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pro vyhledávání: '"Liu, TianYu"'
Since the discovery of the relation between the Chern number and quantum Hall effect, searching for observables of topological invariants has been an intriguing topic. Topological Hopf-link semimetals have attracted tremendous interest, in which the
Externí odkaz:
http://arxiv.org/abs/2412.07122
Autor:
Li, Lei, Wei, Yuancheng, Xie, Zhihui, Yang, Xuqing, Song, Yifan, Wang, Peiyi, An, Chenxin, Liu, Tianyu, Li, Sujian, Lin, Bill Yuchen, Kong, Lingpeng, Liu, Qi
Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored. Current assessment methods primarily rely on AI-annotated preference labels fr
Externí odkaz:
http://arxiv.org/abs/2411.17451
Recent work finds that retrieval-augmented generation with large language models is prone to be influenced by the order of retrieved documents in the context. However, the lack of in-depth analysis limits the use of this phenomenon for prompt enginee
Externí odkaz:
http://arxiv.org/abs/2411.07773
Autor:
Zhang, Ruisi, Liu, Tianyu, Feng, Will, Gu, Andrew, Purandare, Sanket, Liang, Wanchao, Massa, Francisco
Distributed training of large models consumes enormous computation resources and requires substantial engineering efforts to compose various training techniques. This paper presents SimpleFSDP, a PyTorch-native compiler-based Fully Sharded Data Paral
Externí odkaz:
http://arxiv.org/abs/2411.00284
Elastic strain can displace the massless Dirac fermions in monolayer graphene in a space-dependent fashion, similar to the effect of an external magnetic field, thus giving rise to Landau quantization. We here show that the strain-induced Landau quan
Externí odkaz:
http://arxiv.org/abs/2410.21921
Autor:
Miao, Yibo, Gao, Bofei, Quan, Shanghaoran, Lin, Junyang, Zan, Daoguang, Liu, Jiaheng, Yang, Jian, Liu, Tianyu, Deng, Zhijie
The last year has witnessed the rapid progress of large language models (LLMs) across diverse domains. Among them, CodeLLMs have garnered particular attention because they can not only assist in completing various programming tasks but also represent
Externí odkaz:
http://arxiv.org/abs/2410.18585
Early detection through imaging and accurate diagnosis is crucial in mitigating the high mortality rate associated with breast cancer. However, locating tumors from low-resolution and high-noise medical images is extremely challenging. Therefore, thi
Externí odkaz:
http://arxiv.org/abs/2410.17812
One strength of modern language models is their ability to incorporate information from a user-input context when answering queries. However, they are not equally sensitive to the subtle changes to that context. To quantify this, Du et al. (2024) giv
Externí odkaz:
http://arxiv.org/abs/2410.14361
Autor:
Gao, Bofei, Song, Feifan, Yang, Zhe, Cai, Zefan, Miao, Yibo, Dong, Qingxiu, Li, Lei, Ma, Chenghao, Chen, Liang, Xu, Runxin, Tang, Zhengyang, Wang, Benyou, Zan, Daoguang, Quan, Shanghaoran, Zhang, Ge, Sha, Lei, Zhang, Yichang, Ren, Xuancheng, Liu, Tianyu, Chang, Baobao
Recent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g., OpenAI o1 achieves 94.8%
Externí odkaz:
http://arxiv.org/abs/2410.07985
Autor:
Liang, Wanchao, Liu, Tianyu, Wright, Less, Constable, Will, Gu, Andrew, Huang, Chien-Chin, Zhang, Iris, Feng, Wei, Huang, Howard, Wang, Junjie, Purandare, Sanket, Nadathur, Gokul, Idreos, Stratos
The development of large language models (LLMs) has been instrumental in advancing state-of-the-art natural language processing applications. Training LLMs with billions of parameters and trillions of tokens require sophisticated distributed systems
Externí odkaz:
http://arxiv.org/abs/2410.06511