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Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
Rezaei,Kiarash Ayoub,Omran Troia,Sebastian Lelli,Francesco Monti,Paolo Natalino,Carlos
Rezaei,Kiarash
Ayoub,Omran
Troia,Sebastian
Lelli,Francesco
Monti,Paolo
Natalino,Carlos
Abstract
As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant barrier to operator trust. Existing explainable artificial intelligence (XAI) techniques often fail to bridge this gap for non-specialists, producing technical outputs that are difficult to translate into actionable insights. This paper presents a framework specifically designed to address this shortcoming. It leverages a moderately sized large language model (LLM) and extends beyond the standard use of SHapley Additive exPlanations (SHAP) feature influence values. The framework employs a structured prompt enriched with mutual feature interaction data to generate human-understandable natural language explanations. To validate our framework, we performed an empirical evaluation on an optical quality of transmission (QoT) estimation use case with human evaluators. We collected independent performance evaluations from specialists, which showed a high inter-evaluator agreement. Compared to a state-of-the-art baseline that uses only SHAP feature influence values in a straightforward prompt, our approach improves the explanation usefulness and scope by 12.2% and 6.2%, while achieving 97.5% correctness
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Date
2025-10-20
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PaperLLMinNetworks.pdf
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Keywords
large language model, LLM, Explainable AI, XAI, Interoperability, transparancy
Citation
Rezaei, K, Ayoub, O, Troia, S, Lelli, F, Monti, P & Natalino, C 2025, Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions. in 21st International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).