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pro vyhledávání: '"Kim, Young Jin"'
Autor:
Liu, Liyuan, Kim, Young Jin, Wang, Shuohang, Liang, Chen, Shen, Yelong, Cheng, Hao, Liu, Xiaodong, Tanaka, Masahiro, Wu, Xiaoxia, Hu, Wenxiang, Chaudhary, Vishrav, Lin, Zeqi, Zhang, Chenruidong, Xue, Jilong, Awadalla, Hany, Gao, Jianfeng, Chen, Weizhu
Mixture-of-Experts (MoE) models scale more effectively than dense models due to sparse computation through expert routing, selectively activating only a small subset of expert modules. However, sparse computation challenges traditional training pract
Externí odkaz:
http://arxiv.org/abs/2409.12136
Autor:
Abdin, Marah, Aneja, Jyoti, Awadalla, Hany, Awadallah, Ahmed, Awan, Ammar Ahmad, Bach, Nguyen, Bahree, Amit, Bakhtiari, Arash, Bao, Jianmin, Behl, Harkirat, Benhaim, Alon, Bilenko, Misha, Bjorck, Johan, Bubeck, Sébastien, Cai, Martin, Cai, Qin, Chaudhary, Vishrav, Chen, Dong, Chen, Dongdong, Chen, Weizhu, Chen, Yen-Chun, Chen, Yi-Ling, Cheng, Hao, Chopra, Parul, Dai, Xiyang, Dixon, Matthew, Eldan, Ronen, Fragoso, Victor, Gao, Jianfeng, Gao, Mei, Gao, Min, Garg, Amit, Del Giorno, Allie, Goswami, Abhishek, Gunasekar, Suriya, Haider, Emman, Hao, Junheng, Hewett, Russell J., Hu, Wenxiang, Huynh, Jamie, Iter, Dan, Jacobs, Sam Ade, Javaheripi, Mojan, Jin, Xin, Karampatziakis, Nikos, Kauffmann, Piero, Khademi, Mahoud, Kim, Dongwoo, Kim, Young Jin, Kurilenko, Lev, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Li, Yunsheng, Liang, Chen, Liden, Lars, Lin, Xihui, Lin, Zeqi, Liu, Ce, Liu, Liyuan, Liu, Mengchen, Liu, Weishung, Liu, Xiaodong, Luo, Chong, Madan, Piyush, Mahmoudzadeh, Ali, Majercak, David, Mazzola, Matt, Mendes, Caio César Teodoro, Mitra, Arindam, Modi, Hardik, Nguyen, Anh, Norick, Brandon, Patra, Barun, Perez-Becker, Daniel, Portet, Thomas, Pryzant, Reid, Qin, Heyang, Radmilac, Marko, Ren, Liliang, de Rosa, Gustavo, Rosset, Corby, Roy, Sambudha, Ruwase, Olatunji, Saarikivi, Olli, Saied, Amin, Salim, Adil, Santacroce, Michael, Shah, Shital, Shang, Ning, Sharma, Hiteshi, Shen, Yelong, Shukla, Swadheen, Song, Xia, Tanaka, Masahiro, Tupini, Andrea, Vaddamanu, Praneetha, Wang, Chunyu, Wang, Guanhua, Wang, Lijuan, Wang, Shuohang, Wang, Xin, Wang, Yu, Ward, Rachel, Wen, Wen, Witte, Philipp, Wu, Haiping, Wu, Xiaoxia, Wyatt, Michael, Xiao, Bin, Xu, Can, Xu, Jiahang, Xu, Weijian, Xue, Jilong, Yadav, Sonali, Yang, Fan, Yang, Jianwei, Yang, Yifan, Yang, Ziyi, Yu, Donghan, Yuan, Lu, Zhang, Chenruidong, Zhang, Cyril, Zhang, Jianwen, Zhang, Li Lyna, Zhang, Yi, Zhang, Yue, Zhang, Yunan, Zhou, Xiren
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi
Externí odkaz:
http://arxiv.org/abs/2404.14219
Autor:
Zhong, Jian, Chen, Chen, Kim, Young-Jin, Huang, Yuxiong, Teng, Mengjie, Bian, Yiheng, Bie, Zhaohong
Distribution system (DS) communication failures following extreme events often degrade monitoring and control functions, thus preventing the acquisition of complete global DS component state information, on which existing post-disaster DS restoration
Externí odkaz:
http://arxiv.org/abs/2403.01256
Autor:
Xu, Haoran, Sharaf, Amr, Chen, Yunmo, Tan, Weiting, Shen, Lingfeng, Van Durme, Benjamin, Murray, Kenton, Kim, Young Jin
Moderate-sized large language models (LLMs) -- those with 7B or 13B parameters -- exhibit promising machine translation (MT) performance. However, even the top-performing 13B LLM-based translation models, like ALMA, does not match the performance of
Externí odkaz:
http://arxiv.org/abs/2401.08417
Pre-trained language models (PLMs) show impressive performance in various downstream NLP tasks. However, pre-training large language models demands substantial memory and training compute. Furthermore, due to the substantial resources required, many
Externí odkaz:
http://arxiv.org/abs/2311.08590
Large Mixture of Experts (MoE) models could achieve state-of-the-art quality on various language tasks, including machine translation task, thanks to the efficient model scaling capability with expert parallelism. However, it has brought a fundamenta
Externí odkaz:
http://arxiv.org/abs/2310.02410
Generative Large Language Models (LLMs) have achieved remarkable advancements in various NLP tasks. However, these advances have not been reflected in the translation task, especially those with moderate model sizes (i.e., 7B or 13B parameters), whic
Externí odkaz:
http://arxiv.org/abs/2309.11674
Autor:
Pham, Hai, Kim, Young Jin, Mukherjee, Subhabrata, Woodruff, David P., Poczos, Barnabas, Awadalla, Hany Hassan
Mixture-of-experts (MoE) architecture has been proven a powerful method for diverse tasks in training deep models in many applications. However, current MoE implementations are task agnostic, treating all tokens from different tasks in the same manne
Externí odkaz:
http://arxiv.org/abs/2308.15772
Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment due to their substantial memory requirements. Furthermore, the latest generative models suffer from hig
Externí odkaz:
http://arxiv.org/abs/2308.09723
Autor:
Kim, Hanjin1 (AUTHOR) gks359@koreatech.ac.kr, Kim, Young-Jin2 (AUTHOR) you359@sehan.ac.kr, Kim, Won-Tae1 (AUTHOR) wtkim@koreatech.ac.kr
Publikováno v:
Sensors (14248220). Aug2024, Vol. 24 Issue 16, p5281. 21p.