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pro vyhledávání: '"Ma, Weitao"'
Autor:
Huang, Lei, Feng, Xiaocheng, Ma, Weitao, Zhao, Liang, Fan, Yuchun, Zhong, Weihong, Xu, Dongliang, Yang, Qing, Liu, Hongtao, Qin, Bing
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems. However, improving this capability requires high-quality attribution data,
Externí odkaz:
http://arxiv.org/abs/2410.13298
Autor:
Ye, Yangfan, Feng, Xiachong, Feng, Xiaocheng, Ma, Weitao, Qin, Libo, Xu, Dongliang, Yang, Qing, Liu, Hongtao, Qin, Bing
News summarization in today's global scene can be daunting with its flood of multilingual content and varied viewpoints from different sources. However, current studies often neglect such real-world scenarios as they tend to focus solely on either si
Externí odkaz:
http://arxiv.org/abs/2410.04087
Autor:
Huang, Lei, Feng, Xiaocheng, Ma, Weitao, Gu, Yuxuan, Zhong, Weihong, Feng, Xiachong, Yu, Weijiang, Peng, Weihua, Tang, Duyu, Tu, Dandan, Qin, Bing
Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with in-line citations, have shown potential in mitigating hallucinations
Externí odkaz:
http://arxiv.org/abs/2408.04568
Autor:
Zhong, Weihong, Feng, Xiaocheng, Zhao, Liang, Li, Qiming, Huang, Lei, Gu, Yuxuan, Ma, Weitao, Xu, Yuan, Qin, Bing
Though advanced in understanding visual information with human languages, Large Vision-Language Models (LVLMs) still suffer from multimodal hallucinations. A natural concern is that during multimodal interaction, the generated hallucinations could in
Externí odkaz:
http://arxiv.org/abs/2407.00569
Autor:
Ma, Weitao, Feng, Xiaocheng, Zhong, Weihong, Huang, Lei, Ye, Yangfan, Feng, Xiachong, Qin, Bing
Large language model unlearning has garnered increasing attention due to its potential to address security and privacy concerns, leading to extensive research in the field. However, much of this research has concentrated on instance-level unlearning,
Externí odkaz:
http://arxiv.org/abs/2406.15796
Autor:
Feng, Zhangyin, Ma, Weitao, Yu, Weijiang, Huang, Lei, Wang, Haotian, Chen, Qianglong, Peng, Weihua, Feng, Xiaocheng, Qin, Bing, liu, Ting
Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursue
Externí odkaz:
http://arxiv.org/abs/2311.05876
Autor:
Huang, Lei, Yu, Weijiang, Ma, Weitao, Zhong, Weihong, Feng, Zhangyin, Wang, Haotian, Chen, Qianglong, Peng, Weihua, Feng, Xiaocheng, Qin, Bing, Liu, Ting
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Nevertheless, LLMs are prone to hallucination, generating plausible yet non
Externí odkaz:
http://arxiv.org/abs/2311.05232
Autor:
Wang, Nannan, Li, Liangwei, Ma, Weitao, Li, Zaixing, Chen, Xiaofei, Sun, Qing, Chen, Ping, Liu, Bochao
Publikováno v:
In Journal of Water Process Engineering January 2024 57
Akademický článek
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Autor:
Ma, Weitao
The application of composites has been increasing dramatically in aerospace structures recently, for example, composites have contributed over 50 percent of the structure mass of large transport airplanes Boeing 787 and Airbus 350XWB. However, the fu
Externí odkaz:
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566010