Zobrazeno 1 - 10
of 1 760
pro vyhledávání: '"Cong, Xin"'
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, Yan, Yukun, Shi, Xiaodong, Song, Sen, Lin, Yankai, Liu, Zhiyuan, Sun, Maosong
Long-context processing is a critical ability that constrains the applicability of large language models. Although there exist various methods devoted to enhancing the long-context processing ability of large language models (LLMs), they are develope
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
Autor:
Lyu, Bohan, Cong, Xin, Yu, Heyang, Yang, Pan, Qin, Yujia, Ye, Yining, Lu, Yaxi, Zhang, Zhong, Yan, Yukun, Lin, Yankai, Liu, Zhiyuan, Sun, Maosong
While Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated exceptional proficiency in natural language processing, their efficacy in addressing complex, multifaceted tasks remains limited. A growing area of research focuses on LLM-ba
Externí odkaz:
http://arxiv.org/abs/2312.17294
Autor:
Qian, Chen, Dang, Yufan, Li, Jiahao, Liu, Wei, Xie, Zihao, Wang, Yifei, Chen, Weize, Yang, Cheng, Cong, Xin, Che, Xiaoyin, Liu, Zhiyuan, Sun, Maosong
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scenario is in software development, where LLM agents demonstrate efficient col
Externí odkaz:
http://arxiv.org/abs/2312.17025
Autor:
Ye, Yining, Cong, Xin, Tian, Shizuo, Cao, Jiannan, Wang, Hao, Qin, Yujia, Lu, Yaxi, Yu, Heyang, Wang, Huadong, Lin, Yankai, Liu, Zhiyuan, Sun, Maosong
From ancient water wheels to robotic process automation (RPA), automation technology has evolved throughout history to liberate human beings from arduous tasks. Yet, RPA struggles with tasks needing human-like intelligence, especially in elaborate de
Externí odkaz:
http://arxiv.org/abs/2311.10751
Autor:
Ye, Yining, Cong, Xin, Tian, Shizuo, Qin, Yujia, Liu, Chong, Lin, Yankai, Liu, Zhiyuan, Sun, Maosong
Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step decision-making tasks beyond traditional NLP applications. Existing ap
Externí odkaz:
http://arxiv.org/abs/2308.12519