Zobrazeno 1 - 10
of 1 197
pro vyhledávání: '"YANG Hailong"'
Autor:
Qi, Jiaxing, Zeng, Chang, Luan, Zhongzhi, Huang, Shaohan, Yang, Shu, Lu, Yao, Han, Bin, Yang, Hailong, Qian, Depei
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. Existing methods commonly extract log sequence features using classica
Externí odkaz:
http://arxiv.org/abs/2412.13529
Extracting implicit knowledge and logical reasoning abilities from large language models (LLMs) has consistently been a significant challenge. The advancement of multi-agent systems has further en-hanced the capabilities of LLMs. Inspired by the stru
Externí odkaz:
http://arxiv.org/abs/2411.13932
Generative Models (GMs), particularly Large Language Models (LLMs), have garnered significant attention in machine learning and artificial intelligence for their ability to generate new data by learning the statistical properties of training data and
Externí odkaz:
http://arxiv.org/abs/2411.13867
Autor:
Liu, Tongxuan, Xu, Wenjiang, Huang, Weizhe, Wang, Xingyu, Wang, Jiaxing, Yang, Hailong, Li, Jing
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Although some prompting methods, such as Chain-of-Thought, can improve the re
Externí odkaz:
http://arxiv.org/abs/2409.17539
Autor:
Liu, Tongxuan, Wang, Xingyu, Huang, Weizhe, Xu, Wenjiang, Zeng, Yuting, Jiang, Lei, Yang, Hailong, Li, Jing
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of-Thought with Self-Cons
Externí odkaz:
http://arxiv.org/abs/2409.14051
Large language models (LLMs) have emerged as important components across various fields, yet their training requires substantial computation resources and abundant labeled data. It poses a challenge to robustly training LLMs for individual users (cli
Externí odkaz:
http://arxiv.org/abs/2406.07925
Autor:
Wang, Yiqing, Liu, Xiaoyan, Yang, Hailong, Yang, Xinyu, Wang, Pengbo, Liu, Yi, Luan, Zhongzhi, Qian, Depei
As modern HPC computing platforms become increasingly heterogeneous, it is challenging for programmers to fully leverage the computation power of massive parallelism offered by such heterogeneity. Consequently, task-based runtime systems have been pr
Externí odkaz:
http://arxiv.org/abs/2404.03226
Autor:
Wang, Siqi, Yang, Hailong, Wang, Xuezhu, Liu, Tongxuan, Wang, Pengbo, Liang, Xuning, Ma, Kejie, Feng, Tianyu, You, Xin, Bao, Yongjun, Liu, Yi, Luan, Zhongzhi, Qian, Depei
Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is challenging due to its autoregressive decoding that generates tokens only
Externí odkaz:
http://arxiv.org/abs/2402.15678
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-based anomaly detection. These methods face several challenges such as
Externí odkaz:
http://arxiv.org/abs/2309.01189
Modern systems produce a large volume of logs to record run-time status and events. System operators use these raw logs to track a system in order to obtain some useful information to diagnose system anomalies. One of the most important problems in t
Externí odkaz:
http://arxiv.org/abs/2303.11715