Zobrazeno 21 - 30
of 361
pro vyhledávání: '"Zhang, Ningyu"'
Autor:
Wang, Tiannan, Chen, Jiamin, Jia, Qingrui, Wang, Shuai, Fang, Ruoyu, Wang, Huilin, Gao, Zhaowei, Xie, Chunzhao, Xu, Chuou, Dai, Jihong, Liu, Yibin, Wu, Jialong, Ding, Shengwei, Li, Long, Huang, Zhiwei, Deng, Xinle, Yu, Teng, Ma, Gangan, Xiao, Han, Chen, Zixin, Xiang, Danjun, Wang, Yunxia, Zhu, Yuanyuan, Xiao, Yi, Wang, Jing, Wang, Yiru, Ding, Siran, Huang, Jiayang, Xu, Jiayi, Tayier, Yilihamu, Hu, Zhenyu, Gao, Yuan, Zheng, Chengfeng, Ye, Yueshu, Li, Yihang, Wan, Lei, Jiang, Xinyue, Wang, Yujie, Cheng, Siyu, Song, Zhule, Tang, Xiangru, Xu, Xiaohua, Zhang, Ningyu, Chen, Huajun, Jiang, Yuchen Eleanor, Zhou, Wangchunshu
This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fin
Externí odkaz:
http://arxiv.org/abs/2401.17268
Autor:
Qiao, Shuofei, Zhang, Ningyu, Fang, Runnan, Luo, Yujie, Zhou, Wangchunshu, Jiang, Yuchen Eleanor, Lv, Chengfei, Chen, Huajun
Language agents have achieved considerable performance on various complex question-answering tasks by planning with external tools. Despite the incessant exploration in this field, existing language agent systems still struggle with costly, non-repro
Externí odkaz:
http://arxiv.org/abs/2401.05268
Autor:
Zhang, Ningyu, Yao, Yunzhi, Tian, Bozhong, Wang, Peng, Deng, Shumin, Wang, Mengru, Xi, Zekun, Mao, Shengyu, Zhang, Jintian, Ni, Yuansheng, Cheng, Siyuan, Xu, Ziwen, Xu, Xin, Gu, Jia-Chen, Jiang, Yong, Xie, Pengjun, Huang, Fei, Liang, Lei, Zhang, Zhiqiang, Zhu, Xiaowei, Zhou, Jun, Chen, Huajun
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising fro
Externí odkaz:
http://arxiv.org/abs/2401.01286
Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have broadened the scope for improving multi-step reasoning capabilities. We generally divide multi-step reasoning into two phases: path generation to generate the reasoning path
Externí odkaz:
http://arxiv.org/abs/2311.09101
Autor:
Chen, Xiang, Song, Duanzheng, Gui, Honghao, Wang, Chenxi, Zhang, Ningyu, Jiang, Yong, Huang, Fei, Lv, Chengfei, Zhang, Dan, Chen, Huajun
Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in texts generated by LLMs, especially in complex inferential scenarios,
Externí odkaz:
http://arxiv.org/abs/2310.12086
Autor:
Cheng, Siyuan, Tian, Bozhong, Liu, Qingbin, Chen, Xi, Wang, Yongheng, Chen, Huajun, Zhang, Ningyu
In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a higher level of scrutiny and careful consideration in the editing proces
Externí odkaz:
http://arxiv.org/abs/2310.08475
Autor:
Mao, Shengyu, Wang, Xiaohan, Wang, Mengru, Jiang, Yong, Xie, Pengjun, Huang, Fei, Zhang, Ningyu
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality oft
Externí odkaz:
http://arxiv.org/abs/2310.02168
As the cost associated with fine-tuning Large Language Models (LLMs) continues to rise, recent research efforts have pivoted towards developing methodologies to edit implicit knowledge embedded within LLMs. Yet, there's still a dark cloud lingering o
Externí odkaz:
http://arxiv.org/abs/2310.02129
As Natural Language Processing (NLP) systems are increasingly employed in intricate social environments, a pressing query emerges: Can these NLP systems mirror human-esque collaborative intelligence, in a multi-agent society consisting of multiple la
Externí odkaz:
http://arxiv.org/abs/2310.02124
Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the parad
Externí odkaz:
http://arxiv.org/abs/2310.02031