Zobrazeno 1 - 10
of 147
pro vyhledávání: '"Li, Chaozhuo"'
Autor:
Pu, Rui, Li, Chaozhuo, Ha, Rui, Chen, Zejian, Zhang, Litian, Liu, Zheng, Qiu, Lirong, Zhang, Xi
Jailbreak attack can be used to access the vulnerabilities of Large Language Models (LLMs) by inducing LLMs to generate the harmful content. And the most common method of the attack is to construct semantically ambiguous prompts to confuse and mislea
Externí odkaz:
http://arxiv.org/abs/2410.16327
Autor:
Yan, Hao, Li, Chaozhuo, Yu, Zhigang, Yin, Jun, Liu, Ruochen, Zhang, Peiyan, Han, Weihao, Li, Mingzheng, Zeng, Zhengxin, Sun, Hao, Deng, Weiwei, Sun, Feng, Zhang, Qi, Wang, Senzhang
Multimodal attributed graphs (MAGs) are prevalent in various real-world scenarios and generally contain two kinds of knowledge: (a) Attribute knowledge is mainly supported by the attributes of different modalities contained in nodes (entities) themse
Externí odkaz:
http://arxiv.org/abs/2410.09132
The existing Retrieval-Augmented Generation (RAG) systems face significant challenges in terms of cost and effectiveness. On one hand, they need to encode the lengthy retrieved contexts before responding to the input tasks, which imposes substantial
Externí odkaz:
http://arxiv.org/abs/2409.15699
Autor:
Zhou, Yujia, Liu, Yan, Li, Xiaoxi, Jin, Jiajie, Qian, Hongjin, Liu, Zheng, Li, Chaozhuo, Dou, Zhicheng, Ho, Tsung-Yi, Yu, Philip S.
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). While much of the current research in this field focuses on performance optimization, particularly in terms of accuracy
Externí odkaz:
http://arxiv.org/abs/2409.10102
Autor:
Yin, Jun, Zeng, Zhengxin, Li, Mingzheng, Yan, Hao, Li, Chaozhuo, Han, Weihao, Zhang, Jianjin, Liu, Ruochen, Sun, Allen, Deng, Denvy, Sun, Feng, Zhang, Qi, Pan, Shirui, Wang, Senzhang
Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the next-generation recommender systems (RSs). However, the static index
Externí odkaz:
http://arxiv.org/abs/2409.09253
The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in these studies typically only model the pairwise relationship between
Externí odkaz:
http://arxiv.org/abs/2408.00525
Autor:
Fang, Xiaohan, Li, Chaozhuo, Zhao, Yi, Zang, Qian, Zhang, Litian, Peng, Jiquan, Zhang, Xi, Gong, Jibing
Knowledge Graph Alignment (KGA) aims to integrate knowledge from multiple sources to address the limitations of individual Knowledge Graphs (KGs) in terms of coverage and depth. However, current KGA models fall short in achieving a ``complete'' knowl
Externí odkaz:
http://arxiv.org/abs/2407.17745
Autor:
Zhang, Peiyan, Yan, Yuchen, Zhang, Xi, Kang, Liying, Li, Chaozhuo, Huang, Feiran, Wang, Senzhang, Kim, Sunghun
In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional models, typically reliant on a static training-test approach, struggl
Externí odkaz:
http://arxiv.org/abs/2406.08229
Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures. However, existing approaches are confined to rigid relational orthogon
Externí odkaz:
http://arxiv.org/abs/2405.08540
Autor:
Zhou, Ziyi, Zhang, Xiaoming, Zhang, Litian, Liu, Jiacheng, Wang, Senzhang, Liu, Zheng, Zhang, Xi, Li, Chaozhuo, Yu, Philip S.
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content. However, these benchmarks typically focus solely on news pertaining to a single semantic topic or o
Externí odkaz:
http://arxiv.org/abs/2404.01336