paper
Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
paperactiveprovisional
generative-explainability-for-next-generation-networks-llm-augmented-xai-with-mutual-feature-interactions-f30834dc·1 events·first seen 7d agoAliases: Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
Co-occurring entities
More like this (12)
Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature InteractionsExplainable AI (XAI)Listening with Attention: Entropy-Guided Explainability for Transformer-Based Audio ModelsGenerative Adversarial NetworksTying the Loop -- Tied Expert Layers in Mixture-of-Experts Language Modelsgenerative AIxAIBackdoor Unlearning Generalization: A Path Toward the Removal of Unknown Triggers in LLMsMulti-Faceted Interactivity Alignment in Full-Duplex Speech Modelsgenerative language modelingMultimodal Augmented Generation via Multimodal Retrieval WorkshopAgentic AI Systems
Recent events (1)
LLM-augmented XAI framework with mutual feature interactions for network operations
A new arXiv paper proposes a framework combining LLMs with SHAP-based explainability, augmented by mutual feature interaction data, to generate natural language explanations for AI/ML models used in network operations. The approach is validated on an optical quality-of-transmission estimation task with human evaluators, showing 12.2% and 6.2% improvements in explanation usefulness and scope over a SHAP-only baseline, with 97.5% correctness. The work targets the gap between technical XAI outputs and actionable insights for non-specialist network operators.