Leveraging LLM for Enhanced Incident Management in Wireless Networks

Incident management in telecommunications networks generates large volumes of incident management tickets (IMTs), each containing heterogeneous and often unstructured text describing service outages, performance degradations, or security issues. Accurately categorizing these IMTs into multiple impact and cause labels is essential for rapid diagnosis and resolution. However, existing rule-based and standard language-model-based approaches struggle with noisy data, overlapping categories, and limited contextual understanding. To address these challenges, we propose two complementary solutions for automated multi-label classification of IMTs. To mitigate the effects of noisy data and overlapping categories, the first solution employs an encoder-based language model (i.e., Bidirectional Encoder Representations from Transformers (BERT)) with a relevance-guided feature selection strategy that focuses on semantically meaningful attributes. To improve contextual understanding and label consistency, the second solution leverages a decoder-based large language model (i.e., Phi-3.5) enhanced with retrieval-augmented generation (RAG) and a novel probabilistic re-ranking mechanism to refine label predictions. Experimental results show that our encoder-only model achieves an F1 score of 79.20%, while our RAG-enhanced decoder model achieves 94.98%, outperforming traditional machine learning models and BERT baselines by 23.59% and 29% on average, respectively. These findings demonstrate that combining fine-tuned language models with intelligent retrieval and re-ranking significantly improves classification accuracy in incident management systems.