On the Automatic Generation and Simplification of Children's Stories
Maria Valentini Jennifer Weber Jesus Salcido Téa Wright Eliana Colunga Katharina Kann
With recent advances in large language models (LLMs), the concept of
automatically generating children's educational materials has become
increasingly realistic. Working toward the goal of age-appropriate simplicity
in generated educational texts, we first examine the ability of several popular
LLMs to generate stories with properly adjusted lexical and readability levels.
We find that, in spite of the growing capabilities of LLMs, they do not yet
possess the ability to limit their vocabulary to levels appropriate for younger
age groups. As a second experiment, we explore the ability of state-of-the-art
lexical simplification models to generalize to the domain of children's stories
and, thus, create an efficient pipeline for their automatic generation. In
order to test these models, we develop a dataset of child-directed lexical
simplification instances, with examples taken from the LLM-generated stories in
our first experiment. We find that, while the strongest-performing current
lexical simplification models do not perform as well on material designed for
children due to their reliance on large language models behind the scenes, some
models that still achieve fairly strong results on general data can mimic or
even improve their performance on children-directed data with proper
fine-tuning, which we conduct using our newly created child-directed
simplification dataset.