Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Geng, Xiubo"'
Autor:
Chen, Qi, Geng, Xiubo, Rosset, Corby, Buractaon, Carolyn, Lu, Jingwen, Shen, Tao, Zhou, Kun, Xiong, Chenyan, Gong, Yeyun, Bennett, Paul, Craswell, Nick, Xie, Xing, Yang, Fan, Tower, Bryan, Rao, Nikhil, Dong, Anlei, Jiang, Wenqi, Liu, Zheng, Li, Mingqin, Liu, Chuanjie, Li, Zengzhong, Majumder, Rangan, Neville, Jennifer, Oakley, Andy, Risvik, Knut Magne, Simhadri, Harsha Vardhan, Varma, Manik, Wang, Yujing, Yang, Linjun, Yang, Mao, Zhang, Ce
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked
Externí odkaz:
http://arxiv.org/abs/2405.07526
Autor:
Yang, Kaiwen, Shen, Tao, Tian, Xinmei, Geng, Xiubo, Tao, Chongyang, Tao, Dacheng, Zhou, Tianyi
Aligning the recent large language models (LLMs) with computer vision models leads to large vision-language models (LVLMs), which have paved the way for zero-shot image reasoning tasks. However, LVLMs are usually trained on short high-level captions
Externí odkaz:
http://arxiv.org/abs/2312.01598
Autor:
Zhou, Yucheng, Geng, Xiubo, Shen, Tao, Tao, Chongyang, Long, Guodong, Lou, Jian-Guang, Shen, Jianbing
Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation. Nevertheless, they encounter difficulties when confronted with chaotic con
Externí odkaz:
http://arxiv.org/abs/2311.08734
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Autor:
Luo, Haipeng, Sun, Qingfeng, Xu, Can, Zhao, Pu, Lou, Jianguang, Tao, Chongyang, Geng, Xiubo, Lin, Qingwei, Chen, Shifeng, Zhang, Dongmei
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, most existing open-source models are only pre-trained on large-scale int
Externí odkaz:
http://arxiv.org/abs/2308.09583
Autor:
Wang, Xindi, Wang, Yufei, Xu, Can, Geng, Xiubo, Zhang, Bowen, Tao, Chongyang, Rudzicz, Frank, Mercer, Robert E., Jiang, Daxin
Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained. However, despite the success of LLMs, there has been
Externí odkaz:
http://arxiv.org/abs/2307.15411
Autor:
Luo, Ziyang, Xu, Can, Zhao, Pu, Sun, Qingfeng, Geng, Xiubo, Hu, Wenxiang, Tao, Chongyang, Ma, Jing, Lin, Qingwei, Jiang, Daxin
Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper
Externí odkaz:
http://arxiv.org/abs/2306.08568
Autor:
Feng, Jiazhan, Tao, Chongyang, Geng, Xiubo, Shen, Tao, Xu, Can, Long, Guodong, Zhao, Dongyan, Jiang, Daxin
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLM
Externí odkaz:
http://arxiv.org/abs/2305.07402
Autor:
Luo, Ziyang, Xu, Can, Zhao, Pu, Geng, Xiubo, Tao, Chongyang, Ma, Jing, Lin, Qingwei, Jiang, Daxin
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities. However, their performance may be suboptimal for domain-specific tasks that require s
Externí odkaz:
http://arxiv.org/abs/2305.04757
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retriever (LameR), is built upon no other neural models but an LLM, while b
Externí odkaz:
http://arxiv.org/abs/2304.14233
Autor:
Xu, Can, Sun, Qingfeng, Zheng, Kai, Geng, Xiubo, Zhao, Pu, Feng, Jiazhan, Tao, Chongyang, Jiang, Daxin
Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-comp
Externí odkaz:
http://arxiv.org/abs/2304.12244