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.

翻译：随着人工智能与机器学习模型成为网络运行的核心组成部分，其缺乏透明度的问题成为运营商信任度的重大障碍。现有可解释人工智能技术往往难以弥合非专业人员的认知鸿沟，生成的技术输出难以转化为可操作的见解。本文提出一个专门解决此缺陷的框架。该框架利用中等规模的大语言模型，并超越标准SHAP特征影响值的应用范围。通过采用融合特征交互数据的结构化提示，该框架能够生成人类可理解的自然语言解释。为验证该框架，我们在光传输质量估计用例中进行了实证评估，由人类评估者参与。我们收集了来自专家的独立性能评估结果，显示出较高的评估者间一致性。与仅使用SHAP特征影响值的基础方法相比，我们的方法在解释有用性和覆盖范围上分别提升12.2%和6.2%，同时达到97.5%的正确率。