5 citations · 9 across the 9 of their papers we have counts for
9 papers
Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning
Jiaxing Qi, Zhongzhi Luan, Hongyu Zhang +5
Large language models (LLMs) are increasingly used to interpret operational evidence and assist incident response in cloud-native microservice systems. However, recovery-oriented u…
RATrain: A Resource-Aware Training Runtime for Large Language Models on Bandwidth-Constrained Heterogeneous Supercomputing Platforms
Yao Lu, Shiqing Ma, Zhongzhi Luan +5
Production heterogeneous supercomputing platforms are increasingly used to host large language model (LLM) training workloads. However, existing GPU-oriented training runtimes typi…
Transformer-based Neural Operators for 3D Wind Field Prediction over Complex Mountainous Terrain
Yujia Zhang, Jiaxi Qi, Ruiyan Chen +5
Accurate prediction of three-dimensional (3D) wind fields over complex mountainous terrain is essential for renewable energy deployment and regional weather modeling. Traditional c…
Bandwidth-Aware LLM Inference on Heterogeneous Many-Core Supercomputers
Yao Lu, Zhongzhi Luan, Gen Li +6
Large language model (LLM) inference is limited by high computational cost and memory bandwidth demands, making deployment on heterogeneous many-core processors challenging. Taking…
Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection
Jiaxing Qi, Chang Zeng, Zhongzhi Luan +5
Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often s…
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities
Jiaxing Qi, Chang Zeng, Zhongzhi Luan +6
Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly.…
FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning
Yao Lu, Jiaxing QI, Zhongzhi Luan +4
Large language models (LLMs) have emerged as important components across various fields, yet their training requires substantial computation resources and abundant labeled data. It…
Exploring the Comprehension of ChatGPT in Traditional Chinese Medicine Knowledge
Li Yizhen, Huang Shaohan, Qi Jiaxing +3
No previous work has studied the performance of Large Language Models (LLMs) in the context of Traditional Chinese Medicine (TCM), an essential and distinct branch of medical knowl…
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
Jiaxing Qi, Shaohan Huang, Zhongzhi Luan +3
The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-…