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pro vyhledávání: '"Zhang, Shaokun"'
Autor:
Song, Linxin, Liu, Jiale, Zhang, Jieyu, Zhang, Shaokun, Luo, Ao, Wang, Shijian, Wu, Qingyun, Wang, Chi
Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art. It is thus intriguing to answer a critic
Externí odkaz:
http://arxiv.org/abs/2405.19425
It is a notable trend to use Large Language Models (LLMs) to tackle complex tasks, e.g., tasks that require a sequence of actions and dynamic interaction with tools and external environments. In this paper, we propose StateFlow, a novel LLM-based tas
Externí odkaz:
http://arxiv.org/abs/2403.11322
Autor:
Zhang, Shaokun, Zhang, Jieyu, Liu, Jiale, Song, Linxin, Wang, Chi, Krishna, Ranjay, Wu, Qingyun
Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions. To facilitate the development of LLM agents, we present a nove
Externí odkaz:
http://arxiv.org/abs/2402.11359
Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale data, so that training only on the subset practically performs on par
Externí odkaz:
http://arxiv.org/abs/2311.08675
Autor:
Zhang, Shaokun, Xia, Xiaobo, Wang, Zhaoqing, Chen, Ling-Hao, Liu, Jiale, Wu, Qingyun, Liu, Tongliang
In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving strong performance. However, since the prompts need to be sampled from a la
Externí odkaz:
http://arxiv.org/abs/2310.10873
Autor:
Wu, Qingyun, Bansal, Gagan, Zhang, Jieyu, Wu, Yiran, Li, Beibin, Zhu, Erkang, Jiang, Li, Zhang, Xiaoyun, Zhang, Shaokun, Liu, Jiale, Awadallah, Ahmed Hassan, White, Ryen W, Burger, Doug, Wang, Chi
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ
Externí odkaz:
http://arxiv.org/abs/2308.08155
Autor:
Wu, Yiran, Jia, Feiran, Zhang, Shaokun, Li, Hangyu, Zhu, Erkang, Wang, Yue, Lee, Yin Tat, Peng, Richard, Wu, Qingyun, Wang, Chi
Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields. LLMs, with their gene
Externí odkaz:
http://arxiv.org/abs/2306.01337
In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in ma
Externí odkaz:
http://arxiv.org/abs/2305.18421
Autor:
Guo, Maojun, Huang, Xiaolong, Zhang, Junhan, Huang, Ying, Tang, Ying, Wen, Honghua, Xu, Yanan, Zhang, Shaokun, Wei, Xiao, Sun, Shuoshuo, Zhu, Qun
Publikováno v:
In Experimental Cell Research 15 July 2024 440(2)
Publikováno v:
In Progress in Organic Coatings July 2024 192