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pro vyhledávání: '"Li Yu-Feng"'
Neutrinos are neutral in the Standard Model, but they have tiny charge radii generated by radiative corrections. In theories Beyond the Standard Model, neutrinos can also have magnetic and electric moments and small electric charges (millicharges). W
Externí odkaz:
http://arxiv.org/abs/2411.03122
The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowledge still restricts their application in human's daily life. In this pa
Externí odkaz:
http://arxiv.org/abs/2410.13360
Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the
Externí odkaz:
http://arxiv.org/abs/2409.19696
Vision-language models (VLMs) like CLIP have demonstrated impressive zero-shot ability in image classification tasks by aligning text and images but suffer inferior performance compared with task-specific expert models. On the contrary, expert models
Externí odkaz:
http://arxiv.org/abs/2408.11449
Pre-trained vision-language models like CLIP have shown powerful zero-shot inference ability via image-text matching and prove to be strong few-shot learners in various downstream tasks. However, in real-world scenarios, adapting CLIP to downstream t
Externí odkaz:
http://arxiv.org/abs/2406.12638
In offline Imitation Learning (IL), one of the main challenges is the \textit{covariate shift} between the expert observations and the actual distribution encountered by the agent, because it is difficult to determine what action an agent should take
Externí odkaz:
http://arxiv.org/abs/2406.12550
Sterile neutrinos that couple to the Standard Model via the neutrino magnetic dipole portals have been extensively studied at various experiments. In this work, we scrutinize these interactions for sterile neutrinos in the mass range of $\unit[0.1]{}
Externí odkaz:
http://arxiv.org/abs/2406.07477
Large language models (LLMs) have demonstrated impressive performance on reasoning tasks, which can be further improved through few-shot prompting techniques. However, the current evaluation primarily focuses on carefully constructed benchmarks and n
Externí odkaz:
http://arxiv.org/abs/2406.05055
Autor:
Zhou, Zhi, Shi, Jiang-Xin, Song, Peng-Xiao, Yang, Xiao-Wen, Jin, Yi-Xuan, Guo, Lan-Zhe, Li, Yu-Feng
Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks. Nonetheless, when it comes to practical Chinese legal tasks, these models fail to
Externí odkaz:
http://arxiv.org/abs/2406.04614
Vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot capabilities for various downstream tasks. Their performance can be further enhanced through few-shot prompt tuning methods. However, current studies evaluate the per
Externí odkaz:
http://arxiv.org/abs/2406.00345