WorkDifferentWithAI.com Academic Paper Alert!
Written by Hongcheng Guo, Jian Yang, Jiaheng Liu, Liqun Yang, Linzheng Chai, Jiaqi Bai, Junran Peng, Xiaorong Hu, Chao Chen, Dongfeng Zhang, Xu Shi, Tieqiao Zheng, Liangfan Zheng, Bo Zhang, Ke Xu, Zhoujun Li
Category: AI for IT
Article Section: AI for IT
Publication Date: 2023-09-17
SEO Description: “Introducing OWL, a specialized Large Language Model for improving IT Operations.”
Claude.ai Generated Paper Summary
- Introduces Owl, a large language model specialized for IT ops tasks by training on a collected dataset Owl-Instruct covering 9 common IT domains. Shows how self-instruction can expand the training data.
- Constructs a new benchmark Owl-Bench with QA and multiple choice questions to evaluate model capabilities across different IT domains. Provides a testbed for comparing models on IT abilities.
- Proposes using a mixture-of-adapters during fine-tuning to improve performance across diverse tasks/domains. Shows this adapter approach is more efficient than standard tuning.
- Evaluates Owl on Owl-Bench and other IT benchmarks, demonstrating strong improvements over general LLMs and SoTA results on tasks like log parsing and anomaly detection.
- Discusses optimizations like incorporating IT terminology into the tokenizer and supporting long-context inference that improve results on IT data.
In summary, the key innovations are creating a specialized LLM and benchmark for IT ops, showing the value of domain-specific training data and adapters, and delivering excellent empirical results on IT tasks. The paper is focused on tailoring LLMs to a new niche domain through data, model architecture, and evaluation innovations.
Keywords
OWL, IT Operations, Large Language Model, parameter-efficient tuning, IT-related benchmarks
Author’s Abstract
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.