Advancing Explainable Natural Language Generation (NLG): Techniques, Challenges, and Applications

Natural language generation (NLG) lies at the core of applications ranging from conversational agents to content creation. Despite its advances, NLG systems often operate as "black boxes," leaving developers and users uncertain about their decision-making processes. Explainable AI (XAI) bridges this gap by making NLG models more interpretable and controllable. 

This article explores practical techniques and tools for enhancing the transparency of NLG systems, offering detailed code snippets and step-by-step explanations to guide developers in understanding and improving model behavior. Topics include attention visualization, controllable generation, feature attribution, and integrating explainability into workflows. By focusing on real-world examples, this article serves as an educational guide for building more interpretable NLG systems.