Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Awadalla, Hany"'
Autor:
Abdin, Marah, Jacobs, Sam Ade, Awan, Ammar Ahmad, Aneja, Jyoti, Awadallah, Ahmed, Awadalla, Hany, Bach, Nguyen, Bahree, Amit, Bakhtiari, Arash, Bao, Jianmin, Behl, Harkirat, Benhaim, Alon, Bilenko, Misha, Bjorck, Johan, Bubeck, Sébastien, Cai, Qin, Cai, Martin, Mendes, Caio César Teodoro, Chen, Weizhu, Chaudhary, Vishrav, Chen, Dong, Chen, Dongdong, Chen, Yen-Chun, Chen, Yi-Ling, Chopra, Parul, Dai, Xiyang, Del Giorno, Allie, de Rosa, Gustavo, Dixon, Matthew, Eldan, Ronen, Fragoso, Victor, Iter, Dan, Gao, Mei, Gao, Min, Gao, Jianfeng, Garg, Amit, Goswami, Abhishek, Gunasekar, Suriya, Haider, Emman, Hao, Junheng, Hewett, Russell J., Huynh, Jamie, Javaheripi, Mojan, Jin, Xin, Kauffmann, Piero, Karampatziakis, Nikos, Kim, Dongwoo, Khademi, Mahoud, Kurilenko, Lev, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Li, Yunsheng, Liang, Chen, Liden, Lars, Liu, Ce, Liu, Mengchen, Liu, Weishung, Lin, Eric, Lin, Zeqi, Luo, Chong, Madan, Piyush, Mazzola, Matt, Mitra, Arindam, Modi, Hardik, Nguyen, Anh, Norick, Brandon, Patra, Barun, Perez-Becker, Daniel, Portet, Thomas, Pryzant, Reid, Qin, Heyang, Radmilac, Marko, Rosset, Corby, Roy, Sambudha, Ruwase, Olatunji, Saarikivi, Olli, Saied, Amin, Salim, Adil, Santacroce, Michael, Shah, Shital, Shang, Ning, Sharma, Hiteshi, Shukla, Swadheen, Song, Xia, Tanaka, Masahiro, Tupini, Andrea, Wang, Xin, Wang, Lijuan, Wang, Chunyu, Wang, Yu, Ward, Rachel, Wang, Guanhua, Witte, Philipp, Wu, Haiping, Wyatt, Michael, Xiao, Bin, Xu, Can, Xu, Jiahang, Xu, Weijian, Yadav, Sonali, Yang, Fan, Yang, Jianwei, Yang, Ziyi, Yang, Yifan, Yu, Donghan, Yuan, Lu, Zhang, Chengruidong, 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:
Chaves, Juan Manuel Zambrano, Huang, Shih-Cheng, Xu, Yanbo, Xu, Hanwen, Usuyama, Naoto, Zhang, Sheng, Wang, Fei, Xie, Yujia, Khademi, Mahmoud, Yang, Ziyi, Awadalla, Hany, Gong, Julia, Hu, Houdong, Yang, Jianwei, Li, Chunyuan, Gao, Jianfeng, Gu, Yu, Wong, Cliff, Wei, Mu, Naumann, Tristan, Chen, Muhao, Lungren, Matthew P., Chaudhari, Akshay, Yeung-Levy, Serena, Langlotz, Curtis P., Wang, Sheng, Poon, Hoifung
The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising results on some biomedical benchmarks, there are still major challenges
Externí odkaz:
http://arxiv.org/abs/2403.08002
Autor:
Ma, Yubo, Gou, Zhibin, Hao, Junheng, Xu, Ruochen, Wang, Shuohang, Pan, Liangming, Yang, Yujiu, Cao, Yixin, Sun, Aixin, Awadalla, Hany, Chen, Weizhu
Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning. This setting
Externí odkaz:
http://arxiv.org/abs/2402.11451
Most of the recent work in leveraging Large Language Models (LLMs) such as GPT-3 for Machine Translation (MT) has focused on selecting the few-shot samples for prompting. In this work, we try to better understand the role of demonstration attributes
Externí odkaz:
http://arxiv.org/abs/2310.15987
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
Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-sh
Externí odkaz:
http://arxiv.org/abs/2305.16806
Autor:
Xie, Shufang, Zhang, Huishuai, Guo, Junliang, Tan, Xu, Bian, Jiang, Awadalla, Hany Hassan, Menezes, Arul, Qin, Tao, Yan, Rui
Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance. However, the optimal way to implement residual connections in Transformer, which are essential for effective training, is still deba
Externí odkaz:
http://arxiv.org/abs/2304.14802