Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining

Autor: Bai, Tianyi, Yang, Ling, Wong, Zhen Hao, Peng, Jiahui, Zhuang, Xinlin, Zhang, Chi, Wu, Lijun, Qiu, Jiantao, Zhang, Wentao, Yuan, Binhang, He, Conghui

Efficient data selection is crucial to accelerate the pretraining of large language models (LLMs). While various methods have been proposed to enhance data efficiency, limited research has addressed the inherent conflicts between these approaches to

Externí odkaz: http://arxiv.org/abs/2410.08102

InternLM2 Technical Report

Autor: Cai, Zheng, Cao, Maosong, Chen, Haojiong, Chen, Kai, Chen, Keyu, Chen, Xin, Chen, Xun, Chen, Zehui, Chen, Zhi, Chu, Pei, Dong, Xiaoyi, Duan, Haodong, Fan, Qi, Fei, Zhaoye, Gao, Yang, Ge, Jiaye, Gu, Chenya, Gu, Yuzhe, Gui, Tao, Guo, Aijia, Guo, Qipeng, He, Conghui, Hu, Yingfan, Huang, Ting, Jiang, Tao, Jiao, Penglong, Jin, Zhenjiang, Lei, Zhikai, Li, Jiaxing, Li, Jingwen, Li, Linyang, Li, Shuaibin, Li, Wei, Li, Yining, Liu, Hongwei, Liu, Jiangning, Hong, Jiawei, Liu, Kaiwen, Liu, Kuikun, Liu, Xiaoran, Lv, Chengqi, Lv, Haijun, Lv, Kai, Ma, Li, Ma, Runyuan, Ma, Zerun, Ning, Wenchang, Ouyang, Linke, Qiu, Jiantao, Qu, Yuan, Shang, Fukai, Shao, Yunfan, Song, Demin, Song, Zifan, Sui, Zhihao, Sun, Peng, Sun, Yu, Tang, Huanze, Wang, Bin, Wang, Guoteng, Wang, Jiaqi, Wang, Jiayu, Wang, Rui, Wang, Yudong, Wang, Ziyi, Wei, Xingjian, Weng, Qizhen, Wu, Fan, Xiong, Yingtong, Xu, Chao, Xu, Ruiliang, Yan, Hang, Yan, Yirong, Yang, Xiaogui, Ye, Haochen, Ying, Huaiyuan, Yu, Jia, Yu, Jing, Zang, Yuhang, Zhang, Chuyu, Zhang, Li, Zhang, Pan, Zhang, Peng, Zhang, Ruijie, Zhang, Shuo, Zhang, Songyang, Zhang, Wenjian, Zhang, Wenwei, Zhang, Xingcheng, Zhang, Xinyue, Zhao, Hui, Zhao, Qian, Zhao, Xiaomeng, Zhou, Fengzhe, Zhou, Zaida, Zhuo, Jingming, Zou, Yicheng, Qiu, Xipeng, Qiao, Yu, Lin, Dahua

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introdu

Externí odkaz: http://arxiv.org/abs/2403.17297

WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models

Autor: He, Conghui, Jin, Zhenjiang, Xu, Chao, Qiu, Jiantao, Wang, Bin, Li, Wei, Yan, Hang, Wang, Jiaqi, Lin, Dahua

The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs) and multimodal large language models (MLLMs). These cutting-edge mod

Externí odkaz: http://arxiv.org/abs/2308.10755

Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment

Autor: Song, Hongyu, Yu, Jincheng, Qiu, Jiantao, Sun, Zhixiao, Lang, Kuijun, Luo, Qing, Shen, Yuan, Wang, Yu

For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve ful

Externí odkaz: http://arxiv.org/abs/2201.10150

DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

Autor: Xing, Yu, Liang, Shuang, Sui, Lingzhi, Jia, Xijie, Qiu, Jiantao, Liu, Xin, Wang, Yushun, Wang, Yu, Shan, Yi

The convolutional neural network (CNN) has become a state-of-the-art method for several artificial intelligence domains in recent years. The increasingly complex CNN models are both computation-bound and I/O-bound. FPGA-based accelerators driven by c

Externí odkaz: http://arxiv.org/abs/1902.07463