Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Zhu, Kunlun"'
Autor:
Zhu, Kunlun, Luo, Yifan, Xu, Dingling, Wang, Ruobing, Yu, Shi, Wang, Shuo, Yan, Yukun, Liu, Zhenghao, Han, Xu, Liu, Zhiyuan, Sun, Maosong
Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge. However, evaluating the effectiveness of RAG systems in specialized scenarios remains challenging due to the high
Externí odkaz:
http://arxiv.org/abs/2408.01262
Autor:
Feng, Tao, Jin, Chuanyang, Liu, Jingyu, Zhu, Kunlun, Tu, Haoqin, Cheng, Zirui, Lin, Guanyu, You, Jiaxuan
The evolution of artificial intelligence (AI) has profoundly impacted human society, driving significant advancements in multiple sectors. Yet, the escalating demands on AI have highlighted the limitations of AI's current offerings, catalyzing a move
Externí odkaz:
http://arxiv.org/abs/2405.10313
Autor:
Tang, Xiangru, Jin, Qiao, Zhu, Kunlun, Yuan, Tongxin, Zhang, Yichi, Zhou, Wangchunshu, Qu, Meng, Zhao, Yilun, Tang, Jian, Zhang, Zhuosheng, Cohan, Arman, Lu, Zhiyong, Gerstein, Mark
Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these
Externí odkaz:
http://arxiv.org/abs/2402.04247
QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation
Recent years have witnessed the success of question answering (QA), especially its potential to be a foundation paradigm for tackling diverse NLP tasks. However, obtaining sufficient data to build an effective and stable QA system still remains an op
Externí odkaz:
http://arxiv.org/abs/2309.10326
Autor:
Qin, Yujia, Liang, Shihao, Ye, Yining, Zhu, Kunlun, Yan, Lan, Lu, Yaxi, Lin, Yankai, Cong, Xin, Tang, Xiangru, Qian, Bill, Zhao, Sihan, Hong, Lauren, Tian, Runchu, Xie, Ruobing, Zhou, Jie, Gerstein, Mark, Li, Dahai, Liu, Zhiyuan, Sun, Maosong
Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tun
Externí odkaz:
http://arxiv.org/abs/2307.16789
Autor:
Liang, Shihao, Tian, Runchu, Zhu, Kunlun, Qin, Yujia, Wang, Huadong, Cong, Xin, Liu, Zhiyuan, Liu, Xiaojiang, Sun, Maosong
Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of instructions in the training data can consistently enhance generalizati
Externí odkaz:
http://arxiv.org/abs/2307.15504
Autor:
Qin, Yujia, Cai, Zihan, Jin, Dian, Yan, Lan, Liang, Shihao, Zhu, Kunlun, Lin, Yankai, Han, Xu, Ding, Ning, Wang, Huadong, Xie, Ruobing, Qi, Fanchao, Liu, Zhiyuan, Sun, Maosong, Zhou, Jie
Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting fa
Externí odkaz:
http://arxiv.org/abs/2305.06849
Autor:
Qin, Yujia, Hu, Shengding, Lin, Yankai, Chen, Weize, Ding, Ning, Cui, Ganqu, Zeng, Zheni, Huang, Yufei, Xiao, Chaojun, Han, Chi, Fung, Yi Ren, Su, Yusheng, Wang, Huadong, Qian, Cheng, Tian, Runchu, Zhu, Kunlun, Liang, Shihao, Shen, Xingyu, Xu, Bokai, Zhang, Zhen, Ye, Yining, Li, Bowen, Tang, Ziwei, Yi, Jing, Zhu, Yuzhang, Dai, Zhenning, Yan, Lan, Cong, Xin, Lu, Yaxi, Zhao, Weilin, Huang, Yuxiang, Yan, Junxi, Han, Xu, Sun, Xian, Li, Dahai, Phang, Jason, Yang, Cheng, Wu, Tongshuang, Ji, Heng, Liu, Zhiyuan, Sun, Maosong
Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as huma
Externí odkaz:
http://arxiv.org/abs/2304.08354
Publikováno v:
In Advanced Powder Technology April 2018 29(4):863-873
Akademický článek
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