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.
面向下一代网络的可生成解释性：基于大语言模型增强与特征交互的可解释人工智能 / Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
本文提出了一种结合大语言模型和特征交互信息的新框架，能够将网络AI模型的复杂决策自动转化为通俗易懂的自然语言解释，相比传统方法，解释的实用性和覆盖范围分别提升了12.2%和6.2%，准确率高达97.5%，有效帮助非专业用户理解并信任网络系统的运作。
源自 arXiv: 2606.10942
