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OWL: A Large Language Model for IT Operations

arXiv 2309.09298 ↗First posted 17 Sep 2023cs.CLcited by 43

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Abstract (arXiv)

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.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). Builds on 39 of its 74 references that are in the map. First posted on arXiv 17 Sep 2023.

Builds on

39 of 74 references.

References · 39

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Citing papers per year: 2023: 1; 2024: 27; 2025: 9; 2026: 6.

43 papers.

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  11. 2024C-ICL: Contrastive In-context Learning for Information Extractioncited by 23
  12. 2025A Survey of AIOps in the Era of Large Language Modelscited by 19
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  11. 2023M2C: Towards Automatic Multimodal Manga Complementcited by 7
  12. 2024RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenancecited by 7
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