With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques of Natural Language Processing (NLP) have shown remarkable capabilities for various tasks, including named entity recognition, machine translation and dialogue systems. Recently, Large Language Models (LLMs) have achieved significant improvements across various NLP downstream tasks. However, there is a lack of specialized LLMs for IT operations. In this paper, we introduce the OWL, a large language model trained on our collected OWL-Instruct dataset with a wide range of IT-related information, where the mixture-of-adapter strategy is proposed to improve the parameter-efficient tuning across different domains or tasks. Furthermore, we evaluate the performance of our OWL on the OWL-Bench established by us and open IT-related benchmarks. OWL demonstrates superior performance results on IT tasks, which outperforms existing models by significant margins. Moreover, we hope that the findings of our work will provide more insights to revolutionize the techniques of IT operations with specialized LLMs.

翻译：随着IT运维的快速发展，高效管理和分析海量数据在实际应用中变得愈发关键。自然语言处理（NLP）技术在命名实体识别、机器翻译及对话系统等多项任务中展现出卓越能力。近期，大型语言模型（LLMs）在各类NLP下游任务中取得了显著进步。然而，目前尚缺乏针对IT运维的专用大型语言模型。本文提出OWL，一种基于我们收集的涵盖广泛IT相关信息的OWL-Instruct数据集训练而成的大型语言模型。其中，我们设计了混合适配器策略，以提升跨不同领域或任务的参数高效微调效果。此外，我们在自主构建的OWL-Bench及公开IT相关基准上评估了OWL的性能。实验结果表明，OWL在IT任务中表现卓越，性能显著超越现有模型。我们期望本研究能为利用专用大型语言模型革新IT运维技术提供更多思路。