Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Jiao, Yizhu"'
Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying fraud groups in transaction networks. Traditional community detection m
Externí odkaz:
http://arxiv.org/abs/2408.07369
Autor:
Zhu, Kerui, Huang, Bo-Wei, Jin, Bowen, Jiao, Yizhu, Zhong, Ming, Chang, Kevin, Lin, Shou-De, Han, Jiawei
Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks. This study delves into the capabilities of instruction-following LLMs for engaging with real-world gra
Externí odkaz:
http://arxiv.org/abs/2408.05457
Episodic structures are inherently interpretable and adaptable to evolving large-scale key events. However, state-of-the-art automatic event detection methods overlook event episodes and, therefore, struggle with these crucial characteristics. This p
Externí odkaz:
http://arxiv.org/abs/2408.04873
Language models are known to encode a great amount of factual knowledge through pretraining. However, such knowledge might be insufficient to cater to user requests, requiring the model to integrate external knowledge sources and adhere to user-provi
Externí odkaz:
http://arxiv.org/abs/2407.13048
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