Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Liu, Huanshuo"'
Autor:
Zhang, Hao, Zhang, Yuyang, Li, Xiaoguang, Shi, Wenxuan, Xu, Haonan, Liu, Huanshuo, Wang, Yasheng, Shang, Lifeng, Liu, Qun, Liu, Yong, Tang, Ruiming
Integrating external knowledge into large language models (LLMs) presents a promising solution to overcome the limitations imposed by their antiquated and static parametric memory. Prior studies, however, have tended to over-reliance on external know
Externí odkaz:
http://arxiv.org/abs/2405.19010
Autor:
Liu, Huanshuo, Zhang, Hao, Guo, Zhijiang, Wang, Jing, Dong, Kuicai, Li, Xiangyang, Lee, Yi Quan, Zhang, Cong, Liu, Yong
Retrieval-augmented generation (RAG) has emerged as a promising solution for mitigating hallucinations of large language models (LLMs) with retrieved external knowledge. Adaptive RAG enhances this approach by enabling dynamic retrieval during generat
Externí odkaz:
http://arxiv.org/abs/2405.18727
Autor:
Liu, Huanshuo, Chen, Bo, Zhu, Menghui, Lin, Jianghao, Qin, Jiarui, Yang, Yang, Zhang, Hao, Tang, Ruiming
Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level retrieval-based models, such as RIM, have demonstrated remarkable performance. However, they face challenges including inference inefficiency and high resou
Externí odkaz:
http://arxiv.org/abs/2404.18304
Publikováno v:
Communications in Statistics: Simulation & Computation; 2024, Vol. 53 Issue 5, p2389-2405, 17p
Publikováno v:
Journal of the Korean Statistical Society; Mar2023, Vol. 52 Issue 1, p130-153, 24p