Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Henry, Peng"'
How can models effectively detect out-of-distribution (OOD) samples in complex, multi-label settings without extensive retraining? Existing OOD detection methods struggle to capture the intricate semantic relationships and label co-occurrences inhere
Externí odkaz:
http://arxiv.org/abs/2411.13578
Autor:
Liu, Han, Tang, Xianfeng, Chen, Tianlang, Liu, Jiapeng, Indu, Indu, Zou, Henry Peng, Dai, Peng, Galan, Roberto Fernandez, Porter, Michael D, Jia, Dongmei, Zhang, Ning, Xiong, Lian
The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and customer convenience. While recommendation systems have been widely stud
Externí odkaz:
http://arxiv.org/abs/2410.11327
As large language models achieve increasingly impressive results, questions arise about whether such performance is from generalizability or mere data memorization. Thus, numerous data contamination detection methods have been proposed. However, thes
Externí odkaz:
http://arxiv.org/abs/2409.09927
Autor:
Zhang, Weizhi, Yang, Liangwei, Song, Zihe, Zou, Henry Peng, Xu, Ke, Fang, Liancheng, Yu, Philip S.
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world applications. This paper presents a critical examination of the neces
Externí odkaz:
http://arxiv.org/abs/2407.18910
Autor:
Samuel, Vinay, Zou, Henry Peng, Zhou, Yue, Chaudhari, Shreyas, Kalyan, Ashwin, Rajpurohit, Tanmay, Deshpande, Ameet, Narasimhan, Karthik, Murahari, Vishvak
Persona agents, which are LLM agents that act according to an assigned persona, have demonstrated impressive contextual response capabilities across various applications. These persona agents offer significant enhancements across diverse sectors, suc
Externí odkaz:
http://arxiv.org/abs/2407.18416
We find that language models have difficulties generating fallacious and deceptive reasoning. When asked to generate deceptive outputs, language models tend to leak honest counterparts but believe them to be false. Exploiting this deficiency, we prop
Externí odkaz:
http://arxiv.org/abs/2407.00869
Autor:
Du, Jiangshu, Wang, Yibo, Zhao, Wenting, Deng, Zhongfen, Liu, Shuaiqi, Lou, Renze, Zou, Henry Peng, Venkit, Pranav Narayanan, Zhang, Nan, Srinath, Mukund, Zhang, Haoran Ranran, Gupta, Vipul, Li, Yinghui, Li, Tao, Wang, Fei, Liu, Qin, Liu, Tianlin, Gao, Pengzhi, Xia, Congying, Xing, Chen, Cheng, Jiayang, Wang, Zhaowei, Su, Ying, Shah, Raj Sanjay, Guo, Ruohao, Gu, Jing, Li, Haoran, Wei, Kangda, Wang, Zihao, Cheng, Lu, Ranathunga, Surangika, Fang, Meng, Fu, Jie, Liu, Fei, Huang, Ruihong, Blanco, Eduardo, Cao, Yixin, Zhang, Rui, Yu, Philip S., Yin, Wenpeng
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many rout
Externí odkaz:
http://arxiv.org/abs/2406.16253
Autor:
Deng, Chengyuan, Duan, Yiqun, Jin, Xin, Chang, Heng, Tian, Yijun, Liu, Han, Wang, Yichen, Gao, Kuofeng, Zou, Henry Peng, Jin, Yiqiao, Xiao, Yijia, Wu, Shenghao, Xie, Zongxing, Lyu, Weimin, He, Sihong, Cheng, Lu, Wang, Haohan, Zhuang, Jun
Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper pr
Externí odkaz:
http://arxiv.org/abs/2406.05392
Autor:
Zhang, Weizhi, Yang, Liangwei, Song, Zihe, Zou, Henry Peng, Xu, Ke, Zhu, Yuanjie, Yu, Philip S.
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentati
Externí odkaz:
http://arxiv.org/abs/2404.15954
Autor:
Zou, Henry Peng, Samuel, Vinay, Zhou, Yue, Zhang, Weizhi, Fang, Liancheng, Song, Zihe, Yu, Philip S., Caragea, Cornelia
Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publicly available, and lack an in-depth human inspection across diverse do
Externí odkaz:
http://arxiv.org/abs/2404.15592