Zobrazeno 1 - 10
of 7 143
pro vyhledávání: '"An, FuYuan"'
Autor:
Zhang, Kexin, Lyu, Fuyuan, Tang, Xing, Liu, Dugang, Ma, Chen, Ding, Kaize, He, Xiuqiang, Liu, Xue
The evolution of previous Click-Through Rate (CTR) models has mainly been driven by proposing complex components, whether shallow or deep, that are adept at modeling feature interactions. However, there has been less focus on improving fusion design.
Externí odkaz:
http://arxiv.org/abs/2411.15731
Document-level Event Argument Extraction (EAE) faces two challenges due to increased input length: 1) difficulty in distinguishing semantic boundaries between events, and 2) interference from redundant information. To address these issues, we propose
Externí odkaz:
http://arxiv.org/abs/2411.05895
Recently, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. However, existing methods still face several limitations. First, most KGs suffe
Externí odkaz:
http://arxiv.org/abs/2410.12229
Subwavelength grating micro-polarizer arrays, as a type of focal plane division simultaneous detection method, are significantly advancing the development and practical application of polarization imaging technology. Based on the cross-scale, dual-pe
Externí odkaz:
http://arxiv.org/abs/2410.12573
Autor:
Zhang, Qiyuan, Wang, Yufei, YU, Tiezheng, Jiang, Yuxin, Wu, Chuhan, Li, Liangyou, Wang, Yasheng, Jiang, Xin, Shang, Lifeng, Tang, Ruiming, Lyu, Fuyuan, Ma, Chen
With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing the text generation quality in a wide range of tasks. However, there still remains a reliability gap between LLM-as-a
Externí odkaz:
http://arxiv.org/abs/2410.05193
Autor:
Du, Kaile, Zhou, Yifan, Lyu, Fan, Li, Yuyang, Xie, Junzhou, Shen, Yixi, Hu, Fuyuan, Liu, Guangcan
Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve subo
Externí odkaz:
http://arxiv.org/abs/2408.12161
Large Language Models (LLMs) are widely used in many different domains, but because of their limited interpretability, there are questions about how trustworthy they are in various perspectives, e.g., truthfulness and toxicity. Recent research has st
Externí odkaz:
http://arxiv.org/abs/2408.10474
Customer Lifetime Value (CLTV) prediction is a critical task in business applications. Accurately predicting CLTV is challenging in real-world business scenarios, as the distribution of CLTV is complex and mutable. Firstly, there is a large number of
Externí odkaz:
http://arxiv.org/abs/2408.08585
Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research. The pioneering scaling law on downstream works demonstrated intrinsic
Externí odkaz:
http://arxiv.org/abs/2407.01300
Fake news detection has received increasing attention from researchers in recent years, especially multi-modal fake news detection containing both text and images. However, many previous works have fed two modal features, text and image, into a binar
Externí odkaz:
http://arxiv.org/abs/2406.19776