面向5G无线网络根因分析的推理语言模型

摘要

移动网络中的根因分析（RCA）因其对可解释性、领域专业知识和因果推理的需求而依然是一项具有挑战性的任务。在本研究中，我们提出了一种轻量级框架，该框架利用大型语言模型（LLMs）进行RCA。为此，我们引入了TeleLogs，一个精心策划的带注释故障排除问题数据集，旨在评估RCA能力。我们的评估显示，现有的开源推理LLMs在处理这些问题时表现不佳，凸显了领域特定适应的必要性。针对这一问题，我们提出了一种两阶段训练方法，结合了监督微调与强化学习，以提高LLMs的准确性和推理质量。所提出的方法通过微调一系列RCA模型，整合领域知识并生成结构化的多步诊断解释，从而提升了可解释性和有效性。跨多个LLM规模的广泛实验表明，相较于最先进的推理和非推理模型，该方法实现了显著的性能提升，包括对随机测试变体的强大泛化能力。这些结果展示了领域适应、推理增强的LLMs在网络运营和管理中实现实用且可解释RCA的潜力。

English

Root Cause Analysis (RCA) in mobile networks remains a challenging task due to the need for interpretability, domain expertise, and causal reasoning. In this work, we propose a lightweight framework that leverages Large Language Models (LLMs) for RCA. To do so, we introduce TeleLogs, a curated dataset of annotated troubleshooting problems designed to benchmark RCA capabilities. Our evaluation reveals that existing open-source reasoning LLMs struggle with these problems, underscoring the need for domain-specific adaptation. To address this issue, we propose a two-stage training methodology that combines supervised fine-tuning with reinforcement learning to improve the accuracy and reasoning quality of LLMs. The proposed approach fine-tunes a series of RCA models to integrate domain knowledge and generate structured, multi-step diagnostic explanations, improving both interpretability and effectiveness. Extensive experiments across multiple LLM sizes show significant performance gains over state-of-the-art reasoning and non-reasoning models, including strong generalization to randomized test variants. These results demonstrate the promise of domain-adapted, reasoning-enhanced LLMs for practical and explainable RCA in network operation and management.

面向5G无线网络根因分析的推理语言模型

Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

摘要

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