Zobrazeno 1 - 10
of 6 270
pro vyhledávání: '"Li Zhuang"'
Autor:
Qu, Shilin, Wang, Weiqing, Zhou, Xin, Zhan, Haolan, Li, Zhuang, Qu, Lizhen, Luo, Linhao, Li, Yuan-Fang, Haffari, Gholamreza
Publikováno v:
TOMM 2024
Sociocultural norms serve as guiding principles for personal conduct in social interactions, emphasizing respect, cooperation, and appropriate behavior, which is able to benefit tasks including conversational information retrieval, contextual informa
Externí odkaz:
http://arxiv.org/abs/2410.03049
Recent advances in artificial intelligence have seen Large Language Models (LLMs) demonstrate notable proficiency in causal discovery tasks. This study explores the factors influencing the performance of LLMs in causal discovery tasks. Utilizing open
Externí odkaz:
http://arxiv.org/abs/2407.19638
The effectiveness of Large Language Models (LLMs) in legal reasoning is often limited due to the unique legal terminologies and the necessity for highly specialized knowledge. These limitations highlight the need for high-quality data tailored for co
Externí odkaz:
http://arxiv.org/abs/2406.13217
Recent studies have shown that maintaining a consistent response style by human experts and enhancing data quality in training sets can significantly improve the performance of fine-tuned Large Language Models (LLMs) while reducing the number of trai
Externí odkaz:
http://arxiv.org/abs/2406.10882
Autor:
Huang, Shuo, MacLean, William, Kang, Xiaoxi, Wu, Anqi, Qu, Lizhen, Xu, Qiongkai, Li, Zhuang, Yuan, Xingliang, Haffari, Gholamreza
Increasing concerns about privacy leakage issues in academia and industry arise when employing NLP models from third-party providers to process sensitive texts. To protect privacy before sending sensitive data to those models, we suggest sanitizing s
Externí odkaz:
http://arxiv.org/abs/2406.03749
Machine learning models have made incredible progress, but they still struggle when applied to examples from unseen domains. This study focuses on a specific problem of domain generalization, where a model is trained on one source domain and tested o
Externí odkaz:
http://arxiv.org/abs/2404.13504
Autor:
Wu, Weijia, Li, Zhuang, Gu, Yuchao, Zhao, Rui, He, Yefei, Zhang, David Junhao, Shou, Mike Zheng, Li, Yan, Gao, Tingting, Zhang, Di
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control methods, DragAnything offers several advantages. Firstly, trajectory-ba
Externí odkaz:
http://arxiv.org/abs/2403.07420
Autor:
Shao, Zhijing, Wang, Zhaolong, Li, Zhuang, Wang, Duotun, Lin, Xiangru, Zhang, Yu, Fan, Mingming, Wang, Zeyu
We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the motion and appear
Externí odkaz:
http://arxiv.org/abs/2403.05087
Autor:
Zeng, Zijie, Liu, Shiqi, Sha, Lele, Li, Zhuang, Yang, Kaixun, Liu, Sannyuya, Gašević, Dragan, Chen, Guanliang
This study explores the challenge of sentence-level AI-generated text detection within human-AI collaborative hybrid texts. Existing studies of AI-generated text detection for hybrid texts often rely on synthetic datasets. These typically involve hyb
Externí odkaz:
http://arxiv.org/abs/2403.03506
Autor:
Lozhkov, Anton, Li, Raymond, Allal, Loubna Ben, Cassano, Federico, Lamy-Poirier, Joel, Tazi, Nouamane, Tang, Ao, Pykhtar, Dmytro, Liu, Jiawei, Wei, Yuxiang, Liu, Tianyang, Tian, Max, Kocetkov, Denis, Zucker, Arthur, Belkada, Younes, Wang, Zijian, Liu, Qian, Abulkhanov, Dmitry, Paul, Indraneil, Li, Zhuang, Li, Wen-Ding, Risdal, Megan, Li, Jia, Zhu, Jian, Zhuo, Terry Yue, Zheltonozhskii, Evgenii, Dade, Nii Osae Osae, Yu, Wenhao, Krauß, Lucas, Jain, Naman, Su, Yixuan, He, Xuanli, Dey, Manan, Abati, Edoardo, Chai, Yekun, Muennighoff, Niklas, Tang, Xiangru, Oblokulov, Muhtasham, Akiki, Christopher, Marone, Marc, Mou, Chenghao, Mishra, Mayank, Gu, Alex, Hui, Binyuan, Dao, Tri, Zebaze, Armel, Dehaene, Olivier, Patry, Nicolas, Xu, Canwen, McAuley, Julian, Hu, Han, Scholak, Torsten, Paquet, Sebastien, Robinson, Jennifer, Anderson, Carolyn Jane, Chapados, Nicolas, Patwary, Mostofa, Tajbakhsh, Nima, Jernite, Yacine, Ferrandis, Carlos Muñoz, Zhang, Lingming, Hughes, Sean, Wolf, Thomas, Guha, Arjun, von Werra, Leandro, de Vries, Harm
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digita
Externí odkaz:
http://arxiv.org/abs/2402.19173