Sentence Simplification

Sentence simplification aims to rewrite complex text into easier-to-understand versions while preserving meaning, primarily benefiting individuals with reading difficulties or language learners. Current research focuses on improving the accuracy and fluency of simplification using various approaches, including large language models (LLMs) like GPT-4 and transformer-based sequence-to-sequence models, often incorporating techniques like edit-constrained decoding and lexical paraphrasing. These advancements are crucial for enhancing accessibility to information across diverse fields, from medical reports to educational materials, and for developing more robust evaluation metrics that accurately assess both simplification and meaning preservation. The field is also actively developing new datasets and evaluation metrics to better assess model performance and address challenges like factuality preservation.