Generating Educational Materials with Different Levels of Readability using LLMs

We assess the capability of GPT-3.5, LLaMA-2 iterative editing to ensure that the revised texts meet the 70B, and Mixtral 8x7B, to generate content at various readability desired difficulty criteria. This readability assessment is based on levels through zero-shot and few-shot prompting. Evaluating 100 various linguistic features, with sentence length and word frequency processed educational materials reveals that few-shot prompting identified as key factors in previous studies [11]. Although this significantly improves performance in readability manipulation and process appears straightforward, accurately adjusting these elements information preservation. LLaMA-2 70B performs better in achieving to achieve the target reading difficulty is challenging. This the desired difficulty range, while GPT-3.5 maintains original task becomes even more complex for young learners, where factors meaning. However, manual inspection highlights concerns such such as decodability [19], information load [15], and other elements as misinformation introduction and inconsistent edit distribution.