Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Jiao, Yizhu"'
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:
Zhong, Ming, Shen, Yelong, Wang, Shuohang, Lu, Yadong, Jiao, Yizhu, Ouyang, Siru, Yu, Donghan, Han, Jiawei, Chen, Weizhu
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images. Nonetheless, existing methods face challenges in effectively com
Externí odkaz:
http://arxiv.org/abs/2402.16843
Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well wit
Externí odkaz:
http://arxiv.org/abs/2310.16040
Autor:
Ouyang, Siru, Wang, Shuohang, Liu, Yang, Zhong, Ming, Jiao, Yizhu, Iter, Dan, Pryzant, Reid, Zhu, Chenguang, Ji, Heng, Han, Jiawei
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the existing focus of NLP research accurately captures the genuine requirements
Externí odkaz:
http://arxiv.org/abs/2310.12418
Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the qual
Externí odkaz:
http://arxiv.org/abs/2309.02025
Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recommending groups to users can help both users' online experiences and plat
Externí odkaz:
http://arxiv.org/abs/2308.05013
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importance of extracting structured reactions from scientific literature, data
Externí odkaz:
http://arxiv.org/abs/2307.01448
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution shift. There is still significant ambiguity about why graph-based models
Externí odkaz:
http://arxiv.org/abs/2306.03256
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' prefere
Externí odkaz:
http://arxiv.org/abs/2302.03555
The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies exp
Externí odkaz:
http://arxiv.org/abs/2211.01577