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以支撑实际应用变得日益关键。自然语言处理技术已在多项任务中展现出卓越能力，包括命名实体识别、机器翻译和对话系统。近年来，大型语言模型在各种NLP下游任务中取得了显著提升。然而，目前仍缺乏面向IT运维领域的专用大型语言模型。本文介绍了OWL模型，这是一个基于我们收集的OWL-Instruct数据集训练的大型语言模型，该数据集涵盖广泛的IT相关信息。我们提出了混合适配器策略，以提升跨领域或跨任务的参数高效调优能力。此外，我们在自主构建的OWL-Bench及公开的IT相关基准测试上评估了OWL的性能。实验表明，OWL在IT任务上展现出优越的性能，显著超越了现有模型。我们期望本研究能为利用专用大型语言模型革新IT运维技术提供更多启示。