Zobrazeno 1 - 10
of 682
pro vyhledávání: '"Ma, Yubo"'
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
Autor:
Ma, Yubo, Gou, Zhibin, Hao, Junheng, Xu, Ruochen, Wang, Shuohang, Pan, Liangming, Yang, Yujiu, Cao, Yixin, Sun, Aixin, Awadalla, Hany, Chen, Weizhu
Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning. This setting
Externí odkaz:
http://arxiv.org/abs/2402.11451
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
Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations. Although language attribution can be a potential solution, there are no suitable benchmarks and evaluation metrics to attribute LLMs to stru
Externí odkaz:
http://arxiv.org/abs/2310.05634
Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress.This paper presents a thorough
Externí odkaz:
http://arxiv.org/abs/2305.01901
Large Language Models (LLMs) have made remarkable strides in various tasks. Whether LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains an open problem. In this work, we aim to provide a thorough answer to th
Externí odkaz:
http://arxiv.org/abs/2303.08559
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
Information Extraction (IE) seeks to derive structured information from unstructured texts, often facing challenges in low-resource scenarios due to data scarcity and unseen classes. This paper presents a review of neural approaches to low-resource I
Externí odkaz:
http://arxiv.org/abs/2202.08063
Autor:
Pang, Chuhong, Ma, Yubo, Shi, Wenyi, Zi, Mengli, Chen, Jinxia, Liang, Chen, Li, Xiao, Liu, Zhuo, Du, Yian
Publikováno v:
In Journal of Gastrointestinal Surgery May 2024 28(5):694-702
Autor:
Zi, Mengli1,2,3,4 (AUTHOR), Ma, Yubo2,3,4,5 (AUTHOR), Chen, Jinxia1,2,3,4 (AUTHOR), Pang, Chuhong1,2,3,4 (AUTHOR), Li, Xiao2,3,4,5 (AUTHOR), Yuan, Li2,3,4 (AUTHOR), Liu, Zhuo6 (AUTHOR) zhouliu1985@126.com, Yu, Pengfei2 (AUTHOR) ypfzmu@163.com
Publikováno v:
Cancer Medicine. Feb2024, Vol. 13 Issue 4, p1-19. 19p.