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pro vyhledávání: '"Niu, Simin"'
Autor:
Liang, Xun, Wang, Hanyu, Wang, Yezhaohui, Song, Shichao, Yang, Jiawei, Niu, Simin, Hu, Jie, Liu, Dan, Yao, Shunyu, Xiong, Feiyu, Li, Zhiyu
In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly complex requirements. Beyond avoiding misleading or inappropriate cont
Externí odkaz:
http://arxiv.org/abs/2408.12599
Autor:
Chen, Yanfang, Chen, Ding, Song, Shichao, Niu, Simin, Wang, Hanyu, Tang, Zeyun, Xiong, Feiyu, Li, Zhiyu
As people increasingly prioritize their health, the speed and breadth of health information dissemination on the internet have also grown. At the same time, the presence of false health information (health rumors) intermingled with genuine content po
Externí odkaz:
http://arxiv.org/abs/2407.00668
Autor:
Liang, Xun, Niu, Simin, li, Zhiyu, Zhang, Sensen, Song, Shichao, Wang, Hanyu, Yang, Jiawei, Xiong, Feiyu, Tang, Bo, Xi, Chenyang
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs). Nevertheless, constructing and validating high-quality knowledge repositories require considerable effort. We pr
Externí odkaz:
http://arxiv.org/abs/2405.16933
Autor:
Yu, Xiaomin, Wang, Yezhaohui, Chen, Yanfang, Tao, Zhen, Xi, Dinghao, Song, Shichao, Niu, Simin, Li, Zhiyu
In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become dee
Externí odkaz:
http://arxiv.org/abs/2405.00711
Autor:
Lyu, Yuanjie, Li, Zhiyu, Niu, Simin, Xiong, Feiyu, Tang, Bo, Wang, Wenjin, Wu, Hao, Liu, Huanyong, Xu, Tong, Chen, Enhong
Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresses common LLM limitations, including outdated information and the tende
Externí odkaz:
http://arxiv.org/abs/2401.17043
Autor:
Liang, Xun, Song, Shichao, Niu, Simin, Li, Zhiyu, Xiong, Feiyu, Tang, Bo, Wang, Yezhaohui, He, Dawei, Cheng, Peng, Wang, Zhonghao, Deng, Haiying
Large language models (LLMs) have emerged as pivotal contributors in contemporary natural language processing and are increasingly being applied across a diverse range of industries. However, these large-scale probabilistic statistical models cannot
Externí odkaz:
http://arxiv.org/abs/2311.15296
Akademický článek
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Akademický článek
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Autor:
Niu, Simin, Jiang, Shu-Qin
Publikováno v:
Journal of Bacteriology. Aug95, Vol. 177 Issue 15, p4297. 6p. 3 Diagrams, 5 Charts.