Text Simplification for Children: Evaluating LLMs vis-à-vis Human Experts

Large Language Models (LLMs) can facilitate teaching and learning by generating educational content for specific grade levels.Generation of age and grade-appropriate materials often involves text simplification.Previous work evaluating LLMs' text simplification for children in educational context showed that LLMs can reduce lexical complexity of a text and improve its readability, but they often use vocabulary that is still too difficult for the targeted grade.In this study we focus on GPT-4o, the most advanced LLM at the time of writing.We evaluate its ability to simplify text for elementary school children and compare its performance vis--vis the human baseline.We show that GPT-4o can successfully reduce text complexity on lexical, syntactic, and discourse levels.However, compared to human experts, it generates syntactically more complex text.This suggests that further fine-tuning with focus on syntax is needed before LLM-generated output reaches the intended user group.