间的文本难度，并观察到有些 LLMs 在处理文本难度和指导调整方面超越了人类。