Zobrazeno 1 - 10
of 7 161
pro vyhledávání: '"Ma, jin"'
Recent studies have highlighted the significant potential of Large Language Models (LLMs) as zero-shot relevance rankers. These methods predominantly utilize prompt learning to assess the relevance between queries and documents by generating a ranked
Externí odkaz:
http://arxiv.org/abs/2411.04539
Recently, Linear Complementary Dual (LCD) codes have garnered substantial interest within coding theory research due to their diverse applications and favorable attributes. This paper directs its attention to the construction of binary and ternary LC
Externí odkaz:
http://arxiv.org/abs/2409.08114
International trading networks significantly influence global economic conditions and environmental outcomes. A notable imbalance between economic gains and emissions transfers persists, manifesting as carbon inequality. This study introduces a novel
Externí odkaz:
http://arxiv.org/abs/2406.16092
In this paper we investigate the issues regarding the convergence of the Policy Iteration Algorithm(PIA) for a class of general continuous-time entropy-regularized stochastic control problems. In particular, instead of employing sophisticated PDE est
Externí odkaz:
http://arxiv.org/abs/2406.10959
The Environmental Extended Multi-Regional Input-Output analysis is the predominant framework in Ecological Economics for assessing the environmental impact of economic activities. This paper introduces ExioML, the first Machine Learning benchmark dat
Externí odkaz:
http://arxiv.org/abs/2406.09046
Autor:
Almuzaini, Atiqah, Ma, Jin
In this paper, we mainly focus on the set-valued (stochastic) analysis on the space of convex, closed, but possibly unbounded sets, and try to establish a useful theoretical framework for studying the set-valued stochastic differential equations with
Externí odkaz:
http://arxiv.org/abs/2403.15662
Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a
Externí odkaz:
http://arxiv.org/abs/2403.06474
Autor:
Deng, Haolin, Wang, Chang, Li, Xin, Yuan, Dezhang, Zhan, Junlang, Zhou, Tianhua, Ma, Jin, Gao, Jun, Xu, Ruifeng
Enhancing the attribution in large language models (LLMs) is a crucial task. One feasible approach is to enable LLMs to cite external sources that support their generations. However, existing datasets and evaluation methods in this domain still exhib
Externí odkaz:
http://arxiv.org/abs/2403.01774
In this paper, we reanalyze the top-quark pair production at the next-to-next-to-leading order (NNLO) in QCD at future $e^+e^-$ colliders by using the Principle of Maximum Conformality (PMC) method. The PMC renormalization scales in $\alpha_s$ are de
Externí odkaz:
http://arxiv.org/abs/2402.02363
Autor:
Qiao, Xiangshuo, Li, Xianxin, Qu, Xiaozhe, Zhang, Jie, Liu, Yang, Luo, Yu, Jin, Cihang, Ma, Jin
Vision-Language Models pre-trained on large-scale image-text datasets have shown superior performance in downstream tasks such as image retrieval. Most of the images for pre-training are presented in the form of open domain common-sense visual elemen
Externí odkaz:
http://arxiv.org/abs/2401.10475