Reproducing Developmental Features and Preserving Semantics in Child-Style Text Generation Using LLM

Abstract

A Large Language Model (LLM) has advanced natural language processing but primarily relies on adult-oriented texts, creating gaps in modeling child-style writing, particularly in Japanese with its unique developmental script shifts. This study explores whether an LLM can regenerate Japanese adult texts into child-style texts across 16 educational stages. Using high-difficulty base texts covering ten themes, we generated outputs in both zero-shot and few-shot settings, evaluating readability with jReadability and semantic similarity with BERT-based cosine metrics. The results demonstrate that the LLM effectively reproduces developmental linguistic features and maintains high semantic fidelity, indicating its potential for ethical child-style data augmentation.

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