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pro vyhledávání: '"Chen, Guanzheng"'
Preparing a lesson plan, e.g., a detailed road map with strategies and materials for instructing a 90-minute class, is beneficial yet challenging for novice teachers. Large language models (LLMs) can ease this process by generating adaptive content f
Externí odkaz:
http://arxiv.org/abs/2408.01102
Autor:
Cheng, Zesen, Leng, Sicong, Zhang, Hang, Xin, Yifei, Li, Xin, Chen, Guanzheng, Zhu, Yongxin, Zhang, Wenqi, Luo, Ziyang, Zhao, Deli, Bing, Lidong
In this paper, we present the VideoLLaMA 2, a set of Video Large Language Models (Video-LLMs) designed to enhance spatial-temporal modeling and audio understanding in video and audio-oriented tasks. Building upon its predecessor, VideoLLaMA 2 incorpo
Externí odkaz:
http://arxiv.org/abs/2406.07476
Autor:
Nguyen, Xuan-Phi, Zhang, Wenxuan, Li, Xin, Aljunied, Mahani, Hu, Zhiqiang, Shen, Chenhui, Chia, Yew Ken, Li, Xingxuan, Wang, Jianyu, Tan, Qingyu, Cheng, Liying, Chen, Guanzheng, Deng, Yue, Yang, Sen, Liu, Chaoqun, Zhang, Hang, Bing, Lidong
Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages. To address this i
Externí odkaz:
http://arxiv.org/abs/2312.00738
Transformer-based Large Language Models (LLMs) are pioneering advances in many natural language processing tasks, however, their exceptional capabilities are restricted within the preset context window of Transformer. Position Embedding (PE) scaling
Externí odkaz:
http://arxiv.org/abs/2310.16450
Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs). By tuning just a fraction amount of parameters comparing to full model finetuning, PETuning methods claim to have
Externí odkaz:
http://arxiv.org/abs/2202.07962
Publikováno v:
In Nano Energy May 2024 123
Akademický článek
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Autor:
Lv, Zihan, Cheng, Lin, Chen, Guanzheng, Luo, Xuanzi, Yu, Hang, Zhang, Hanxiao, Wu, Huaping, Liu, Aiping
Publikováno v:
IEEE Sensors Journal; August 2024, Vol. 24 Issue: 16 p26309-26319, 11p
Akademický článek
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Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs). By tuning just a fraction amount of parameters comparing to full model finetuning, PETuning methods claim to have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cace2fe55a1e175625c4b321c237cfed