Zobrazeno 1 - 10
of 4 757
pro vyhledávání: '"ZHAO, Zhe"'
Autor:
Shi, Wenhang, Chen, Yiren, Bian, Shuqing, Zhang, Xinyi, Zhao, Zhe, Hu, Pengfei, Lu, Wei, Du, Xiaoyong
Knowledge stored in large language models requires timely updates to reflect the dynamic nature of real-world information. To update the knowledge, most knowledge editing methods focus on the low layers, since recent probes into the knowledge recall
Externí odkaz:
http://arxiv.org/abs/2412.17872
Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method that has been widely adopted in various downstream applications of LLMs. Together with the Mixture-of-Expert (MoE) technique, fine-tuning approaches have shown remarkable improvem
Externí odkaz:
http://arxiv.org/abs/2412.16216
Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are ro
Externí odkaz:
http://arxiv.org/abs/2412.13229
Analytic continuation aims to reconstruct real-time spectral functions from imaginary-time Green's functions; however, this process is notoriously ill-posed and challenging to solve. We propose a novel neural network architecture, named the Feature L
Externí odkaz:
http://arxiv.org/abs/2411.17728
Autor:
Jia, Xiaojun, Huang, Yihao, Liu, Yang, Tan, Peng Yan, Yau, Weng Kuan, Mak, Mun-Thye, Sim, Xin Ming, Ng, Wee Siong, Ng, See Kiong, Liu, Hanqing, Zhou, Lifeng, Yan, Huanqian, Sun, Xiaobing, Liu, Wei, Wang, Long, Qian, Yiming, Liu, Yong, Yang, Junxiao, Zhang, Zhexin, Lei, Leqi, Chen, Renmiao, Lu, Yida, Cui, Shiyao, Wang, Zizhou, Li, Shaohua, Wang, Yan, Goh, Rick Siow Mong, Zhen, Liangli, Zhang, Yingjie, Zhao, Zhe
This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms
Externí odkaz:
http://arxiv.org/abs/2411.14502
Autor:
Liu, Fei, Yao, Yiming, Guo, Ping, Yang, Zhiyuan, Zhao, Zhe, Lin, Xi, Tong, Xialiang, Yuan, Mingxuan, Lu, Zhichao, Wang, Zhenkun, Zhang, Qingfu
Algorithm Design (AD) is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions.
Externí odkaz:
http://arxiv.org/abs/2410.14716
Autor:
Chen, Jiachi, Zhong, Qingyuan, Wang, Yanlin, Ning, Kaiwen, Liu, Yongkun, Xu, Zenan, Zhao, Zhe, Chen, Ting, Zheng, Zibin
The emergence of Large Language Models (LLMs) has significantly influenced various aspects of software development activities. Despite their benefits, LLMs also pose notable risks, including the potential to generate harmful content and being abused
Externí odkaz:
http://arxiv.org/abs/2409.15154
With the expansion of business scenarios, real recommender systems are facing challenges in dealing with the constantly emerging new tasks in multi-task learning frameworks. In this paper, we attempt to improve the generalization ability of multi-tas
Externí odkaz:
http://arxiv.org/abs/2408.17214
Autor:
Khani, Nikhil, Yang, Shuo, Nath, Aniruddh, Liu, Yang, Abbo, Pendo, Wei, Li, Andrews, Shawn, Kula, Maciej, Kahn, Jarrod, Zhao, Zhe, Hong, Lichan, Chi, Ed
Knowledge Distillation (KD) is a powerful approach for compressing a large model into a smaller, more efficient model, particularly beneficial for latency-sensitive applications like recommender systems. However, current KD research predominantly foc
Externí odkaz:
http://arxiv.org/abs/2408.14678
We present the Learned Ranking Function (LRF), a system that takes short-term user-item behavior predictions as input and outputs a slate of recommendations that directly optimizes for long-term user satisfaction. Most previous work is based on optim
Externí odkaz:
http://arxiv.org/abs/2408.06512