Text and audio simplification to increase information comprehension are
important in healthcare. With the introduction of ChatGPT, an evaluation of its
simplification performance is needed. We provide a systematic comparison of
human and ChatGPT simplified texts using fourteen metrics indicative of text
difficulty. We briefly introduce our online editor where these simplification
tools, including ChatGPT, are available. We scored twelve corpora using our
metrics: six text, one audio, and five ChatGPT simplified corpora. We then
compare these corpora with texts simplified and verified in a prior user study.
Finally, a medical domain expert evaluated these texts and five, new ChatGPT
simplified versions. We found that simple corpora show higher similarity with
the human simplified texts. ChatGPT simplification moves metrics in the right
direction. The medical domain expert evaluation showed a preference for the
ChatGPT style, but the text itself was rated lower for content retention.

我们通过比较人类和 ChatGPT 简化的文本，使用 14 个反映文本难度的指标，评估 ChatGPT 的简化性能，并找到简化的文本与人类简化文本之间存在较高的相似性。医学领域的专家评估显示出对 ChatGPT 风格的偏好，但文本内容的保留程度较低。

文本和音频简化：人类与 ChatGPT

Text and Audio Simplification: Human vs. ChatGPT

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。

LLM 是否隐含确定适合用户的文本难度？

Do LLMs Implicitly Determine the Suitable Text Difficulty for Users?

Given a task, human learns from easy to hard, whereas the model learns
randomly. Undeniably, difficulty insensitive learning leads to great success in
NLP, but little attention has been paid to the effect of text difficulty in
NLP. In this research, we propose the Human Learning Matching Index (HLM Index)
to investigate the effect of text difficulty. Experiment results show: (1) LSTM
has more human-like learning behavior than BERT. (2) UID-SuperLinear gives the
best evaluation of text difficulty among four text difficulty criteria. (3)
Among nine tasks, some tasks' performance is related to text difficulty,
whereas some are not. (4) Model trained on easy data performs best in easy and
medium data, whereas trains on a hard level only perform well on hard data. (5)
Training the model from easy to hard leads to fast convergence.

该研究探讨了文本难度对自然语言处理领域的影响，并提出了人类学习匹配指数 (HLM Index)。实验结果表明 LSTM 的学习行为比 BERT 更类似于人类学习行为，使用 UID-SuperLinear 可以更好地评估文本难度。在九项任务中，有些任务的表现与文本难度有关，而有些任务则没有关系。在训练模型时，使用简单数据进行训练可以获得最佳效果，并且采用了从易到难的训练方法可以实现更快的收敛。