OpsEval: A Comprehensive Benchmark Suite for Evaluating Large Language Models’ Capability in IT Operations Domain
In recent decades, the field of software engineering has driven the rapid evolution of Information Technology (IT) systems, including advances in cloud computing, 5G networks, and financial information platforms. Ensuring the stability, reliability, and robustness of these complex IT systems has emerged as a critical challenge. Large language models (LLMs) that have exhibited remarkable capabilities in NLP-related tasks are showing great potential in AIOps, such as root cause analysis of failures, generation of operations and maintenance scripts, and summarizing of alert information. Unlike knowledge in general corpora, knowledge of Ops varies with the different IT systems, encompassing various private sub-domain knowledge, sensitive to prompt engineering due to various sub-domains, and containing numerous terminologies. Existing NLP-related benchmarks can not guide the selection of suitable LLMs for Ops (OpsLLM), and current metrics (e.g., BLEU, ROUGE) can not adequately reflect the question-answering (QA) effectiveness in the Ops domain. We propose a comprehensive benchmark suite, OpsEval, including an Ops-oriented evaluation dataset, an Ops evaluation benchmark, and a specially designed Ops QA evaluation method. Our dataset contains 7,334 multiple-choice questions and 1,736 QA questions. We have carefully selected and released 20% of the dataset written by domain experts in various sub-domains to assist current researchers in preliminary evaluations of OpsLLMs. We test over 24 latest LLMs under various settings such as self-consistency, chain-of-thought, and in-context learning, revealing findings when applying LLMs to Ops. We also propose an evaluation method for QA in Ops, which has a coefficient of 0.9185 with human experts and is improved by 0.4471 and 1.366 compared to BLEU and ROUGE, respectively. Over the past one year, our dataset and leaderboard have been continuously updated.
Tue 24 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:30 - 12:30 | SE for LLMJournal First / Industry Papers / Demonstrations / Research Papers / Ideas, Visions and Reflections at Cosmos 3C Chair(s): Hongyu Zhang Chongqing University | ||
10:30 10mTalk | Enhancing Code LLM Training with Programmer Attention Ideas, Visions and Reflections Yifan Zhang Vanderbilt University, Chen Huang Sichuan University, Zachary Karas Vanderbilt University, Thuy Dung Nguyen Vanderbilt University, Kevin Leach Vanderbilt University, Yu Huang Vanderbilt University | ||
10:40 20mTalk | Risk Assessment Framework for Code LLMs via Leveraging Internal States Industry Papers Yuheng Huang The University of Tokyo, Lei Ma The University of Tokyo & University of Alberta, Keizaburo Nishikino Fujitsu Limited, Takumi Akazaki Fujitsu Limited | ||
11:00 20mTalk | An Empirical Study of Issues in Large Language Model Training Systems Industry Papers Yanjie Gao Microsoft Research, Ruiming Lu Shanghai Jiao Tong University, Haoxiang Lin Microsoft Research, Yueguo Chen Renmin University of China DOI | ||
11:20 20mTalk | Look Before You Leap: An Exploratory Study of Uncertainty Analysis for Large Language Models Journal First Yuheng Huang The University of Tokyo, Norman Song , Zhijie Wang University of Alberta, Shengming Zhao University of Alberta, Huaming Chen The University of Sydney, Felix Juefei-Xu New York University, Lei Ma The University of Tokyo & University of Alberta Link to publication DOI Pre-print | ||
11:40 10mTalk | EvidenceBot: A Privacy-Preserving, Customizable RAG-Based Tool for Enhancing Large Language Model Interactions Demonstrations Nafiz Imtiaz Khan Department of Computer Science, University of California, Davis, Vladimir Filkov University of California at Davis, USA | ||
11:50 20mTalk | OpsEval: A Comprehensive Benchmark Suite for Evaluating Large Language Models’ Capability in IT Operations Domain Industry Papers Yuhe Liu Tsinghua University, Changhua Pei Computer Network Information Center at Chinese Academy of Sciences, Longlong Xu Tsinghua University, Bohan Chen Tsinghua University, Mingze Sun Tsinghua University, Zhirui Zhang Beijing University of Posts and Telecommunications, Yongqian Sun Nankai University, Shenglin Zhang Nankai University, Kun Wang Zhejiang University, Haiming Zhang Chinese Academy of Sciences, Jianhui Li Computer Network Information Center at Chinese Academy of Sciences, Gaogang Xie Computer Network Information Center at Chinese Academy of Sciences, Xidao Wen BizSeer, Xiaohui Nie Computer Network Information Center at Chinese Academy of Sciences, Minghua Ma Microsoft, Dan Pei Tsinghua University | ||
12:10 20mTalk | Hallucination Detection in Large Language Models with Metamorphic Relations Research Papers Borui Yang Beijing University of Posts ad Telecommunications, Md Afif Al Mamun University of Calgary, Jie M. Zhang King's College London, Gias Uddin York University, Canada DOI | ||
Cosmos 3C is the third room in the Cosmos 3 wing.
When facing the main Cosmos Hall, access to the Cosmos 3 wing is on the left, close to the stairs. The area is accessed through a large door with the number “3”, which will stay open during the event.