Zobrazeno 1 - 10
of 172
pro vyhledávání: '"Chen, Meiqi"'
Autor:
Ma, Yubo, Zang, Yuhang, Chen, Liangyu, Chen, Meiqi, Jiao, Yizhu, Li, Xinze, Lu, Xinyuan, Liu, Ziyu, Ma, Yan, Dong, Xiaoyi, Zhang, Pan, Pan, Liangming, Jiang, Yu-Gang, Wang, Jiaqi, Cao, Yixin, Sun, Aixin
Understanding documents with rich layouts and multi-modal components is a long-standing and practical task. Recent Large Vision-Language Models (LVLMs) have made remarkable strides in various tasks, particularly in single-page document understanding
Externí odkaz:
http://arxiv.org/abs/2407.01523
Causal reasoning is fundamental to human intelligence and crucial for effective decision-making in real-world environments. Despite recent advancements in large vision-language models (LVLMs), their ability to comprehend causality remains unclear. Pr
Externí odkaz:
http://arxiv.org/abs/2406.19131
Autor:
Chen, Sirui, Peng, Bo, Chen, Meiqi, Wang, Ruiqi, Xu, Mengying, Zeng, Xingyu, Zhao, Rui, Zhao, Shengjie, Qiao, Yu, Lu, Chaochao
Causal reasoning is viewed as crucial for achieving human-level machine intelligence. Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal
Externí odkaz:
http://arxiv.org/abs/2405.00622
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Recent advancements in Large Language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from an over-reliance on unimodal biases (e.g., language bias and vision bias),
Externí odkaz:
http://arxiv.org/abs/2403.18346
Autor:
Lu, Chaochao, Qian, Chen, Zheng, Guodong, Fan, Hongxing, Gao, Hongzhi, Zhang, Jie, Shao, Jing, Deng, Jingyi, Fu, Jinlan, Huang, Kexin, Li, Kunchang, Li, Lijun, Wang, Limin, Sheng, Lu, Chen, Meiqi, Zhang, Ming, Ren, Qibing, Chen, Sirui, Gui, Tao, Ouyang, Wanli, Wang, Yali, Teng, Yan, Wang, Yaru, Wang, Yi, He, Yinan, Wang, Yingchun, Wang, Yixu, Zhang, Yongting, Qiao, Yu, Shen, Yujiong, Mou, Yurong, Chen, Yuxi, Zhang, Zaibin, Shi, Zhelun, Yin, Zhenfei, Wang, Zhipin
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the performance of recent MLLM-based applications and the ex
Externí odkaz:
http://arxiv.org/abs/2401.15071
Large language models (LLMs) have gained enormous attention from both academia and industry, due to their exceptional ability in language generation and extremely powerful generalization. However, current LLMs still output unreliable content in pract
Externí odkaz:
http://arxiv.org/abs/2310.09158
Publikováno v:
Ziyuan Kexue, Vol 46, Iss 5, Pp 853-866 (2024)
[Objective] The Yangtze River is an important ecological security barrier in China. Analyzing the spatial-temporal changes of ecosystem services and the driving force of land use change in the Yangtze River Basin is conducive to the coordinated devel
Externí odkaz:
https://doaj.org/article/0a4ecc0747104b6a8e16ccbe67abe05e
Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a novel Event
Externí odkaz:
http://arxiv.org/abs/2204.07434
In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tunin
Externí odkaz:
http://arxiv.org/abs/2202.12109
Autor:
Xu, Guocai, Chai, Shengjun, Zhang, Rong, Chen, Meiqi, Fan, Xiaoxia, Zhang, Yao, Cai, Chunmei, Ge, Ri-Li
Publikováno v:
In Biophysical Journal 18 June 2024 123(12):1722-1734