Zobrazeno 1 - 10
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pro vyhledávání: '"Cong, Xin"'
Autor:
Fan, Shengda, Cong, Xin, Fu, Yuepeng, Zhang, Zhong, Zhang, Shuyan, Liu, Yuanwei, Wu, Yesai, Lin, Yankai, Liu, Zhiyuan, Sun, Maosong
Recent advancements in large language models (LLMs) have driven a revolutionary paradigm shift in process automation from Robotic Process Automation to Agentic Process Automation by automating the workflow orchestration procedure based on LLMs. Howev
Externí odkaz:
http://arxiv.org/abs/2411.05451
Autor:
Tian, Runchu, Li, Yanghao, Fu, Yuepeng, Deng, Siyang, Luo, Qinyu, Qian, Cheng, Wang, Shuo, Cong, Xin, Zhang, Zhong, Wu, Yesai, Lin, Yankai, Wang, Huadong, Liu, Xiaojiang
Positional bias in large language models (LLMs) hinders their ability to effectively process long inputs. A prominent example is the "lost in the middle" phenomenon, where LLMs struggle to utilize relevant information situated in the middle of the in
Externí odkaz:
http://arxiv.org/abs/2410.14641
Autor:
Lu, Yaxi, Yang, Shenzhi, Qian, Cheng, Chen, Guirong, Luo, Qinyu, Wu, Yesai, Wang, Huadong, Cong, Xin, Zhang, Zhong, Lin, Yankai, Liu, Weiwen, Wang, Yasheng, Liu, Zhiyuan, Liu, Fangming, Sun, Maosong
Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scenarios requiring foresight and autonomous decision-making. In this paper
Externí odkaz:
http://arxiv.org/abs/2410.12361
Autor:
Chen, Guoxin, Zhang, Zhong, Cong, Xin, Guo, Fangda, Wu, Yesai, Lin, Yankai, Feng, Wenzheng, Wang, Yasheng
Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of external environments, these tools and APIs may become outdated over t
Externí odkaz:
http://arxiv.org/abs/2410.06617
Autor:
Luo, Qinyu, Ye, Yining, Liang, Shihao, Zhang, Zhong, Qin, Yujia, Lu, Yaxi, Wu, Yesai, Cong, Xin, Lin, Yankai, Zhang, Yingli, Che, Xiaoyin, Liu, Zhiyuan, Sun, Maosong
Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging. However, their utilization in the domain of code documentation generation remains underexplored. To this
Externí odkaz:
http://arxiv.org/abs/2402.16667
Autor:
Yang, Zhiyu, Zhou, Zihan, Wang, Shuo, Cong, Xin, Han, Xu, Yan, Yukun, Liu, Zhenghao, Tan, Zhixing, Liu, Pengyuan, Yu, Dong, Liu, Zhiyuan, Shi, Xiaodong, Sun, Maosong
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns. Despite its importance, the use of Large Language Models (LLMs) for scien
Externí odkaz:
http://arxiv.org/abs/2402.11453
Autor:
Qian, Cheng, He, Bingxiang, Zhuang, Zhong, Deng, Jia, Qin, Yujia, Cong, Xin, Zhang, Zhong, Zhou, Jie, Lin, Yankai, Liu, Zhiyuan, Sun, Maosong
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adept at devising strategies and performing tasks, these agents struggle w
Externí odkaz:
http://arxiv.org/abs/2402.09205
Autor:
Fang, Junjie, Tang, Likai, Bi, Hongzhe, Qin, Yujia, Sun, Si, Li, Zhenyu, Li, Haolun, Li, Yongjian, Cong, Xin, Lin, Yankai, Yan, Yukun, Shi, Xiaodong, Song, Sen, Liu, Zhiyuan, Sun, Maosong
Long-context processing is a critical ability that constrains the applicability of large language models (LLMs). Although there exist various methods devoted to enhancing the long-context processing ability of LLMs, they are developed in an isolated
Externí odkaz:
http://arxiv.org/abs/2402.03009
Autor:
Qian, Cheng, Liang, Shihao, Qin, Yujia, Ye, Yining, Cong, Xin, Lin, Yankai, Wu, Yesai, Liu, Zhiyuan, Sun, Maosong
This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategy for enhancing the adaptability and flexibility of AI agents through inter-task self-evolution. Unlike existing methods focused on intra-task learning, ICE promotes the tran
Externí odkaz:
http://arxiv.org/abs/2401.13996
Autor:
Tian, Runchu, Ye, Yining, Qin, Yujia, Cong, Xin, Lin, Yankai, Pan, Yinxu, Wu, Yesai, Hui, Haotian, Liu, Weichuan, Liu, Zhiyuan, Sun, Maosong
Large Language Models (LLMs) have demonstrated exceptional coding capability. However, as another critical component of programming proficiency, the debugging capability of LLMs remains relatively unexplored. Previous evaluations of LLMs' debugging a
Externí odkaz:
http://arxiv.org/abs/2401.04621