Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
arXiv:2606.10942v1 Announce Type: cross 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 u
The increasing integration of AI/ML into critical network operations necessitates greater transparency and explainability, which current XAI techniques often fail to deliver effectively for non-specialists.
Improving AI explainability in networks builds trust, enables more effective human oversight, and accelerates the adoption of AI/ML in infrastructure critical to future technological advancements.
The proposed framework leverages LLMs to make AI explanations more accessible and actionable for network operators, bridging the gap between technical AI outputs and practical insights.
- · Network operators
- · AI/ML developers
- · Cybersecurity sector
- · LLM providers
- · Opaque AI systems
- · Traditional XAI vendors
Increased trust and faster deployment of AI/ML models within network infrastructure.
Improved network resilience and efficiency due to better human-AI collaboration in operational decision-making.
The development of more sophisticated, human-centric AI systems across various critical infrastructure domains, moving beyond just networks.
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Read at arXiv cs.LG