Zobrazeno 1 - 10
of 150
pro vyhledávání: '"Li, Tianlin"'
Autor:
Liu, Aishan, Zhou, Yuguang, Liu, Xianglong, Zhang, Tianyuan, Liang, Siyuan, Wang, Jiakai, Pu, Yanjun, Li, Tianlin, Zhang, Junqi, Zhou, Wenbo, Guo, Qing, Tao, Dacheng
Large language models (LLMs) have transformed the development of embodied intelligence. By providing a few contextual demonstrations, developers can utilize the extensive internal knowledge of LLMs to effortlessly translate complex tasks described in
Externí odkaz:
http://arxiv.org/abs/2408.02882
Deep Neural networks (DNNs), extensively applied across diverse disciplines, are characterized by their integrated and monolithic architectures, setting them apart from conventional software systems. This architectural difference introduces particula
Externí odkaz:
http://arxiv.org/abs/2407.20281
Diffusion-based video generation has achieved significant progress, yet generating multiple actions that occur sequentially remains a formidable task. Directly generating a video with sequential actions can be extremely challenging due to the scarcit
Externí odkaz:
http://arxiv.org/abs/2405.18003
Autor:
Xia, Zeke, Hu, Ming, Yan, Dengke, Xie, Xiaofei, Li, Tianlin, Li, Anran, Zhou, Junlong, Chen, Mingsong
Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the pres
Externí odkaz:
http://arxiv.org/abs/2404.12850
Autor:
Li, Yanzhou, Li, Tianlin, Chen, Kangjie, Zhang, Jian, Liu, Shangqing, Wang, Wenhan, Zhang, Tianwei, Liu, Yang
Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs). To address these issues, for the fi
Externí odkaz:
http://arxiv.org/abs/2403.13355
MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented Generation
Autor:
Wang, Guanyu, Li, Yuekang, Liu, Yi, Deng, Gelei, Li, Tianlin, Xu, Guosheng, Liu, Yang, Wang, Haoyu, Wang, Kailong
Augmented generation techniques such as Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) have revolutionized the field by enhancing large language model (LLM) outputs with external knowledge and cached information. However, t
Externí odkaz:
http://arxiv.org/abs/2402.14480
The emerging success of large language models (LLMs) heavily relies on collecting abundant training data from external (untrusted) sources. Despite substantial efforts devoted to data cleaning and curation, well-constructed LLMs have been reported to
Externí odkaz:
http://arxiv.org/abs/2402.14845
Autor:
Li, Tianlin, Zhang, Xiaoyu, Du, Chao, Pang, Tianyu, Liu, Qian, Guo, Qing, Shen, Chao, Liu, Yang
The widespread adoption of large language models (LLMs) underscores the urgent need to ensure their fairness. However, LLMs frequently present dominant viewpoints while ignoring alternative perspectives from minority parties, resulting in potential b
Externí odkaz:
http://arxiv.org/abs/2402.12150
Autor:
Huang, Yihao, Yu, Kaiyuan, Guo, Qing, Juefei-Xu, Felix, Jia, Xiaojun, Li, Tianlin, Pu, Geguang, Liu, Yang
In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving. However, their robustness against common weather corruption such as fog, rain, snow, and sunlight in the intricate physical world rema
Externí odkaz:
http://arxiv.org/abs/2402.02738
Autor:
Zhou, Qi, Wang, Dongxia, Li, Tianlin, Xu, Zhihong, Liu, Yang, Ren, Kui, Wang, Wenhai, Guo, Qing
Guided image synthesis methods, like SDEdit based on the diffusion model, excel at creating realistic images from user inputs such as stroke paintings. However, existing efforts mainly focus on image quality, often overlooking a key point: the diffus
Externí odkaz:
http://arxiv.org/abs/2402.03705