  We leverage generative large language models for language learning
applications, focusing on estimating the difficulty of foreign language texts
and simplifying them to lower difficulty levels. We frame both tasks as
prediction problems and develop a difficulty classification model using labeled
examples, transfer learning, and large language models, demonstrating superior
accuracy compared to previous approaches. For simplification, we evaluate the
trade-off between simplification quality and meaning preservation, comparing
zero-shot and fine-tuned performances of large language models. We show that
meaningful text simplifications can be obtained with limited fine-tuning. Our
experiments are conducted on French texts, but our methods are
language-agnostic and directly applicable to other foreign languages.
