Zobrazeno 1 - 10
of 911
pro vyhledávání: '"Liu,Yilun"'
Autor:
Ji, Yuhe, Liu, Yilun, Yao, Feiyu, He, Minggui, Tao, Shimin, Zhao, Xiaofeng, Chang, Su, Yang, Xinhua, Meng, Weibin, Xie, Yuming, Chen, Boxing, Yang, Hao
The increasing complexity of computer systems necessitates innovative approaches to fault and error management, going beyond traditional manual log analysis. While existing solutions using large language models (LLMs) show promise, they are limited b
Externí odkaz:
http://arxiv.org/abs/2412.01377
The Mixture-of-Experts (MoE) paradigm has emerged as a powerful approach for scaling transformers with improved resource utilization. However, efficiently fine-tuning MoE models remains largely underexplored. Inspired by recent works on Parameter-Eff
Externí odkaz:
http://arxiv.org/abs/2411.08212
Autor:
Liu, Yilun, Ji, Yuhe, Tao, Shimin, He, Minggui, Meng, Weibin, Zhang, Shenglin, Sun, Yongqian, Xie, Yuming, Chen, Boxing, Yang, Hao
Automatic log analysis is essential for the efficient Operation and Maintenance (O&M) of software systems, providing critical insights into system behaviors. However, existing approaches mostly treat log analysis as training a model to perform an iso
Externí odkaz:
http://arxiv.org/abs/2410.09352
What Do You Want? User-centric Prompt Generation for Text-to-image Synthesis via Multi-turn Guidance
Autor:
Liu, Yilun, He, Minggui, Yao, Feiyu, Ji, Yuhe, Tao, Shimin, Du, Jingzhou, Li, Duan, Gao, Jian, Zhang, Li, Yang, Hao, Chen, Boxing, Yoshie, Osamu
The emergence of text-to-image synthesis (TIS) models has significantly influenced digital image creation by producing high-quality visuals from written descriptions. Yet these models heavily rely on the quality and specificity of textual prompts, po
Externí odkaz:
http://arxiv.org/abs/2408.12910
Autor:
Chen, Yan, Wang, Xueru, Deng, Xiaobin, Liu, Yilun, Chen, Xi, Zhang, Yunwei, Wang, Lei, Xiao, Hang
Inverse design of solid-state materials with desired properties represents a formidable challenge in materials science. Although recent generative models have demonstrated potential, their adoption has been hindered by limitations such as inefficienc
Externí odkaz:
http://arxiv.org/abs/2408.07608
Autor:
Cui, Tianyu, Ma, Shiyu, Chen, Ziang, Xiao, Tong, Tao, Shimin, Liu, Yilun, Zhang, Shenglin, Lin, Duoming, Liu, Changchang, Cai, Yuzhe, Meng, Weibin, Sun, Yongqian, Pei, Dan
Log analysis is crucial for ensuring the orderly and stable operation of information systems, particularly in the field of Artificial Intelligence for IT Operations (AIOps). Large Language Models (LLMs) have demonstrated significant potential in natu
Externí odkaz:
http://arxiv.org/abs/2407.01896
Large-scale graphs are valuable for graph representation learning, yet the abundant data in these graphs hinders the efficiency of the training process. Graph condensation (GC) alleviates this issue by compressing the large graph into a significantly
Externí odkaz:
http://arxiv.org/abs/2405.14246
Legal case retrieval (LCR) is a specialised information retrieval task that aims to find relevant cases to a given query case. LCR holds pivotal significance in facilitating legal practitioners in finding precedents. Most of existing LCR methods are
Externí odkaz:
http://arxiv.org/abs/2405.11791
Autor:
Zhao, Haofei, Liu, Yilun, Tao, Shimin, Meng, Weibin, Chen, Yimeng, Geng, Xiang, Su, Chang, Zhang, Min, Yang, Hao
Publikováno v:
2024 International Joint Conference on Neural Networks (IJCNN)
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT. After two decades of evol
Externí odkaz:
http://arxiv.org/abs/2403.14118
Autor:
Ge, Yuan, Liu, Yilun, Hu, Chi, Meng, Weibin, Tao, Shimin, Zhao, Xiaofeng, Ma, Hongxia, Zhang, Li, Chen, Boxing, Yang, Hao, Li, Bei, Xiao, Tong, Zhu, Jingbo
With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required for training and evaluating models, it is advantageous to have an efficient method for se
Externí odkaz:
http://arxiv.org/abs/2402.18191