Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions

The paper introduces a novel explainable AI (XAI) framework that integrates a moderately sized large language model (LLM) with mutual feature interactions to enhance the interpretability of AI/ML models in network operations. This framework extends traditional SHAP feature influence values and demonstrates a 12.2% improvement in explanation usefulness and a 6.2% increase in scope over a baseline approach, achieving 97.5% correctness in an optical quality of transmission (QoT) estimation task. This advancement is significant for practitioners as it provides more actionable insights for non-specialists, fostering greater trust in AI systems deployed in critical network environments.