Zobrazeno 1 - 10
of 276
pro vyhledávání: '"GAO Jiahui"'
Publikováno v:
康复学报, Pp 1-8 (2024)
ObjectiveAnalysis of Alpha Spectral Characteristics in the Parieto-Occipital lobe of Elderly Individuals with Chronic Insomnia and Mild Cognitive Impairment （MCI）.MethodsEighty elderly individuals with chronic insomnia and mild cognitive impairme
Externí odkaz:
https://doaj.org/article/bc05d49f391c43059ab51be2c1df091e
Multivariate time series (MTS) data is generated through multiple sensors across various domains such as engineering application, health monitoring, and the internet of things, characterized by its temporal changes and high dimensional characteristic
Externí odkaz:
http://arxiv.org/abs/2411.12222
Autor:
Li, Qintong, Gao, Jiahui, Wang, Sheng, Pi, Renjie, Zhao, Xueliang, Wu, Chuan, Jiang, Xin, Li, Zhenguo, Kong, Lingpeng
Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data, leading to impressive performance across a range of downstream applications. Current methods often rely on human-annotated data or pr
Externí odkaz:
http://arxiv.org/abs/2410.16736
Autor:
Ye, Jiacheng, Gao, Jiahui, Gong, Shansan, Zheng, Lin, Jiang, Xin, Li, Zhenguo, Kong, Lingpeng
Autoregressive language models, despite their impressive capabilities, struggle with complex reasoning and long-term planning tasks. We introduce discrete diffusion models as a novel solution to these challenges. Through the lens of subgoal imbalance
Externí odkaz:
http://arxiv.org/abs/2410.14157
Large vision-language models (LVLMs) have witnessed significant progress on visual understanding tasks. However, they often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation. To tackle t
Externí odkaz:
http://arxiv.org/abs/2410.14138
CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration
Autor:
Gao, Jiahui, Pi, Renjie, Han, Tianyang, Wu, Han, Hong, Lanqing, Kong, Lingpeng, Jiang, Xin, Li, Zhenguo
The deployment of multimodal large language models (MLLMs) has demonstrated remarkable success in engaging in conversations involving visual inputs, thanks to the superior power of large language models (LLMs). Those MLLMs are typically built based o
Externí odkaz:
http://arxiv.org/abs/2409.11365
The widespread adoption of large language models (LLMs) has raised concerns about their safety and reliability, particularly regarding their vulnerability to adversarial attacks. In this paper, we propose a novel perspective that attributes this vuln
Externí odkaz:
http://arxiv.org/abs/2406.14393
Autor:
Liu, Zhili, Gou, Yunhao, Chen, Kai, Hong, Lanqing, Gao, Jiahui, Mi, Fei, Zhang, Yu, Li, Zhenguo, Jiang, Xin, Liu, Qun, Kwok, James T.
As the capabilities of large language models (LLMs) have expanded dramatically, aligning these models with human values presents a significant challenge. Traditional alignment strategies rely heavily on human intervention, such as Supervised Fine-Tun
Externí odkaz:
http://arxiv.org/abs/2405.00557
Autor:
Yao, Yuxuan, Wu, Han, Guo, Zhijiang, Zhou, Biyan, Gao, Jiahui, Luo, Sichun, Hou, Hanxu, Fu, Xiaojin, Song, Linqi
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content. One potential approach to mitigate these issues is learnin
Externí odkaz:
http://arxiv.org/abs/2403.19094
Publikováno v:
Geofluids, Vol 2021 (2021)
Coal seam water injection is an important technical method to prevent and control coal and gas outburst and other disasters. Water can soften coal and change its mechanical properties. In order to study the mechanical properties of coal samples with
Externí odkaz:
https://doaj.org/article/57ad8876c11b4825b1b17915e163742f