Do LLMs Implicitly Determine the Suitable Text Difficulty for Users?
Seiji Gobara Hidetaka Kamigaito Taro Watanabe
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.