Generating and Evaluating Multi-Level Text Simplification: A Case Study on Italian

Recent advances in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, yet controlling model outputs remains a challenge. In this study, we explore the use of LLMs to generate high-quality synthetic data for Automatic Text Simplification (ATS), evaluating the ability of models fine-tuned on Italian to produce multiple simplified versions of the same original sentence that vary in readability and in their lexical and (morpho-)syntactic characteristics. The approach is tested across two domains, Wikipedia and Public Administration, allowing us to explore domain sensitivity. Additionally, we compare the linguistic phenomena observed in the generated data with those found in ATS resources previously created through manual or semi-automatic methods. Our results suggest that the best-performing LLM can generate linguistically diverse simplifications that align with known simplification patterns, offering a promising direction for building reliable ATS resources, including simplifications suited to varying levels of reader proficiency.