Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Zhang, Qishen"'
Autor:
Wang, Mengru, Zhang, Ningyu, Xu, Ziwen, Xi, Zekun, Deng, Shumin, Yao, Yunzhi, Zhang, Qishen, Yang, Linyi, Wang, Jindong, Chen, Huajun
This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs). We construct a benchmark, SafeEdit, which covers nine unsafe categories with various powerful attack prompts and equips comprehensive metrics for sys
Externí odkaz:
http://arxiv.org/abs/2403.14472
Autor:
Li, Mingzhe, Chen, Xiuying, Xiang, Jing, Zhang, Qishen, Ma, Changsheng, Dai, Chenchen, Chang, Jinxiong, Liu, Zhongyi, Zhang, Guannan
Text matching systems have become a fundamental service in most searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for
Externí odkaz:
http://arxiv.org/abs/2402.07788
Autor:
Sun, Xiaojie, Bi, Keping, Guo, Jiafeng, Yang, Sihui, Zhang, Qishen, Liu, Zhongyi, Zhang, Guannan, Cheng, Xueqi
Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as category and brand. Recent work has proposed two approaches to incorp
Externí odkaz:
http://arxiv.org/abs/2312.02538
Autor:
Wang, Yue, Wang, Xinrui, Li, Juntao, Chang, Jinxiong, Zhang, Qishen, Liu, Zhongyi, Zhang, Guannan, Zhang, Min
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks. The success of instruction tuning depends on the availability of high-quality instruction data. Owing to th
Externí odkaz:
http://arxiv.org/abs/2308.12711
Autor:
Sun, Xiaojie, Bi, Keping, Guo, Jiafeng, Ma, Xinyu, Yixing, Fan, Shan, Hongyu, Zhang, Qishen, Liu, Zhongyi
Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product sear
Externí odkaz:
http://arxiv.org/abs/2308.11474
Autor:
Chen, Xiuying, Li, Mingzhe, Gao, Shen, Cheng, Xin, Yang, Qiang, Zhang, Qishen, Gao, Xin, Zhang, Xiangliang
Automatic summarization plays an important role in the exponential document growth on the Web. On content websites such as CNN.com and WikiHow.com, there often exist various kinds of side information along with the main document for attention attract
Externí odkaz:
http://arxiv.org/abs/2305.11503
Autor:
Li, Mingzhe, Lin, XieXiong, Chen, Xiuying, Chang, Jinxiong, Zhang, Qishen, Wang, Feng, Wang, Taifeng, Liu, Zhongyi, Chu, Wei, Zhao, Dongyan, Yan, Rui
Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references. Existing works mostly focus on contrastive learning on the instance-lev
Externí odkaz:
http://arxiv.org/abs/2205.13346
Akademický článek
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Autor:
Zhang, Qisheng
In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a
Externí odkaz:
https://hdl.handle.net/10919/117329
Publikováno v:
2013 IEEE Sixth International Conference on Biometrics: Theory, Applications & Systems (BTAS); 2013, p1-6, 6p
