Artificial intelligence systems that generate text now influence how people search for information, learn new skills, obtain health advice, and communicate online. Yet these systems can behave in ways that are hard to predict. They can copy misleading patterns from their training data, produce text that is too complex for a reader's needs, and retain private, outdated, or incorrect content. This project studies how these systems learn, keep, and forget language patterns over time, and uses that knowledge to build systems that are easier to control and safer to use. The results can help create text at appropriate reading levels for second language learners, patients reading health information, and people with communication or cognitive challenges. The project also develops methods to remove harmful patterns without weakening a model's general ability to generate useful text. The project advances reliable artificial intelligence, improves access to understandable information, and trains students through coursework, mentoring, freely available tools, and interdisciplinary workshops to support science and public well-being. The project develops a linguistically grounded framework for robust and interpretable neural language models. It creates methods for controlled text generation and paraphrasing that allow models to follow user-defined lexical, syntactic, and discourse constraints. These methods combine instruction tuning, explicit control signals, multi-objective optimiza