Zobrazeno 1 - 10
of 4 861
pro vyhledávání: '"Wang, WenJie"'
Autor:
Ren, Ruiyang, Wang, Yuhao, Zhou, Kun, Zhao, Wayne Xin, Wang, Wenjie, Liu, Jing, Wen, Ji-Rong, Chua, Tat-Seng
Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passages are reranked in a listwise manner and a textual reranked permutati
Externí odkaz:
http://arxiv.org/abs/2411.04602
Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs, even with acceleration methods. This work explores adapting to dynam
Externí odkaz:
http://arxiv.org/abs/2410.23136
Autor:
Zhang, Yang, You, Juntao, Bai, Yimeng, Zhang, Jizhi, Bao, Keqin, Wang, Wenjie, Chua, Tat-Seng
Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, current LLM-based approaches struggle to fully leverage user behavior sequen
Externí odkaz:
http://arxiv.org/abs/2410.22809
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Autor:
Zhu, Fengbin, Liu, Ziyang, Ng, Xiang Yao, Wu, Haohui, Wang, Wenjie, Feng, Fuli, Wang, Chao, Luan, Huanbo, Chua, Tat Seng
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated. Existing benchmarks either contain limited fine-grai
Externí odkaz:
http://arxiv.org/abs/2410.21311
Autor:
Cai, Hongru, Li, Yongqi, Wang, Wenjie, Zhu, Fengbin, Shen, Xiaoyu, Li, Wenjie, Chua, Tat-Seng
Web agents have emerged as a promising direction to automate Web task completion based on user instructions, significantly enhancing user experience. Recently, Web agents have evolved from traditional agents to Large Language Models (LLMs)-based Web
Externí odkaz:
http://arxiv.org/abs/2410.17236
Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing content and are constrained by its limited diversity, making it difficu
Externí odkaz:
http://arxiv.org/abs/2410.14170
Autor:
Liao, Xinting, Liu, Weiming, Zhou, Pengyang, Yu, Fengyuan, Xu, Jiahe, Wang, Jun, Wang, Wenjie, Chen, Chaochao, Zheng, Xiaolin
Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge. However, deploying FL models in real-world scenarios remains unreliable due to the coexistence of in-distribution data
Externí odkaz:
http://arxiv.org/abs/2410.11397
Hypothesis: Sphingomyelin (SPM), a crucial phospholipid in the myelin sheath, is vital in insulating nerve fibers. We hypothesize that iron ions selectively bind to the phosphatidylcholine (PC) template within the SPM membrane under near-physiologica
Externí odkaz:
http://arxiv.org/abs/2410.08054
Autor:
Nayak, Binay P., Batey, James Ethan, Kim, Hyeong Jin, Wang, Wenjie, Bu, Wei, Zhang, Honghu, Mallapragada, Surya K., Vaknin, David
Employing grazing-incidence small-angle X-ray scattering (GISAXS) and X-ray reflectivity (XRR), we demonstrate that films composed of polyethylene glycol (PEG)-grafted silver nanoparticles (AgNP) and gold nanoparticles (AuNP), as well as their binary
Externí odkaz:
http://arxiv.org/abs/2410.05456
Autor:
Lin, Xinyu, Yang, Chaoqun, Wang, Wenjie, Li, Yongqi, Du, Cunxiao, Feng, Fuli, Ng, See-Kiong, Chua, Tat-Seng
Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration,
Externí odkaz:
http://arxiv.org/abs/2410.05165