Zobrazeno 1 - 10
of 302
pro vyhledávání: '"Zhang, Ruizhe"'
Autor:
Zhang, Ruizhe
Power device robustness refers to the device capability of withstanding abnormal events in power electronics applications, which is one of the key device capabilities that are desired in numerous applications. While the current robustness test method
Externí odkaz:
http://hdl.handle.net/10919/116216
Autor:
Jiang, Xinke, Qiu, Rihong, Xu, Yongxin, Zhang, Wentao, Zhu, Yichen, Zhang, Ruizhe, Fang, Yuchen, Chu, Xu, Zhao, Junfeng, Wang, Yasha
Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel
Externí odkaz:
http://arxiv.org/abs/2410.23855
Autor:
Xu, Yongxin, Zhang, Ruizhe, Jiang, Xinke, Feng, Yujie, Xiao, Yuzhen, Ma, Xinyu, Zhu, Runchuan, Chu, Xu, Zhao, Junfeng, Wang, Yasha
Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by Large Language Models (LLMs) in hallucination generation and knowledge obsolescence by incorporating externally retrieved knowledge. However, existing methods la
Externí odkaz:
http://arxiv.org/abs/2410.10360
We propose a polynomial-time algorithm for preparing the Gibbs state of the two-dimensional toric code Hamiltonian at any temperature, starting from any initial condition, significantly improving upon prior estimates that suggested exponential scalin
Externí odkaz:
http://arxiv.org/abs/2410.01206
Large language models (LLMs) have made significant progress in natural language processing tasks and demonstrate considerable potential in the legal domain. However, legal applications demand high standards of accuracy, reliability, and fairness. App
Externí odkaz:
http://arxiv.org/abs/2409.20288
Autor:
Jiang, Xinke, Fang, Yue, Qiu, Rihong, Zhang, Haoyu, Xu, Yongxin, Chen, Hao, Zhang, Wentao, Zhang, Ruizhe, Fang, Yuchen, Chu, Xu, Zhao, Junfeng, Wang, Yasha
In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized
Externí odkaz:
http://arxiv.org/abs/2408.09199
Autor:
Zhang, Ruizhe, Xu, Yongxin, Xiao, Yuzhen, Zhu, Runchuan, Jiang, Xinke, Chu, Xu, Zhao, Junfeng, Wang, Yasha
By integrating external knowledge, Retrieval-Augmented Generation (RAG) has become an effective strategy for mitigating the hallucination problems that large language models (LLMs) encounter when dealing with knowledge-intensive tasks. However, in th
Externí odkaz:
http://arxiv.org/abs/2408.03297
Subspace-based signal processing techniques, such as the Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT) algorithm, are popular methods for spectral estimation. These algorithms can achieve the so-called super-resolution
Externí odkaz:
http://arxiv.org/abs/2404.03885
The tasks of legal case retrieval have received growing attention from the IR community in the last decade. Relevance feedback techniques with implicit user feedback (e.g., clicks) have been demonstrated to be effective in traditional search tasks (e
Externí odkaz:
http://arxiv.org/abs/2403.13242
In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains. However, their universal competence in addressing challenges specific to special
Externí odkaz:
http://arxiv.org/abs/2403.11152