no code implementations • 7 Mar 2025 • Ling Team, Binwei Zeng, Chao Huang, Chao Zhang, Changxin Tian, Cong Chen, dingnan jin, Feng Yu, Feng Zhu, Feng Yuan, Fakang Wang, Gangshan Wang, Guangyao Zhai, HaiTao Zhang, Huizhong Li, Jun Zhou, Jia Liu, Junpeng Fang, Junjie Ou, Jun Hu, Ji Luo, Ji Zhang, Jian Liu, Jian Sha, Jianxue Qian, Jiewei Wu, Junping Zhao, Jianguo Li, Jubao Feng, Jingchao Di, Junming Xu, Jinghua Yao, Kuan Xu, Kewei Du, Longfei Li, Lei Liang, Lu Yu, Li Tang, Lin Ju, Peng Xu, Qing Cui, Song Liu, Shicheng Li, Shun Song, Song Yan, Tengwei Cai, Tianyi Chen, Ting Guo, Ting Huang, Tao Feng, Tao Wu, Wei Wu, Xiaolu Zhang, Xueming Yang, Xin Zhao, Xiaobo Hu, Xin Lin, Yao Zhao, Yilong Wang, Yongzhen Guo, Yuanyuan Wang, Yue Yang, Yang Cao, Yuhao Fu, Yi Xiong, Yanzhe Li, Zhe Li, Zhiqiang Zhang, Ziqi Liu, ZhaoXin Huan, Zujie Wen, Zhenhang Sun, Zhuoxuan Du, Zhengyu He
Ultimately, our experimental findings demonstrate that a 300B MoE LLM can be effectively trained on lower-performance devices while achieving comparable performance to models of a similar scale, including dense and MoE models.
no code implementations • 8 Feb 2025 • Shengbin Yue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuanjing Huang, Zhongyu Wei
Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios.
no code implementations • 12 Apr 2024 • Hongqiao Lian, Zeyuan Ma, Hongshu Guo, Ting Huang, Yue-Jiao Gong
In this paper, we propose RLEMMO, a Meta-Black-Box Optimization framework, which maintains a population of solutions and incorporates a reinforcement learning agent for flexibly adjusting individual-level searching strategies to match the up-to-date optimization status, hence boosting the search performance on MMOP.
3 code implementations • 26 Mar 2024 • Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, Xiaoyi Dong, Haodong Duan, Qi Fan, Zhaoye Fei, Yang Gao, Jiaye Ge, Chenya Gu, Yuzhe Gu, Tao Gui, Aijia Guo, Qipeng Guo, Conghui He, Yingfan Hu, Ting Huang, Tao Jiang, Penglong Jiao, Zhenjiang Jin, Zhikai Lei, Jiaxing Li, Jingwen Li, Linyang Li, Shuaibin Li, Wei Li, Yining Li, Hongwei Liu, Jiangning Liu, Jiawei Hong, Kaiwen Liu, Kuikun Liu, Xiaoran Liu, Chengqi Lv, Haijun Lv, Kai Lv, Li Ma, Runyuan Ma, Zerun Ma, Wenchang Ning, Linke Ouyang, Jiantao Qiu, Yuan Qu, FuKai Shang, Yunfan Shao, Demin Song, Zifan Song, Zhihao Sui, Peng Sun, Yu Sun, Huanze Tang, Bin Wang, Guoteng Wang, Jiaqi Wang, Jiayu Wang, Rui Wang, Yudong Wang, Ziyi Wang, Xingjian Wei, Qizhen Weng, Fan Wu, Yingtong Xiong, Chao Xu, Ruiliang Xu, Hang Yan, Yirong Yan, Xiaogui Yang, Haochen Ye, Huaiyuan Ying, JIA YU, Jing Yu, Yuhang Zang, Chuyu Zhang, Li Zhang, Pan Zhang, Peng Zhang, Ruijie Zhang, Shuo Zhang, Songyang Zhang, Wenjian Zhang, Wenwei Zhang, Xingcheng Zhang, Xinyue Zhang, Hui Zhao, Qian Zhao, Xiaomeng Zhao, Fengzhe Zhou, Zaida Zhou, Jingming Zhuo, Yicheng Zou, Xipeng Qiu, Yu Qiao, Dahua Lin
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
1 code implementation • 28 May 2019 • Ting Huang, Gehui Shen, Zhi-Hong Deng
Compared to previous models which can also skip words, our model achieves better trade-offs between performance and efficiency.
no code implementations • 18 Aug 2018 • Gehui Shen, Zhi-Hong Deng, Ting Huang, Xi Chen
Recursive Neural Network (RecNN), a type of models which compose words or phrases recursively over syntactic tree structures, has been proven to have superior ability to obtain sentence representation for a variety of NLP tasks.
no code implementations • 2 Jun 2018 • Xi Chen, Zhi-Hong Deng, Gehui Shen, Ting Huang
This paper proposes a novel framework for recurrent neural networks (RNNs) inspired by the human memory models in the field of cognitive neuroscience to enhance information processing and transmission between adjacent RNNs' units.