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.

翻译：电信领域对大型语言模型（LLM）日益增长的兴趣凸显了其在革新运营效率方面的潜力。然而，这些复杂模型的部署常因其庞大的规模与计算需求而受阻，引发了在资源受限环境中可行性的担忧。针对这一挑战，近期进展中涌现出的小型语言模型在许多任务（如代码生成与常识推理）中展现出可与大型模型相媲美的性能，令人惊讶。Phi-2 作为一个紧凑而强大的模型，代表了这一高效小型语言模型的新浪潮。本文对 Phi-2 在电信领域的内在理解能力进行了全面评估。认识到规模相关的局限性，我们通过检索增强生成方法提升了 Phi-2 的能力，精心整合了一个专门基于电信标准规范构建的广泛知识库。增强后的 Phi-2 模型在准确性上表现出显著提升，其回答电信标准相关问题的精确度已接近资源消耗更大的 GPT-3.5。本文进一步探讨了 Phi-2 在解决电信领域问题场景中的精炼能力，并指出了其潜力与局限。