The telecommunications industry's rapid evolution demands intelligent systems capable of managing complex networks and adapting to emerging technologies. While large language models (LLMs) show promise in addressing these challenges, their deployment in telecom environments faces significant constraints due to edge device limitations and inconsistent documentation. To bridge this gap, we present TeleOracle, a telecom-specialized retrieval-augmented generation (RAG) system built on the Phi-2 small language model (SLM). To improve context retrieval, TeleOracle employs a two-stage retriever that incorporates semantic chunking and hybrid keyword and semantic search. Additionally, we expand the context window during inference to enhance the model's performance on open-ended queries. We also employ low-rank adaption for efficient fine-tuning. A thorough analysis of the model's performance indicates that our RAG framework is effective in aligning Phi-2 to the telecom domain in a downstream question and answer (QnA) task, achieving a 30% improvement in accuracy over the base Phi-2 model, reaching an overall accuracy of 81.20%. Notably, we show that our model not only performs on par with the much larger LLMs but also achieves a higher faithfulness score, indicating higher adherence to the retrieved context.

翻译：电信行业的快速发展要求智能系统能够管理复杂网络并适应新兴技术。尽管大型语言模型（LLMs）在应对这些挑战方面展现出潜力，但由于边缘设备限制和文档规范不统一，其在电信环境中的部署面临显著制约。为弥合这一差距，我们提出了TeleOracle——一个基于Phi-2小型语言模型（SLM）构建的电信专用检索增强生成（RAG）系统。为提升上下文检索能力，TeleOracle采用包含语义分块及混合关键词与语义搜索的双阶段检索器。此外，我们在推理阶段扩展了上下文窗口以增强模型对开放式查询的处理性能，并采用低秩自适应方法进行高效微调。对模型性能的深入分析表明，我们的RAG框架能有效使Phi-2适配电信领域的下游问答（QnA）任务，相比基础Phi-2模型准确率提升30%，总体准确率达到81.20%。值得注意的是，我们的模型不仅性能与规模大得多的LLMs相当，还获得了更高的忠实度评分，表明其对检索上下文的遵循程度更高。