Zobrazeno 1 - 10
of 7 407
pro vyhledávání: '"zheng, Bo"'
Autor:
Li, Shilong, He, Yancheng, Guo, Hangyu, Bu, Xingyuan, Bai, Ge, Liu, Jie, Liu, Jiaheng, Qu, Xingwei, Li, Yangguang, Ouyang, Wanli, Su, Wenbo, Zheng, Bo
Long-context capabilities are essential for large language models (LLMs) to tackle complex and long-input tasks. Despite numerous efforts made to optimize LLMs for long contexts, challenges persist in robustly processing long inputs. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2406.14550
Large language models have seen widespread adoption in math problem-solving. However, in geometry problems that usually require visual aids for better understanding, even the most advanced multi-modal models currently still face challenges in effecti
Externí odkaz:
http://arxiv.org/abs/2406.11503
Autor:
Que, Haoran, Liu, Jiaheng, Zhang, Ge, Zhang, Chenchen, Qu, Xingwei, Ma, Yinghao, Duan, Feiyu, Bai, Zhiqi, Wang, Jiakai, Zhang, Yuanxing, Tan, Xu, Fu, Jie, Su, Wenbo, Wang, Jiamang, Qu, Lin, Zheng, Bo
Continual Pre-Training (CPT) on Large Language Models (LLMs) has been widely used to expand the model's fundamental understanding of specific downstream domains (e.g., math and code). For the CPT on domain-specific LLMs, one important question is how
Externí odkaz:
http://arxiv.org/abs/2406.01375
Autor:
Deng, Ken, Liu, Jiaheng, Zhu, He, Liu, Congnan, Li, Jingxin, Wang, Jiakai, Zhao, Peng, Zhang, Chenchen, Wu, Yanan, Yin, Xueqiao, Zhang, Yuanxing, Su, Wenbo, Xiang, Bangyu, Ge, Tiezheng, Zheng, Bo
Code completion models have made significant progress in recent years. Recently, repository-level code completion has drawn more attention in modern software development, and several baseline methods and benchmarks have been proposed. However, existi
Externí odkaz:
http://arxiv.org/abs/2406.01359
Autor:
Han, Dongchen, Wang, Ziyi, Xia, Zhuofan, Han, Yizeng, Pu, Yifan, Ge, Chunjiang, Song, Jun, Song, Shiji, Zheng, Bo, Huang, Gao
Mamba is an effective state space model with linear computation complexity. It has recently shown impressive efficiency in dealing with high-resolution inputs across various vision tasks. In this paper, we reveal that the powerful Mamba model shares
Externí odkaz:
http://arxiv.org/abs/2405.16605
Autor:
Guo, Jiayan, Huo, Yusen, Zhang, Zhilin, Wang, Tianyu, Yu, Chuan, Xu, Jian, Zhang, Yan, Zheng, Bo
Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bidding. However, most current RL auto-bidding methods are modeled throug
Externí odkaz:
http://arxiv.org/abs/2405.16141
Autor:
Ge, Chunjiang, Cheng, Sijie, Wang, Ziming, Yuan, Jiale, Gao, Yuan, Song, Jun, Song, Shiji, Huang, Gao, Zheng, Bo
High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadratic complexity while still generating excessive visual tokens. However,
Externí odkaz:
http://arxiv.org/abs/2405.15738
Video storytelling is engaging multimedia content that utilizes video and its accompanying narration to attract the audience, where a key challenge is creating narrations for recorded visual scenes. Previous studies on dense video captioning and vide
Externí odkaz:
http://arxiv.org/abs/2405.14040
Autor:
Liu, Zhendong, Nie, Yuanbi, Tan, Yingshui, Yue, Xiangyu, Cui, Qiushi, Wang, Chongjun, Zhu, Xiaoyong, Zheng, Bo
Benefiting from the powerful capabilities of Large Language Models (LLMs), pre-trained visual encoder models connected to an LLMs can realize Vision Language Models (VLMs). However, existing research shows that the visual modality of VLMs is vulnerab
Externí odkaz:
http://arxiv.org/abs/2405.13581
Recently, integrating visual controls into text-to-image~(T2I) models, such as ControlNet method, has received significant attention for finer control capabilities. While various training-free methods make efforts to enhance prompt following in T2I m
Externí odkaz:
http://arxiv.org/abs/2404.14768