Zobrazeno 1 - 10
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pro vyhledávání: '"A Zhang"'
Autor:
Zhang, Zhaoyang, Septembre, Ismaël, Liu, Zhenzhi, Kokhanchik, Pavel, Liang, Shun, Liu, Fu, Li, Changbiao, Wang, Hongxing, Liu, Maochang, Zhang, Yanpeng, Xiao, Min, Malpuech, Guillaume, Solnyshkov, Dmitry
Topological physics has broadened its scope from the study of topological insulating phases to include nodal phases containing band structure singularities. The geometry of the corresponding quantum states is described by the quantum metric which pro
Externí odkaz:
http://arxiv.org/abs/2410.14428
When tracking maneuvering targets, model-driven approaches encounter difficulties in comprehensively delineating complex real-world scenarios and are prone to model mismatch when the targets maneuver. Meanwhile, contemporary data-driven methods have
Externí odkaz:
http://arxiv.org/abs/2410.14422
This paper proposes an integrated framework for coordinating multiple unmanned aerial vehicles (UAVs) in a distributed fashion to persistently enclose and track a moving target without external localization systems. It is assumed that the UAV can obt
Externí odkaz:
http://arxiv.org/abs/2410.14407
Autor:
Yang, Ruihan, Zhang, Caiqi, Zhang, Zhisong, Huang, Xinting, Yang, Sen, Collier, Nigel, Yu, Dong, Yang, Deqing
While Large Language Models (LLMs) demonstrate impressive capabilities, they still struggle with generating factually incorrect content (i.e., hallucinations). A promising approach to mitigate this issue is enabling models to express uncertainty when
Externí odkaz:
http://arxiv.org/abs/2410.14309
Autor:
Zhang, Chen, Zhong, Meizhi, Wang, Qimeng, Lu, Xuantao, Ye, Zheyu, Lu, Chengqiang, Gao, Yan, Hu, Yao, Chen, Kehai, Zhang, Min, Song, Dawei
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both latency and memory. In this paper, however, we discover that MoD can bar
Externí odkaz:
http://arxiv.org/abs/2410.14268
Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefore, model owners and third parties need to identify whether a suspect m
Externí odkaz:
http://arxiv.org/abs/2410.14273
In this paper, we investigate the problem of jamming detection and channel estimation during multi-user uplink beam training under random pilot jamming attacks in beamspace massive multi-input-multi-output (MIMO) systems. For jamming detection, we di
Externí odkaz:
http://arxiv.org/abs/2410.14215
Autor:
Zhang, Mozhi, Wang, Pengyu, Tan, Chenkun, Huang, Mianqiu, Zhang, Dong, Zhou, Yaqian, Qiu, Xipeng
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications. To make LLMs more usable, aligning them with human preferences is essential. Existing
Externí odkaz:
http://arxiv.org/abs/2410.14184
Autor:
Zhang, Chenyang, Lin, Jiayi, Tong, Haibo, Hou, Bingxuan, Zhang, Dongyu, Li, Jialin, Wang, Junli
Large language models (LLMs) show remarkable abilities with instruction tuning. However, they fail to achieve ideal tasks when lacking high-quality instruction tuning data on target tasks. Multi-Aspect Controllable Text Generation (MCTG) is a represe
Externí odkaz:
http://arxiv.org/abs/2410.14144
Autor:
Yang, Yuzhe, Zhang, Yifei, Hu, Yan, Guo, Yilin, Gan, Ruoli, He, Yueru, Lei, Mingcong, Zhang, Xiao, Wang, Haining, Xie, Qianqian, Huang, Jimin, Yu, Honghai, Wang, Benyou
This paper introduces the UCFE: User-Centric Financial Expertise benchmark, an innovative framework designed to evaluate the ability of large language models (LLMs) to handle complex real-world financial tasks. UCFE benchmark adopts a hybrid approach
Externí odkaz:
http://arxiv.org/abs/2410.14059