The increasing interest in Large Language Models (LLMs) within the
telecommunications sector underscores their potential to revolutionize
operational efficiency. However, the deployment of these sophisticated models
is often hampered by their substantial size and computational demands, raising
concerns about their viability in resource-constrained environments. Addressing
this challenge, recent advancements have seen the emergence of small language
models that surprisingly exhibit performance comparable to their larger
counterparts in many tasks, such as coding and common-sense reasoning. Phi-2, a
compact yet powerful model, exemplifies this new wave of efficient small
language models. This paper conducts a comprehensive evaluation of Phi-2's
intrinsic understanding of the telecommunications domain. Recognizing the
scale-related limitations, we enhance Phi-2's capabilities through a
Retrieval-Augmented Generation approach, meticulously integrating an extensive
knowledge base specifically curated with telecom standard specifications. The
enhanced Phi-2 model demonstrates a profound improvement in accuracy, answering
questions about telecom standards with a precision that closely rivals the more
resource-intensive GPT-3.5. The paper further explores the refined capabilities
of Phi-2 in addressing problem-solving scenarios within the telecom sector,
highlighting its potential and limitations.

该论文评估了 Phi-2 模型在电信领域的的内在理解能力，并通过检索增强生成方法，将电信标准规范知识库与其整合，提高了其在准确度方面的性能。Phi-2 模型在回答与电信标准相关的问题上表现出与资源密集型 GPT-3.5 相当的准确度，同时探索了 Phi-2 在电信领域解决问题的能力和局限性。